Download ALFALFA H-alpha: The Star-Formation-Rate Density

Wesleyan University
The Honors College
ALFALFA Hα:
The Star-Formation-Rate Density
of the Local Universe
by
Arthur Sugden
Class of 2008
A thesis submitted to the
faculty of Wesleyan University
in partial fulfillment of the requirements for the
Degree of Bachelor of Arts
with Departmental Honors in Astronomy
Middletown, Connecticut
April, 2008
Acknowledgments
The Wesleyan Astronomy Department includes the most exciting and supportive professors I have met. Their interest in the field was infectious and drew me in.
One class in particular – John Salzer’s Introduction to Astronomical Techniques –
converted me to an astronomy major. For this and two years’ focus and advising,
I would like to thank John first and foremost. He has taught me IRAF, taken me
to Kitt Peak, and spent countless hours explaining the background of ADBS and
ALFALFA and correcting my thesis to reflect that background.
Two other faculty, Ed Moran and Kathryn Johnston, broadened my horizons
and demonstrated how the fields of active galactic nuclei/LINERs and Galactic
simulations, respectively, can be some of the most exciting parts of the Universe.
The teaching of the department’s faculty as a whole has given me the background
I hope to use for the rest of my life. Eric Williams introduced me to programming
– now a major part of my life – and Roy Kilgard has taught me about computers,
the Chandra X-Ray Observatory, and troubleshooting.
The department includes not only great professors, but great students. All
have taught me about astronomy and the joy of scientific peers. Four, in particular, have made the last two years exciting: Rachel Fueschl, Matt Johnson, Jessica
Kellar, and Jenny Konon.
All of this would have been impossible without my family. They introduced
me to science, pushed me to explore, and taught me that the expression of science
is its most important facet. I cannot thank you enough.
ii
Contents
1 Introduction
2
1.1
The Star Formation Rate Density . . . . . . . . . . . . . . . . . .
2
1.2
Previous Work
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
1.3
Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
2 Tracing Hydrogen
10
2.1
Physics of Hα . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
2.2
Physics of HI . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13
2.3
Observing HI: ADBS and ALFALFA . . . . . . . . . . . . . . . .
14
2.4
Observing Hα: 0.9 meter Telescope . . . . . . . . . . . . . . . . .
19
3 Telescopic Observations
22
3.1
HI-selected Galaxy Samples . . . . . . . . . . . . . . . . . . . . .
22
3.2
Observational Methods . . . . . . . . . . . . . . . . . . . . . . . .
23
3.3
Problems: Fall Data . . . . . . . . . . . . . . . . . . . . . . . . .
24
4 Data Reduction and Measurement
27
4.1
CCDs as Astronomical Detectors . . . . . . . . . . . . . . . . . .
27
4.2
Data Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
28
4.2.1
28
Preliminary Reductions . . . . . . . . . . . . . . . . . . . .
iii
4.2.2
Hα-Image Reductions . . . . . . . . . . . . . . . . . . . . .
30
4.3
Photometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
34
4.4
New ALFALFA Hα Software . . . . . . . . . . . . . . . . . . . . .
36
4.4.1
Data Reduction . . . . . . . . . . . . . . . . . . . . . . . .
36
4.4.2
Photometry . . . . . . . . . . . . . . . . . . . . . . . . . .
38
5 From Magnitude to SFRD
41
5.1
Calibrating the Magnitude . . . . . . . . . . . . . . . . . . . . . .
41
5.2
Converting Magnitude to Flux . . . . . . . . . . . . . . . . . . . .
43
5.3
Flux Corrections . . . . . . . . . . . . . . . . . . . . . . . . . . .
51
5.3.1
Galactic Absorption . . . . . . . . . . . . . . . . . . . . .
51
5.3.2
Galaxian Absorption . . . . . . . . . . . . . . . . . . . . .
54
5.3.3
[NII] Correction . . . . . . . . . . . . . . . . . . . . . . . .
57
5.4
Accounting for Distance . . . . . . . . . . . . . . . . . . . . . . .
60
5.5
The Star-Formation Rate . . . . . . . . . . . . . . . . . . . . . . .
60
5.6
The Star-Formation-Rate Density . . . . . . . . . . . . . . . . . .
66
6 Conclusion
70
6.1
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
70
6.2
Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
71
A equalize.cl
74
B pipeline.cl
81
C haphot.cl
86
D sdssphot.cl
95
1
E Abbreviations
100
Chapter 1
Introduction
Over one hundred billion stars lie in the Milky Way galaxy, a moderate-sized
galaxy in a universe of roughly one hundred billion galaxies. These galaxies thus
amount to 1022 (ten sextillion) stars in our Universe. The science of astronomy
allows us to contemplate such grand numbers and to investigate the Universe on
the largest scale.
Not all of the stars in the Universe formed at once; new stars are being formed
all of the time. Furthermore, the number of stars formed each year is continually changing. This thesis provides a foundation to examine the evolution of the
Universe by considering how the rate of star formation changes over time.
A key to understanding the change of star formation over time is an accurate
assessment of the rate of star formation today. This assessment provides an anchor
to which the history of star formation can be linked. With two recent surveys, a
great refinement of the current rate of star formation is becoming possible.
1.1 The Star Formation Rate Density
The determination of the rate of star formation has been a major focus for the past
15 to 20 years (Kennicutt, 1998 and references therein). An acceptable estimate of
this rate is imperative for the description and understanding of galaxy formation,
making its determination an active area of research.
2
1. Introduction
3
For astronomers studying past star-formation rates (SFRs), distances play the
role of a time machine. Light travels at a finite speed; it travels slowly enough
that astronomers can still view light from the Big Bang. Light emitted by the
Sun takes more than eight minutes to reach Earth, roughly five hours to reach
Pluto, and more than four years to reach the nearest star, Proxima Centauri. For
the measurement of SFR we have to deal with much greater times of travel and
distances. Measurements of the historical SFRs have been taken up to a redshift
of four (z = 4); this light has traveled for almost 12 billion years – equivalent to a
distance of 12 billion light years (Connolly et al. 1997; Madau et al. 1998; Villar
et al. 2007). This thesis will anchor the SFRs “now”, that is, for the immediate
past in which light has traveled for less than 250 million years, or 2% of the age
of the Universe. This time window means that the dinosaurs were just beginning
to roam the Earth in the early Triassic Period as the furthest light was being
emitted. To put this time window in a different perspective, light from the most
nearby major galaxy, the Andromeda Galaxy, takes 1/100 of this time window or
2.5 million years to reach us. Andromeda’s light was emitted just as we evolved
from Australopithecus to the earliest Homonids.
To calculate a localized density of the rate of star formation (SFRD) one must
detect all the star-forming regions within a representative volume of the universe.
This requires a survey of the galaxies in that volume and follow-up observations
that measure their SFRs. Previous surveys with that purpose have measured light
in bands ranging from the ultraviolet (UV) to radio frequencies. Unfortunately,
all have suffered from statistical biases.
Prism-based surveys such as the KPNO International Spectroscopic Survey
(KISS) (Salzer et al. 2000) have used thin prisms placed over the front of the
telescope to disperse light into small spectra. Bright regions, so-called knots,
1. Introduction
4
in such a spectrum indicate the presence of the Hα emission line, hence of star
formation. This facilitates the study of SFR, a rough estimate of which can
be obtained directly from the survey images themselves. However, prism-based
surveys underestimate dwarf and other galaxies of low-surface-brightness; yet such
galaxies contain approximately one-third of all the HI gas (neutral hydrogen gas)
of the Universe and thus significantly contribute to star formation. Dwarfs and
other low-surface-brightness galaxies are not detected because prism-based images
spread incoming light across a band of pixels. Because these galaxies lie barely
above the noise level in standard optical images, the same flux distributed over
more pixels appears as noise.
Space-based surveys in both the UV and far infrared (FIR) bands have also
been used to measure star formation. NASA’s GALEX mission takes images in
the UV while the Spitzer Space Telescope does so in the FIR. Both are appropriate for the study of SFRs because both UV and FIR light correlate with star
formation. This correlation arises because massive O and B stars dominate the
light of star-forming regions and these stars emit primarily in the UV. The surrounding protostellar gas and dust absorb photons and reradiate them in the IR.
UV surveys, though, underestimate star formation in dusty galaxies and FIR surveys underestimate SFRs in low-dust galaxies. The gas and dust emitting FIR
radiation reduce the detectable number of UV photons, and thus produce the
UV bias. Low-dust systems do not contain enough particles to absorb the strong
UV radiation for FIR emission. In principle, the two kinds of survey could be
combined, but the high cost of these surveys restricts their use to regions of the
universe that are too small to allow a successful combination of their results.
Optical broadband surveys, such as the Sloan Digital Sky Survey (SDSS),
are numerous and cover vast areas of the sky (York et al. 2000). The Earth’s
1. Introduction
5
atmosphere absorbs few photons in the optical wavelengths, making them the
easiest to observe. The SFR is not directly linked to broadband fluxes so followup, narrow-band imaging is required for its determination. Narrow-band imaging
is too time-consuming to be used for surveying. This restriction forces broadband
surveys to include all galaxies in a given volume, because any galaxy may have
significant star formation, independent of its broadband magnitudes. Due to a bias
against low-surface-brightness galaxies, the volume sample may be incomplete. As
dim galaxies produce few photons per pixel of the CCD, they may remain below
the noise threshold.
All modern optical images are taken by a charge-coupled device or CCD. These
wafers of silicon turn incoming photons into electrons which are then stored on
the chip. After the appropriate integration time, all photons collected are passed
through an amplifier and an analog-to-digital converter which measures the voltage per pixel. This voltage per pixel is converted into a measure of counts which
are then passed to a computer. The voltage measurement is only accurate to
within a few electrons; the collected inaccuracies are termed noise.
Of the surveys used to detect star-forming galaxies, those in the radio band
like HIPASS (Meurer et al. 2006) use the longest wavelengths. Such surveys are
difficult to perform because the collecting area required for deep surveys requires
many hours on only the largest telescopes. They detect the constituent matter
for making stars, neutral hydrogen (HI gas). Although not all galaxies contain
measurable HI gas, and therefore not all galaxies in a volume are detected, an
HI survey is still appropriate for the measurement of star formation. HI gas is a
requirement for star formation, and thus, if it is not found, there can be no star
formation in the observed galaxy.
Two new radio surveys provide better measurements than earlier ones; they are
1. Introduction
6
the Arecibo Legacy Fast Arecibo L-band Feed Array (ALFALFA) and the Arecibo
Dual Beam Survey (ADBS). Both have been conducted at the 305 meter-diameter
Arecibo receiver – the most sensitive radio telescope ever constructed. The high
sensitivity allows a thorough investigation up to the unprecedented distance of 19
to 71 Mpc, or 62 to 231 million light years.
From a complete survey like ALFALFA or ADBS, a representative sample of
galaxies can be selected and their SFRs calculated. The calculation is based on
correlation studies of UV, FIR, or visible-light luminosities with the theoretical
SFR (the SFR cannot be directly measured as individual stars cannot be distinguished in galaxies beyond the Local Group). Of these, the optical band is
observed most easily as it contains the spectral line of Hα, the strength of which
is correlated with galaxian SFR. Hα is emitted when HI gas is ionized by extremely massive and hot stars, which have lifespans less than one thousandth of
that of our Sun. Because the ionizing stars are so short-lived, the detection of
Hα emission indicates star formation that is occuring now. The high degree of
correlation between the intensity of Hα and the mass of newly-formed stars per
year in a galaxy has been studied by Kennicut (1998, and references therein). In
large starburst galaxies, the collective mass of stars created per year ranges from
a few to one-hundred times the mass of the Sun.
1.2 Previous Work
Astronomers have defined our surrounding Universe using gravitational force to
distinguish regions. The Local Group (or Local Cluster) of galaxies contains two
large galaxies – the Milky Way and Andromeda galaxies – and approximately 30
small dwarf galaxies. This group is roughly three to six million light years across
1. Introduction
7
and lies in a deep gravitational well. The Local Supercluster is a much larger collection, ten times the size. Comprising 100 groups of galaxies, or more than 3300
total galaxies (Tully 1982), the Local Supercluster is an overabundance of galaxies
slowly moving towards its gravitational center. The Local Universe, as used in
this thesis, includes the region surrounding the Local Supercluster. Selecting only
galaxies outside the Local Supercluster but within the Local Universe reduces the
statistical bias caused by its known overabundance.
Many teams have explored the SFRD of our Local Universe prior to this study
(Gallego et al. 1995; Hanish et al. 2006; Houck et al. 2007; Salim et al. 2007).
All required large corrections because their samples were necessarily biased. An
incomplete sample of galaxies for a given volume, inaccuracies of the measurement
of distance, or too small a sampled volume can independently or jointly contribute
to large errors.
Most commonly, current SFRD calculations are unable to detect all galaxies
in a volume due to the Malmquist Bias (luminous galaxies are detected more
easily and thus preferentially). If galaxies are detected by emission independent
of star formation, weak sources may contain much of the star formation. Instead
of detecting galaxies based on emission independent of SFR, we propose to detect
galaxies by selecting for an ingredient of star formation. In that case the most
likely missed HI sources will also be insignificant contributors to the total SFR.
An accurate determination of distance is needed to define the boundaries of a
volume, as well as to calculate the galaxy luminosity. The Survey for Ionization
in Neutral Gas Galaxies (SINGG) focuses heavily on sources within the Local
Supercluster. If used for a calculation of SFRD, as by Hanish et al. (2006), that
restriction of the sample introduces peculiar velocity errors that are the equivalent
of up to 20% for distance and 40% for luminosity (Meurer et al. 2006). These errors
1. Introduction
8
are due to gravitational attraction between galaxies that adds velocity unrelated
to the velocity of the expansion of the Universe, used to determine the distance.
To address the distance problem, our study selects galaxies with a velocity range
of 1460-5300 km/s. As peculiar velocities (those velocities due to gravitational
attraction between galaxies) are on the order of 0-300 km/s throughout the Universe, they amount to a smaller percentage, roughly 6-20%, of the magnitude of
the velocity, and introduce a limited error to our measurements of distance.
A density, being mass per unit volume, should be unbiased by local mass
fluctuations. The majority of the Local Supercluster is at a distance of less than
20 Mpc. By Hubble’s Law (v = H0 D), the velocity limit due to the expansion
of the Universe is less than 1500 km/s. The proposed study chooses a sample
volume outside the high-density region of the Local Supercluster.
1.3 Goals
This study is designed to anchor our understanding of the SFRD of the Local
Universe “now” to an unprecedented level. The thesis includes the preliminary
value of the local SFRD from ADBS as well as measured Hα fluxes and SFRs
for approximately 200 ADBS and ALFALFA Hα galaxies. In addition, all of the
software and calculations have been prepared to analyze all future ALFALFA Hα
observations.
Chapter two introduces the physics of observable hydrogen as it pertains to
observations of Hα and HI. Understanding the nature of hydrogen emission –
HI and Hα – is key to the derivation of the SFRD. This understanding also
establishes the advantages of SFRD calculations based on HI-selected samples.
Using the physical understanding of hydrogen, the methods of its observation
can be explained. This explanation includes a description of the Arecibo radio
1. Introduction
9
telescope and the Kitt Peak 0.9 meter optical telescope.
Chapter three describes in detail the observational methods followed at Kitt
Peak. The inclusion of a well-defined protocol is important because the ALFALFA
Hα data may lose statistical completeness with inconsistent observational techniques.
Chapter four describes the extraction of the brightness of each galaxy. This
includes new software written expressly for the reduction of ALFALFA Hα images.
The data-reduction includes correcting for instrumental signatures inherent to
CCD images (flat-fielding and biasing), in addition to the isolation of the signal
from the Hα line (continuum subtraction). Once the Hα line has been isolated,
its strength must be measured to determine the galaxy’s Hα brightness. The
software described in this chapter has decreased galaxy reductions significantly
and has regularized the photometry process to improve on the homogeneity of the
final data products.
Chapter five describes the series of corrections required to develop an accurate
measurement of the target galaxy’s Hα flux. From this accurate flux, the derivation of the SFR is demonstrated and applied to the spring ALFALFA Hα galaxies.
While the ALFALA Hα sample is not yet statistically complete, the ADBS sample
is. The SFRD has been calculated for the ADBS sample and is included at the
end of the chapter.
The final chapter includes a summary of work completed in addition to future work to be performed with ADBS and ALFALA Hα. At least three current
projects use ALFALFA Hα data, and more projects are expected when the survey
has been completed. A sample of this size and to this distance has broad applications. Therefore, the ideas included present only the beginnings of ADBS and
ALFALFA Hα follow-up programs.
Chapter 2
Tracing Hydrogen
Astronomy, unlike most other sciences, obtains data not by manipulation but only
by observation. In most instances, this means the detection of photons emitted
by the objects under study. Because hydrogen is the most common component
both of the Universe and of stellar formation, it is imperative to understand the
physics of photon emission from hydrogen.
The transition of an electron in a hydrogen atom between energy levels absorbs
or emits energy in the form of photons. When an atom’s electrons exist in states
above the lowest possible energy level, they can transition to lower levels, emitting
photons in the process. The energy difference associated with these transitions
can be large, as in the case of the optical-wavelength Hα line, or it can be very
small, as in the case of radio-frequency 21-cm emission.
2.1 Physics of Hα
The Hα emission line has many important applications for astronomers. The line
lies in the visible part of the spectrum and therefore has been easily detected since
the time of spectroscopic observations in the early 1800s. Images of star-forming
nebulae, such as the Orion nebula, include extremely strong, red Hα lines. Hα is
often one of the strongest lines due to the high abundance of hydrogen (75% of
the Universe by mass). The luminosity of Hα is correlated with star formation,
10
11
2. Tracing Hydrogen
Continuum
n=5
n=4
n=3
-0.5 eV
-0.9 eV
-1.5 eV
n=2
-3.4 eV
Ηα Ηβ Ηγ
n=1
Ionizing
photon
-13.6 eV
Figure 2.1: The energy levels of hydrogen as related to the creation of Hα
making the study of Hα interesting and particularly informative for astronomers
(Kennicutt 1998).
The Hα line is created by an energy transition of a hydrogen atom’s electron
from the third to the second energy level. Transitions between orbitals n > 2 to
n = 2 make up the Balmer series. A hydrogen electron’s transition from the n = 3
state to the n = 2 state is the longest-wavelength member of the Balmer series,
or the α transition, and results in a photon having a wavelength of 6562.81 Å.
Transitions between energy levels are relatively common. Absorption of an
incoming photon or collisions between atoms can boost the energy level of a bound
electron, after which the electron can transition down energy levels until it reaches
the lowest energy level (ground state). Due to the electron’s ability to lose different
quanta of energy, not all excited electrons will make an Hα transition, although
many will.
Exciting a hydrogen electron from the n = 1 to the continuum level (ionization)
2. Tracing Hydrogen
12
requires a relatively high-energy photon of at least 13.6 eV. Exciting the electron
to only the n = 3 state, the lowest position necessary for an Hα transition, requires
almost 90% of the ionization energy (12.1 eV). For a photon to be absorbed by
hydrogen, it must be either exactly equal to an energy step or greater than the
ionization energy level. Therefore, most photons exciting the electron above the
n = 3 state will have energies greater than 13.6 eV or wavelengths less than
912 Å. The freed electrons often possess significant amounts of kinetic energy
which gives them large velocities relative to the hydrogen nuclei. This lowers
the probability of recombination occurring. However, electron capture by the
hydrogen ion does occur eventually, and usually to an excited state (high-n value).
The recaptured electron will rapidly de-excite to the lowest energy level. This often
occurs in multiple steps or transitions with each transition being accompanied by
the emission of a photon with a discrete amount of energy.
To understand what kinds of stars can create this type of photon, we can
use Wein’s Displacement Law which relates an object’s temperature to the peak
wavelength emitted: λpeak T = 2.898 × 10−3 m K. This relation indicates that the
photosphere, or light-emitting part of a star must be at least 31,000 K to create
large numbers of ionizing photons. This calculation does not account for comparison of the rate of recombination and ionization, but still accurately predicts that
only hot B and O stars (temperatures > 28,000 K) can ionize hydrogen enough
to create the Hα line. Such stars are the hottest of stars and are extremely short
lived with lifetimes of roughly three to thirty million years. Thus, they are necessarily recently formed. When one detects Hα emission from ionized gas, it is
likely that one is observing regions of current star formation.
In addition to young, hot, ionizing stars, an Hα-emitting region must have a
significant density of hydrogen gas to be visible. This requirement results from
13
2. Tracing Hydrogen
the recombination process being inefficient at low densities. Without recombinations occurring, no Hα emission is possible. High-density gas regions, particularly
those dense enough for Hα emission, form in gravitational wells along with starformation. This correlation also links Hα with star formation.
Astronomers have created models of star-formation to understand the SFRs of
galaxies. With the discovery of extreme starburst galaxies by the Infrared Astronomy Satellite, the field made leaps forward (Kennicutt 1998). These models have
been improved and are now used to determine correlations of SFR-Hα luminosity.
In particular, the models used in Kennicutt 1998 are based on a Salpeter initial
mass function (0.1-100 M ). From these models, the resulting correlation is:
SF R(M /year) = 7.9 × 10−42 L(Hα) (ergs/s)
(2.1)
Kennicutt’s equation uses a constant multiplied by the luminosity of the Hα line
(L(Hα)), the luminosity of which is derived from ALFALFA Hα images in chapters
four and five, and produces the rate of star formation in terms of solar masses per
year.
2.2 Physics of HI
Hα is used to measure the SFR but the density component of the SFRD must be
determined from a statistically significant sample. The samples used in this thesis
were derived from observations of neutral hydrogen (HI gas).
HI gas is detectable due to the rare, forbidden spin-flip transition of a neutral
hydrogen atom. In neutral hydrogen, a single electron orbits a single proton, each
with a spin state of up or down. Parallel spin states of both particles have a
slightly higher energy than anti-parallel spin states. Hydrogen atoms enter the
2. Tracing Hydrogen
14
excited spin state via collisions between atoms. The de-excitation from the parallel
to the anti-parallel state emits a photon having a wavelength of about 21 cm. In
a cold neutral gas cloud, the average time in this excited state for HI is roughly
1.1 × 107 years (Binney & Merrifield 1998). Even with such a rare event, due
to the high density of hydrogen in the interstellar medium (ISM), the HI line is
easily detectable in HI-rich galaxies.
As hydrogen is the building block of new stars, a region lacking dense hydrogen
cannot support stellar formation. Therefore, selecting for the presence of dense
hydrogen determines all possible locations for star formation.
2.3 Observing HI: ADBS and ALFALFA
Multiple surveys have been conducted that identify galaxies via their HI gas content (Staveley-Smith et al. 2000; Rosenberg & Schneider 2000; Giovanelli et al.
2005). The latter two were conducted at the Arecibo radio telescope. Arecibo’s
dish is 305 meters in diameter. Because light collection increases by a factor of
the radius squared, Arecibo is much more sensitive than any of the world’s other
radio telescopes.
In the mid 1990s, a survey was conducted using Arecibo called the Arecibo
Dual-Beam Survey (ADBS) (Rosenberg & Schneider 2000). This survey used
a pair of old-fashioned 21-cm line-feeds while the telescope was undergoing a
renovation. The survey covered only 420 square degrees, or 1% of the sky, but
did so at an extremely deep level. The telescope upgrade concurrent to the ADBS
was the introduction of the Gregorian optics, described below. During the early
stages of the upgrade, the telescope still functioned, but had a very limited range
of movement. This was a perfect opportunity to have the telescope be stationary
2. Tracing Hydrogen
15
and let the sky drift past the telescope due to the Earth’s rotation. ADBS was
performed in this manner.
Roughly ten years later, the ongoing ALFALFA survey was begun at Arecibo
to map out over 7000 square degrees or more than 15% of the sky. ALFALFA is a
survey designed to avoid the Galactic Plane but to include all regions of the sky
visible to Arecibo. It, too, uses the drift-scan technique.
Observations in the radio band have a distinct advantage over those taken in
the optical band: radio band data include both position and frequency/wavelength
information. This eliminates the need for additional follow-up observations to
measure the velocity of the galaxy. The observed frequency of the 21-cm line
is correlated with velocity, making 21-cm survey data three-dimensional. Edwin
Hubble proposed a linear correlation between distance and the velocity at which
a galaxy is receding from us, which is now called Hubble’s Law.
Distance (Mpc) = V elocity (km/s) /H0 (km/s/Mpc)
(2.2)
Outside of the Local Supercluster, this relation is accurate to approximately ±5%.
Using the redshifted position of the 21-cm HI-emission line, we can limit our survey
based on distance and select galaxies at specific distances for further study.
The Arecibo telescope can effectively find all of the galaxies with HI masses
as low as 106 M within 70 Mpc. This is equivalent to 0.0001% of the Milky
Way’s HI mass (Nakanishi & Sofue 2002). With such high sensitivity, a galaxy
sample within 70 Mpc requires very little extrapolation to account for undetected,
HI-poor galaxies.
HI observations at Arecibo are taken from the thousand-ton platform hanging
500 feet above the dish. The telescope dish was planned as a stationary parabola
2. Tracing Hydrogen
16
Figure 2.2: A diagram of the Arecibo radio telescope and the detection of light by a
line feed. A spherical reflector can be thought of as a series of concentric rings centered
beneath the line feed. Incoming light hitting anywhere on a ring will come to the same
focus. Light hitting different concentric rings (colors reflect the ring hit) will focus at a
different height.
for observing the atmosphere directly above it. Astronomers finally convinced the
Arecibo designers to make the dish a spherical reflector. This modification made
multi-wavelength observations more difficult but allowed observations of up to 20
degrees from the zenith. To account for the aberrations induced by the spherical
reflector, the renovations made during the mid-1990s included a six-story dome,
hung from the platform above the dish, which includes reflectors to correct the
beam for spherical aberration. These Gregorian optics have enabled the use of
the new seven-beam ALFA receiver.
A receiver or detector of light, for any wavelength, should collect data from a
range of wavelengths and positions at a single focus. An optical telescope must
2. Tracing Hydrogen
17
focus light hitting a large mirror to a CCD chip roughly one inch across. Similarly,
radio receivers measure a range of wavelengths, although positional information
is limited. Arecibo’s original detectors were line feeds – antennae roughly 100
feet long. Spherical aberration – the error induced by using a spherical reflector
– leads parallel photons hitting different positions on the reflector to focus along
a line (see Figure 2.3). The line feeds stretched along this line to detect all of the
possible photons.
A line feed is only capable of detecting radio-frequency information from a
single point on the sky. For the Arecibo reflector, the effective angular resolution
of the line-feeds was 3.3 arcminutes at 21 cm. This is equivalent to an optical
telescope with a poorly focused mirror and one single pixel. By observing strips
of right ascension (RA) in the sky, one observes the equivalent of one row of pixels
and if the telescope is moved in declination (Dec) between observations, a grid is
observed.
The modern Gregorian optics include secondary and tertiary reflectors that
focus the incoming photons to a single point. Using this method, detectors can
be much smaller, allowing for multiple beam receivers. ALFA has seven receivers,
giving much greater positional information with each observation. These multiple
receivers aid follow-up imaging, such as ALFALFA Hα, because the beam is a
fraction of the CCD field of view and neighboring galaxies can be distinguished
more easily.
For ALFALFA, the receiver is positioned in a single declination strip as the sky
drifts past throughout an observing session. This means the detector is positioned
along the arm to the north or south of center. The sky then drifts past in RA and
data are continually recorded. During a different session, beginning at the same
RA, the receiver is positioned slightly further south or north of its original position.
2. Tracing Hydrogen
18
Figure 2.3: The seven beams of the ALFA receiver are rotated so that each beam
overlaps halfway with at least one more beam. Red lines are drawn at the edge of each
beam. Notice that the edge of one beam lies in the center of another beam. Excluding
edges, all parts of the sky are observed at least twice. Plus-signs refer to the center of
each beam and beams are numbered. Source image from Heiles (2004).
2. Tracing Hydrogen
19
Therefore, it detects the adjacent strip and provides now a wider rectangle of
sky coverage. The process is repeated until all of the visible sky is covered. In
this setup, no time is lost due to advancing the telescope to the next position.
Incoming photons reflect off of the dish and are re-reflected off of secondary and
tertiary mirrors into one of ALFA’s seven overlapping receivers (see Figure 2.3).
Due to their configuration, a galaxy is observed for 45 seconds on average and
the intensity profile from multiple beams enables a spatial localization previously
impossible in single-dish radio receivers.
ADBS used two line feeds to have the equivalent of two pixel coverage of the
sky. In a similar manner to ALFALFA, it used the drift-scan technique to maximize the active observational time and minimize time lost to telescope positioning.
Sources were detected by either receiver for less than the 45 seconds of ALFALFA
due to not having multiple beams in a row. Repeated observations of the same
positions allowed ADBS to have a similar depth to ALFALFA, although the sky
coverage suffered as a consequence.
2.4 Observing Hα: 0.9 meter Telescope
We selected a subset of ADBS and ALFALFA galaxies for follow-up observations.
The follow-up observations of ADBS were a pilot study testing whether the WIYN
0.9 meter telescope could be used for sensitive Hα observations. Due to the success
of the ADBS work, a much larger subset of ALFALFA galaxies has been selected.
Hα follow-up observations do not require the use of a large telescope. With a
telescope as small as 0.9 meters, 40 minutes of integration yield sensitivity to a
SFR as low as 0.005 M /year at 70 Mpc.
Narrow filters – roughly 65-70 Å wide – were used to detect only the Hα line.
2. Tracing Hydrogen
20
Figure 2.4: An example spectrum demonstrating a nonzero continuum level, Hα emission line, and [NII] emission line. It is this continuum level that is subtracted from the
Hα narrow-band images. The flux, fλ is in terms of erg/s/cm2 . Image by John Salzer.
Due to Hubble’s Law describing the expansion of the Universe, most galaxies
are receding from us. The emission lines of ADBS and ALFALFA Hα observed
galaxies are therefore redshifted by between 50 and 100 Å. Knowing the recessional
velocity of a galaxy allows a selection of one of several filters to detect the Hα
emission line. The filters are selected to match the ranges of velocities of only
those galaxies outside the Local Supercluster.
A galaxy’s spectrum includes emission lines and absorption lines superimposed
on a continuous background level of photons (see Figure 2.4). To determine only
the strength of the Hα emission line, one has to account for the continuum flux
by subtracting a wide-band image that covers multiple lines as well as the background. The Johnson-Morgan R-band covers the part of the spectrum in which
the Hα line lies and is used for measuring the continuum flux.
All modern telescopic filters are pieces of glass, inserted between the telescope
2. Tracing Hydrogen
21
and the camera, with many 100-nm-thick layers of metal oxide applied to one
surface. These metal layers produce reflective cavities that, through constructive
interference, transmit the flux of the selected wavelengths. Similarly, through destructive interference, all other wavelengths are removed from the beam (Murphy
et al. 2008).
Chapter 3
Telescopic Observations
The Hα-imaging data used for the ADBS study were collected over a series of
three runs between October 2004 and November 2005. To date, we have had
five imaging runs for observations of ALFALFA targets. ALFALFA observations
were performed by John Salzer, John Cannon, Jessica Kellar, Anna Williams, and
myself in collaboration with Ed Moran and Chris Dieck at the WIYN 0.9 meter
telescope at Kitt Peak National Observatory. Galaxy samples were carefully chosen leading to a statistically complete subset of ADBS galaxies but a statistically
incomplete sample of ALFALFA galaxies. This latter set will remain incomplete
until more galaxies have been added to the ALFALFA catalog.
3.1 HI-selected Galaxy Samples
The galaxy samples for follow-up observations of ADBS and ALFALFA are important for an accurate assessment of the SFRD. Both ADBS and ALFALFA were
designed to avoid flux uncertainties and selection incompleteness due to regions
of high opacity associated with the Galactic Plane. In addition, areas of known
density concentrations (e.g., the Local Supercluster) were avoided. The ADBS
sample is comprised of 82 galaxies chosen to represent a range of positions on the
sky. This broad selection of positions prevents errors due to the selection of a
single over-dense region due to random fluctuations.
22
3. Telescopic Observations
23
The ALFALFA sample has been limited by the rate of data-reduction of the
ALFALFA team. These data are reduced in “grids” of roughly two-by-two degrees
square. The grids reduced to date have not been randomly selected, a choice
which may introduce a general bias. It is likely that over-dense regions have been
preferentially selected due to their being interesting to the ALFALFA observers.
As time goes on, the full-sky area of the ALFALFA survey will become available,
and the selection of an unbiased sample will be possible.
3.2 Observational Methods
Well over two hundred galaxies have been observed between the two datasets.
Each galaxy is imaged three times taking approximately one hour. Observations
are made as follows: a narrow-band Hα integration of twenty minutes, a broadband R integration of four minutes, and a second narrow-band integration of
twenty minutes. The rest of the hour is used to position the telescope and find
a guide star. Read-out times of two minutes per image add to the total time per
field. Dome flat fields – images of a matte screen designed to detect sensitivity
variations – and biases – zero-second images designed to detect the noise level of
the CCD amplifier – are taken at the beginning of each night in addition to one
standard star calibration image per two galaxies.
Prior to observations, five dome flats are taken for each filter used. The use
of a dome-flat system has recently been demonstrated to be preferable to twilight
flats or nighttime flats (Marshall & DePoy 2005). Each galaxy requires two filters:
a broadband filter to detect the continuum flux from a field and a narrow-band
Hα filter to detect the flux from only the galaxy’s Hα line. In addition to flat
fields, ten bias frames are taken.
24
3. Telescopic Observations
Table 3.1: Filter wavelengths and integration times.
Filter
Johnson R
KP1564
KP1565
Central Wavelength (Å)
6300
6618
6653
FWHM (Å)
1200
74
68
Integration Time (s)
240
1200
1200
The broadband filter used for ADBS and ALFALFA data is the R-band filter
of the Johnson-Morgan system. This filter passes wavelengths from 6000-7800 Å.
This filter was chosen as it covers the same part of the spectrum as the redshifted
Hα filters and therefore provides an appropriate continuum level to subtract from
the narrow-band image. One four-minute exposure is taken of each galaxy in the
R-band.
The narrow band filters used are selected based on the redshift of the galaxy
(known from the HI survey data). These filters take into account the redshifting
of the Hα line at 6562.81 Å to between 6618 and 6668 Å. Two Kitt Peak filters
are used to cover this wavelength range – KP1564 and KP1565 (see Figure 7.1
for filter tracings). Two twenty-minute narrow-band exposures are taken of each
galaxy sandwiching the broadband image.
All images are taken with guide cameras turned on to prevent drift during
and between images. Approximately every two hours, narrow-band standard-star
calibration images are taken. These images provide a calibration of the absolute
magnitude of the observed galaxies. Images were taken of Hα-calibrated stars
BD+17 4708, BD + 26 2606, HD 19445, and HD 84937.
3.3 Problems: Fall Data
Fall data from ALFALFA follow-up observations have not been included in this
thesis. Galaxy images were taken in both September of 2006 and October of
25
3. Telescopic Observations
Table 3.2: Observing runs for ADBS and ALFALFA follow-up imaging
Project
ADBS
ALFALFA
Run
October 2004
April 2005
November 2005
March 2006
September 2006
February 2007
May 2007
October 2007
2007. All images were processed and Hα luminosities were derived. Due to an
inadvertent error in the insertion of filters for the October 2007 run, these data
were observed through the wrong narrow-band filter. This error rendered all data
obtained in October 2007 useless. These galaxies will be re-observed in the fall of
2008.
With the loss of the October 2007 data, the fall data sample cannot afford
us a useable SFRD due to the limited number of objects observed in September
2006. Therefore, the results will be saved for future analyses.
3. Telescopic Observations
Figure 3.1: Filter curves for the two narrow-band Hα filters
26
Chapter 4
Data Reduction and Measurement
4.1 CCDs as Astronomical Detectors
Images in modern astronomy are taken by a charge-coupled device (CCD). The
CCD provides many benefits over photographic plates, including high sensitivity,
a linear response to light, and efficient data-processing. While photographic plates
convert only 1-2% of incoming photons to a signal on the plate, CCDs detect and
record up to 90% of the incoming photons. This is an increase of sensitivity of
up to 90-fold. Similar to the human eye, the photographic plate detects photons
logarithmically. For example, a factor of two difference in incoming intensity
translates to a factor of 0.3 of apparent brightening of a plate. The CCD detects
individual photons and thus is a linear detector. For a CCD, a factor of two
difference in incoming flux leads to a factor of two difference of measured intensity.
In addition to linearity, the CCD, in being connected to a computer, allows for
much faster data-processing and sophisticated forms of data analysis. Before
astronomical images were reduced by computer, source intensity was measured
either by eye or by a photomultiplier tube. Both methods were slower and less
accurate than the CCD. Furthermore, computer-based processing speeds largescale corrections of data such as those described in Chapter 3 and in this chapter.
The CCD is a chip of silicon into which impurities, boron and phosphorus, have
27
4. Data Reduction and Measurement
28
been added. An incoming photon strikes the impure, or doped, silicon, which frees
an electron into the conduction band. The resultant photoelectrons are passed
to the conduction band and become mobile. Silicon is used because its metallic
structure consists of a lattice that has a conduction band. Photoelectrons can
move easily across the lattice in the conduction band. The band in which the
electrons lie is divided in one axis by a series of insulators and in the other axis
by sets of three wires charged negatively, positively, and negatively, respectively.
The negatively charged wires repel the electrons in the conduction band while
the positively charged wire attracts. In the resulting square picture elements, or
pixels, electrons can be trapped for eventual measurement.
For silicon, if the incoming photon has a wavelength shorter than about 1.1 µm,
an electron is liberated and can move into the conduction band. Here it is stored
in a pixel along with all other energized electrons. At the end of an integration,
the pixels are successively passed through an amplifier and an analog-to-digital
converter to detect the number of electrons in each pixel. This count of electrons
per pixel can be displayed as an image.
4.2 Data Reduction
4.2.1
Preliminary Reductions
All CCD images require two types of corrections – an additive correction and a
multiplicative correction.
During the CCD readout, prior to the first amplification stage, each pixel has
added to it an initial baseline charge, called a bias voltage. This adds a roughly
constant number of electrons per pixel. The bias level can vary from pixel to pixel
but the average value is roughly constant across an image and between images.
4. Data Reduction and Measurement
29
Ten bias images are acquired to assess this additive correction. Bias frames are
zero second images in which the CCD is not exposed to light. Ideally then, the
CCD is creating an image of only these injected electrons. Because the total bias
level can vary between images, modern CCDs include an overscan region. After
a row of pixels has been measured, the read-out process repeats 32 more times.
These extra pixels are used to provide an instantaneous measurement of the bias
level relevant for each row of the image.
In the first stage of processing, the overscan region of each image is fit by a
low-order polynomial (to reduce noise). Then, this value is subtracted from each
image, including the bias frames. Next, the ten bias frames are averaged together
and subtracted from all galaxy images. This corrects for any two-dimensional
structure in the bias.
The silicon of the CCD is not consistently sensitive to incoming photons,
Therefore, different pixels will have different sensitivities. To account for these
sensitivity variations, flat-field images are taken. These images, as mentioned
above, are taken of a matte-white screen which is reflecting light evenly across
the entire surface. Therefore, every pixel of the CCD is exposed to the same intensity of light. Variations in measured counts in the resulting image reflect the
pixel-to-pixel sensitivity variations of the detector. The five flat-field images per
filter taken each afternoon are averaged to a mean flat-field image. A normalized version of this image is then divided into each data image to correct for the
variations in pixel-to-pixel sensitivity.
In addition to photons, CCDs are sensitive to charged alpha-particles, or cosmic rays. These are highly energetic Helium nuclei emitted by the Sun and other
sources, which have enough energy to push electrons into the conduction band.
Cosmic rays, however, look different from photon-based detections. These differ-
4. Data Reduction and Measurement
30
ences are used in an IRAF script written by van Dokkum to distinguish between
cosmic rays and astronomical objects in the image (van Dokkum 2001). This
script was run on the images to remove signals from cosmic rays.
4.2.2
Hα-Image Reductions
The original Hα images are further reduced by a process that effectively subtracts
the continuum background. The goal of this step of the processing is to create a
continuum-subtracted Hα image. In order to achieve this goal, one must align,
smooth, and scale the three images to match. These matched images are then
used to subtract the broadband image from the narrow-band images to create a
single Hα-flux image. The process was streamlined with IRAF scripts which are
described below.
At this stage of the processing, the data will have been corrected for all of the
instrumental signatures. Subsequently, the subset of cosmic-ray cleaned observations for each galaxy is moved into individual directories. These images are two
narrow-band Hα images and a broadband R-image.
Because the individual pixel counts of the R-band image will eventually be
subtracted from those of the Hα image, the galaxy images must be aligned to
a common center. Using the star-centroiding from IRAF’s imexamine task, the
exact center of the star can be determined with sub-pixel precision by assuming a
Gaussian fit to the data. Image alignment is performed by John Salzer’s getshfts
IRAF script. The first image is displayed and the user marks no fewer than
ten stars using IRAF’s task imexamine. The stars are selected to be uniformly
distributed across the first image. Subsequently, the user identifies a single star in
each of the remaining images, aided by a box drawn around the previous location
4. Data Reduction and Measurement
31
of the star. The relative centers of the stars are compared between images and an
offset is determined for each image.
The IRAF script doalign, created by John Salzer, is used for the alignment of
the images. The offsets found by getshfts are used by IRAF’s imalign task to align
the images to within ±0.1 pixel of each other. After the images have been shifted,
the individual frames will contain pixels at their edges that are not common to
all three images. The non-overlapping parts of each image are removed.
The coordinates assigned to each image by the telescope control software are
not very accurate. However, accurate positions of all objects in each frame are
required. To achieve this accuracy, a script called getastrom (Kellar 2008) is
used. The approximate image location is used to retrieve a map of bright stars
from the US Naval Observatory (USNO) Stellar Positions Catalog. The locations
of the USNO catalog stars are displayed on the image, and the user matches the
pattern of stellar positions from the catalog with those in the image to determine a
positional and rotational offset. The headers of each image are then updated with
the new coordinates. This proves useful in any follow-up observations, because all
objects in the frame now have right ascensions and declinations with sub-arcsecond
precision.
Due to variations in atmospheric seeing while each image is taken, the fullwidth at half maximum (FWHM) of the Gaussian profile of stars in each image
may be different. A larger FWHM corresponds to a smearing of the image while a
lower FWHM corresponds to a higher concentration of photons very near the true
stellar position. Because subtracting a concentrated Gaussian profile (point-like)
from a wide Gaussian profile (disk-like) would result in negative photons in the
stellar cores (see Figure 4.1), all images must be smoothed to the highest FWHM.
An IRAF script findfwhm, created by Scott Randall, uses the IRAF task imex-
4. Data Reduction and Measurement
32
Figure 4.1: A star with an undersubtracted core and an oversubtracted outer region
due to nonmatching point-spread functions.
amine to find the FWHM of stars in an image. At least four stars are selected near
the galaxy in the image from which the FWHM is calculated. A graph then shows
the FWHM determined from each star and the user is prompted to remove outliers. In general, a 3σ cutoff is used to determine outliers. The remaining FWHMs
are averaged together to determine the FWHM of the image in the region of the
galaxy. This process is done for each image in the group. If the FHWM of any
image differs from that of another in the group by more than 0.2”, the image
or images with the smallest FWHMs are Gaussian-smoothed by the IRAF script
rgauss.
To use the IRAF script rgauss, the user calculates by hand the required σ for
p
smoothing the image using the equation σ = (W/2.354)2 − (w/2.354)2 where
W is the larger FWHM and w is the smaller FWHM. Rgauss then uses σ input
by the user to smooth the image appropriately.
4. Data Reduction and Measurement
33
Figure 4.2: The R-band (left side) and Hα-subtracted (right side) images of three
galaxies are shown in this figure. They are, from top to bottom, ADBS galaxies
112134+2020, 114921+2607, and 125850+1308.
4. Data Reduction and Measurement
34
The images now are all at the appropriate positions and shapes to be subtracted but not yet at the same intensities. Due to the width of the broadband R
filter in comparison with the Hα filter, the broadband images have approximately
four times the intensity of the narrow-band images. To account for this difference, the IRAF script getscale, created by John Salzer, uses the flux values of at
least five stars selected by the user, to determine the scale differences between the
narrow-band and broadband images. The script then uses imarith to scale the
images to a uniform intensity.
The results of the process are three images: two narrow-band Hα images
and one broadband R image. The images have now been aligned to a common
position. All have the same FWHM and intensity scale. The broadband R image
is subtracted from the two Hα images and the resulting difference images are
averaged together. This final image contains only the narrow-band flux above the
continuum – ideally all of the Hα flux. Examples of final Hα images are shown in
Figure 4.2.
4.3 Photometry
From the Hα-subtracted image, the number of counts must be measured from
which the Hα apparent magnitude can be determined. Similarly, the apparent
magnitude of the broadband image is required for flux corrections. The IRAF
task phot was designed to perform accurate photometry.
Phot was designed to determine the number of counts within a selected aperture taking into account the background flux. The result of the script is an
apparent magnitude with an arbitrary – but constant – zero point.
A circular aperture is used because elliptical apertures do not decrease errors
4. Data Reduction and Measurement
35
significantly (Webb 2005). The circular aperture is determined by eye and is
selected to include all flux visible in both R and subtracted Hα images due to the
galaxy. Phot uses the aperture to measure the flux of the object and a surrounding
annulus 15 pixels wide to determine the background count. The background
level is subtracted from the flux in the aperture to give an apparent instrumental
magnitude.
For the calibration of the Hα images, phot is similarly used to determine the
Hα apparent magnitude of standard stars HD 19445, BD+19 4907, HD 84937,
and BD+26 2606.
Flux corrections require calibrated broadband images. Because R-band images have already been taken of each galaxy, these images can be used to find
the apparent magnitudes of the galaxies. The same procedure is used as above to
determine the galaxy magnitude except that no calibration images are taken for
the R-band. The spring sky covered by ALFALFA is covered mostly by the Sloan
Digital Sky Survey (SDSS) in which there is accurate photometry for all bright
stars (all galaxies greater than 10 hours in RA are covered). By determining the
apparent magnitude of at least three stars in each broadband image (using the
same aperture technique), the apparent magnitudes of each star can be compared
with the calibrated apparent magnitudes from SDSS to determine a zero-point
offset. SDSS uses a non-standard filter system called the u’g’r’i’z’ system. An accurate conversion factor has been determined to convert to the R-band magnitude
using the SDSS g’ and r’ magnitudes (Smith et al. 2002).
R = −0.14g 0 + 1.14r0 + 0.14
From the calibrated apparent R-band magnitude, the offset to the broadband
4. Data Reduction and Measurement
36
image can be calculated, and the apparent magnitude of the galaxy can be determined. For those galaxies not covered by the SDSS, a nightly average zero-point
can be determined using the images covered by SDSS. This average zero-point is
used to determine the calibrated magnitude of galaxies between 9 and 10 hours
RA.
4.4 New ALFALFA Hα Software
The data-reduction process described above takes a minimum of 15 minutes per
galaxy (not including the photometric measurements). Many of the steps take
repetitive user input and others are open to automation. The user would select
stars from the image for measurement at multiple stages. Many times, the selection of the stars used could be the same for more than one task (e.g., findfwhm
and getscale). Furthermore, each step created a new set of images which was
then passed to the next script. Because the eventual goal of this project is the
measurement of Hα emission from almost 1000 galaxies, speed and reliability are
of the essence. Therefore, an IRAF script called pipeline was written to speed
the process by keeping track of the images throughout, reuse the stellar positions
determined by the user, and smoothly pass input between the various routines.
The most time-consuming part of the process was the calculation, by hand, of the
σ for smoothing images. A separate script called equalize was written to automate this process. Stellar positions, selected by the user for the initial FWHM
calculation, are reused for later calculations in getscale.
4.4.1
Data Reduction
The script equalize prompts the user to select a series of stars surrounding the
galaxy in the broadband image. It uses imexamine to capture the centroided
4. Data Reduction and Measurement
37
location of the selected stars. It feeds the locations into a modified version of
findfwhm which uses a list of coordinates. The user is then shown a graph of the
FWHM of each of the selected stars and is prompted to delete outliers. Using the
same coordinate list from the first image, the process is repeated for each image
in the group. Finally, if the FWHMs of the images vary from the maximum by
more than 0.2, σ is computed for the lower FWHM image or images and each
image is smoothed to have a matching FWHM.
The creation of the various input lists required for the different routines in
the data-reduction process is time-consuming. This is the second most timeconsuming part of the process. The script pipeline was written to automatically
select each step and create the input list for each of the following steps.
Pipeline requires the list of three input images with the broadband image listed
first. The first step is the same as the getshfts task described above. After the user
has completed getshfts, doalign is automatically run. The user is then prompted
to match the pattern of stars required for the accurate coordinates assigned by
getastrom. Finally, the user is asked for the input of equalize. The coordinate
list from equalize is then reused for the scaling of the images. The scaled images
are then ready to be combined. First, the broadband image is subtracted from
each of the narrow-band images using imarith. The two resulting images are then
combined, using imcombine, by a simple average.
By introducing this software, the data-reduction time has been decreased to
two and a half minutes per galaxy. This more than five-fold decrease in reduction
time is a key to the success of the ALFALFA Hα project. As a result of using
pipeline, the process is highly automated, user input mistakes are minimized, and
the resulting Hα data are more homogeneous.
4. Data Reduction and Measurement
4.4.2
38
Photometry
A script was written to expedite the process of determining the aperture to be
used for the galaxy photometry. This program was used both in the determination
of R-band and Hα apparent magnitudes.
The user is prompted in the IRAF script haphot to select the center of the
galaxy. The script uses the IRAF task imexamine to determine the location selected by the user. Centroiding is turned off as galaxies do not have the same
point-spread functions (PSF) as those of stars. Five evenly-spaced concentric circles are drawn on the image using the IRAF task tvmark from radii of 20 to 40
pixels, although these numbers are user-configurable (see Figure 4.3). The user
relocates the center of the circles or changes the radius of the smallest circle iteratively until the desired center and size are reached. Once the aperture contains
all of the visible light from the galaxy but no more than five pixels beyond the
edge of the galaxy, the user chooses the appropriate aperture number.
If stars lie in the photometry aperture, the user is prompted to mask the star
using the IRAF task imedit. This averages the pixel values in an annulus of radius
five pixels and width of one to fill in those pixels within the radius.
From the masking stage, the parameters are automatically passed to phot
which measures an apparent magnitude inside the user specified aperture, and
copies the resulting magnitude file produced by phot to a common directory for
easy processing.
The R-band and Hα images are calibrated differently. The Hα images are
calibrated using the standard star images taken at various times throughout the
night. Calibration of the R-band image requires the selection of at least three
stars. A program was written based on haphot that prompts the user to select
4. Data Reduction and Measurement
39
Figure 4.3: Annuli drawn by the script haphot on small and large galaxies. The annuli
are drawn in different colors for easier identification.
stars. The program uses imexamine to capture the locations and then uses phot
with a fixed aperture and background annulus to determine their magnitudes. The
program then uses the IRAF task txdump to select the RA and Dec as well as the
instrumental magnitudes of the selected stars. The results are added to a plaintext
file, one for each run’s worth of observations. The resulting file is designed to be
uploaded to the SDSS database which locates the brightest star nearest each of
the given coordinates and returns its apparent magnitude. This file includes the
galaxy from which the R-band stars came, the instrumental magnitudes measured
in the original image, and the calibrated apparent magnitudes from SDSS.
The steps of the data-reduction and photometry process are tabulated below
in Table 4.1. The first column shows the steps using the pipeline and photometry
software developed by the author, the second column shows the individual steps
that make up the process, and the third column shows the scripts required before
the pipeline and photometry software were written. These scripts are still run from
4. Data Reduction and Measurement
40
within the pipeline, but previously, each task was run individually with separate
input and parameter settings.
Table 4.1: The steps of data-reduction and photometry
Pipeline
Bias Subtraction
Flat Field
CR Clean
pipeline+equalize
haphot
Individual Steps
Separate Scripts
Bias Subtraction
Flat Field
CR Clean
lacos im
Calculate Offsets
getshfts
Align Images
doalign
Astrometry
getrot
Measure FWHM
findfwhm
Smooth Images
rgauss
Scale Images
getscale
Combine Images
imcombine
Determine Aperture
Perform Photometry phot
Move Resulting Files mv
Chapter 5
From Magnitude to SFRD
From the software introduced in the previous chapter, the brightness of ALFALFA
Hα galaxies has been measured in terms of instrumental magnitudes. From there,
the number of galaxian photons striking the CCD must be calibrated using standard star observations. This value is extremely different from the number of
photons emitted by excited hydrogen gas within the galaxy. The SFR can be
calculated only with the estimated value of all photons emitted. To calculate the
number of photons emitted, four factors must be taken into account: Galactic absorption (absorption of light within the Milky Way galaxy), galaxian absorption
(absorption of light within the observed galaxy), the contribution to the narrowband flux from the [NII] emission lines, and the distance to the galaxy.
5.1 Calibrating the Magnitude
While the magnitudes derived from the data-reduction are internally consistent,
they are not calibrated to a standard scientific scale. To calibrate the magnitudes,
the galaxy measurements must be compared with those taken of standard stars.
All ground-based observations are taken through the Earth’s atmosphere. The
distance through the atmosphere varies with the position of the target relative to
the zenith. The atmosphere absorbs a fraction of the light that passes through it,
and the amount of the absorption is proportional to the path length through the
41
5. From Magnitude to SFRD
42
atmosphere. The amount of the atmosphere through which the light must pass
is called the airmass. The magnitude is corrected for airmass by the following
equation:
mλ0 = mλ − kλ sec(z)
(5.1)
In the equation above, mλ0 refers to the extinction-corrected magnitude, mλ to
the observed magnitude, and z to the zenith distance. The airmass, which is equal
to sec(z) for low values of z, is computed by the telescope control software and is
written to the header of each observed image.
The calibrated apparent magnitude of a star in Hα, mHα0 , must also take into
account an offset, or zero-point, ξHα . Combining (5.1) with the zero-point, one
derives the standard Hα magnitude. For broadband images, a color coefficient
must be included to correct for wavelength-dependent differences between the
system used to calibrate the standard stars and the system used by the observer.
The calibration of narrow-band images can exclude this term considering the small
range of wavelengths being covered. Hence, the calibrated Hα magnitude will be:
mHα0 = mHα − kλ sec(z) + ξHα
(5.2)
This equation was applied first to the images of standard stars to determine the
zero-point. Using the known calibrated magnitude, mHα0 , the observed Hα magnitude, mHα , and the airmass, the zero-point can be calculated for each standard
star. A consistent zero-point throughout a night demonstrates clear or photometric conditions.
Using the zero-point for the standard star taken closest to the galaxy image,
the Hα magnitude of each galaxy is computed using equation 5.2.
43
5. From Magnitude to SFRD
5.2 Converting Magnitude to Flux
The R-band filter brackets the wavelengths of redshifted Hα of the ADBS and
ALFALFA targets. Therefore, in using the R-band data as the continuum level,
some of the Hα line is removed. To account for this removal, Lee (2005) derived
the following steps for the magnitude to flux conversion of ADBS data. It was
applied to ADBS data in Webb (2005) and can be similarly applied to ALFALFA
data.
The flux is calculated from the extinction-corrected and standard-star calibrated magnitudes. Hayes & Latham (1975) calibrated the monochromatic fluxes
of Vega. From these calibrated fluxes, Massey et al. (1988) derived an equation
relating magnitude to flux in frequency (erg/s/cm2 /Hz) units:
m = −2.5 log(fν ) − 48.59
(5.3)
Which can be rearranged for flux:
fv = 10−0.4m−19.436
(5.4)
Because the integral of the flux with respect to wavelength (fλ ) has to be the
same as the integral of the flux with respect to frequency (fν ), we can write:
Z
Z
fλ dλ =
fν dν
(5.5)
This is true over any spectral interval. Hence, we can equate the integrands:
fλ dλ = fν dν
(5.6)
44
5. From Magnitude to SFRD
Frequency and wavelength can be equated:
ν=
c
c
, so dν = 2 dλ
λ
λ
(5.7)
In the equation above, c is the speed of light. Rewriting equation 5.6 gives:
fλ =
c
c
fν = 6.960 × 1010 fν
fν =
2
λ
(λ (cm))(λ (Å))
(5.8)
The width of the filter (∆λ ) must be included to account for the standard star.
The Hα line lies well within the width of the narrow-band filters. The width of
the filters would be unnecessary to include except that these equations account
for the the width of the continuum of the standard star in addition to the Hα line
flux. We can define the Hα flux in terms of the monochromatic flux:
Z
λ2
fHα =
fλ dλ = fλ ∆λ
(5.9)
λ1
Combining (5.4), (5.8), and (5.9) gives the final equation
fλ = ∆λ10−0.4m−8.593
(5.10)
Using (5.10), the equations used in the script for the two filters are tabulated
in Table 5.1.
Table 5.1: Magnitude to flux conversion for the two narrow-band filters used by ALFALFA Hα
Filter
∆λ (Å) fHα
KP1564 74
10−0.4m−6.724
KP1565 68
10−0.4m−6.760
The Hα fluxes for the ADBS data are listed in Table 5.2. The galaxy names
45
5. From Magnitude to SFRD
are based on the location of the object. The observing run during which the target
galaxy was observed is shown as a coded number in the second column. The first
digit corresponds to the run (6 = October of 2004, 7 = April of 2005, and 8 =
November of 2005), while the second number indicates the night of the run. The
filter used is listed next, where Kitt Peak Hα filter 1, 2, or 3 corresponds to filters
KP1563, KP1564 and KP1565, respectively. The diameter of the apertures used
to measure the Hα flux is included in arcseconds and the velocity is included
in km/s. The Hα flux, fHα , and its error, σf , are given as multiples of 10−14
erg/s/cm2 .
Table 5.2: Uncorrected Hα fluxes for ADBS galaxies
Galaxy Name
Run
Filter
000407+2234
000623+2347
000900+2348
002249+2310
002526+2136
003751+0838
003811+2523
004649+2134
011440+2708
014206+1235
014246+1309
014527+2531
014729+2719
014847+1034
015011+2309
015105+1235
015434+2312
015906+2523
020022+2434
020148+2632
020320+1837
66
67
81
66
82
67
81
62
82
61
83
65
83
81
61
62
62
82
65
81
61
3
3
3
3
3
3
3
3
3
2
1
3
1
3
2
2
3
3
3
3
2
Diameter Velocity
fHα
σf
−14
(arcsec) (km/s) ×10
erg/s/cm2
60.0
4475
5.681
0.298
56.6
4682
8.541
0.186
78.4
4492 18.214
0.202
30.2
4525
0.000
0.000
23.9
4592
0.768
0.122
126.7
5278 23.004
0.878
38.7
5229
3.087
0.282
38.4
5170
1.603
0.130
53.4
3620
4.690
0.391
175.1
3065 50.536
0.605
124.0
807 31.003
0.283
60.1
3845
1.279
0.205
333.0
375 121.451
10.544
86.7
5283
0.740
0.091
45.5
2886
2.847
0.422
80.9
3280
2.601
0.334
34.9
5045
0.121
0.035
83.2
5066
5.765
0.517
88.7
5134 11.171
0.313
60.0
5021 21.802
0.201
70.2
2395
9.313
0.210
Continued on next page
5. From Magnitude to SFRD
Table 5.2 – continued from previous page
Run Filter Diameter Velocity
fHα
σf
−14
(arcsec) (km/s) ×10
erg/s/cm2
020320+2345
62
2
43.7
2855
2.562
0.284
020405+2412
83
1
118.6
641 12.207
0.260
020918+2534
82
3
90.0
4942
9.330
0.173
070911+2036
62
3
76.0
5221 121.060
0.631
071225+2342
67
3
47.3
4476 18.109
0.287
071352+1031
83
1
114.0
324 24.683
0.418
071553+1207
61
2
47.1
2148
0.797
0.107
071831+2709
81
3
80.2
5091 10.442
0.383
072507+0931
81
3
70.0
5278
0.000
0.000
072858+2035
82
3
121.1
4470 30.402
0.850
073445+2234
82
3
63.5
4583
5.073
0.330
073533+1131
82
3
60.9
5187
2.755
0.270
081538+2107
82
3
35.6
4165
3.660
0.181
081707+2433
62
2
27.5
2074
0.695
0.222
081726+2110
83
2
120.3
2157 18.845
0.587
081821+2431
83
2
27.8
2203
4.542
0.107
082551+2807
83
2
100.1
2188
4.351
0.491
100352+1105
73
2
23.4
3298
1.481
0.089
100500+2132
71
3
73.4
3960
4.341
0.209
100508+2207
72
3
38.2
4019
1.747
0.230
100735+1306
75
2
35.7
2704
1.123
0.180
101421+2207
77
2
144.7
1632
5.507
0.459
102922+2605
71
3
143.1
5048 10.347
0.611
103937+2519
72
3
79.0
5202 33.504
0.406
104208+2344
76
2
79.7
3484
8.904
0.320
105204+1008
73
2
154.3
2718 26.014
1.968
111032+1932
71
3
22.9
5061
0.212
0.044
112134+2010
72
3
240.6
4335 245.132
1.644
113115+2530
75
2
48.8
2867
6.358
0.182
113119+2306
76
2
129.7
2868
5.075
0.654
113845+2008
73
2
20.9
3105
2.976
0.046
114921+2607
71
3
91.6
3556 76.789
1.151
115004+2628
77
2
142.1
1768 74.182
0.736
115040+2531
77
2
54.8
1810
1.774
0.133
115840+2519
72
3
106.4
4479 18.638
0.311
115906+2428
75
2
61.5
3406
5.165
0.414
120351+2525
76
2
72.3
3234 36.983
0.888
121206+2518
73
2
36.0
2595
2.015
0.115
Continued on next page
Galaxy Name
46
47
5. From Magnitude to SFRD
Table 5.2 – continued from previous
Run Filter Diameter Velocity
(arcsec) (km/s)
121233+1207
75
2
155.4
2207
121437+1205
76
2
25.2
2155
124930+2528
71
3
136.2
4381
125156+1205
77
2
158.3
1781
125850+1308
77
2
67.5
1910
131051+1128
73
2
96.0
3368
135822+2533
75
2
55.8
2610
141453+1407
71
3
117.9
4938
142335+2131
75
2
0.0
2049
143307+1030
73
2
78.3
2175
143523+0930
76
2
50.1
2054
144842+1226
77
2
108.2
1765
145050+2519
73
3
50.0
4161
145647+0930
73
2
71.7
3030
153438+1510
77
2
122.6
1835
153518+1203
77
2
137.4
1825
153703+2009
76
2
42.0
3070
223744+2347
61
2
231.0
1336
225557+2610
62
2
22.4
2664
230433+2709
83
1
71.4
1082
231941+1011
63
3
116.1
3558
234042+2613
83
1
109.6
742
234734+1836
65
3
48.8
4285
Galaxy Name
page
fHα
σf
−14
×10
erg/s/cm2
12.738
0.951
0.108
0.046
40.888
0.878
118.440
1.220
9.863
0.313
24.121
0.523
20.408
0.229
13.155
0.599
0.000
0.000
72.661
0.428
0.515
0.205
30.939
1.037
1.404
0.109
5.269
0.447
312.896
2.938
48.663
2.023
1.094
0.073
19.561
0.459
1.376
0.111
6.601
1.684
17.286
1.152
37.428
10.723
0.699
0.125
The Hα fluxes for the spring sample of ALFALFA Hα data are listed in Table
5.3. The galaxy names are from the Arecibo Galactic Catalog (AGC). Fourdigit names correspond to the Uppsala Galactic Catalog (UGC). The observing
run during which the target galaxy was observed is shown as a coded number
in the second column. The first digit corresponds to the run (1 = March of
2006, 3 = February of 2007, and 4 = May of 2007), while the second number
indicates the night of the run. The filter is described as Kitt Peak Hα filter 2 or
3, corresponding to filters KP1564 and KP1565, respectively. The four columns
of diameter, velocity, fHα , and σf are in the same units as Table 5.2.
48
5. From Magnitude to SFRD
Table 5.3: Uncorrected Hα flux for galaxies
Galaxy Name
AGC215158
AGC220292
AGC225879
AGC243857
AGC010108
AGC010218
AGC240088
AGC253921
AGC262404
AGC182483
AGC190161
AGC202483
AGC215135
AGC220201
AGC225875
AGC242316
AGC004845
AGC205072
AGC200496
AGC213336
AGC220242
AGC223205
AGC008114
AGC253922
AGC004712
AGC005409
AGC200581
AGC224241
AGC220478
AGC007817
AGC233574
AGC253923
AGC009915
AGC004732
AGC202297
AGC005897
AGC223247
Run
Filter
12
12
12
12
12
12
13
13
13
14
14
14
14
14
14
14
15
15
15
15
15
15
15
15
16
16
16
16
16
16
16
16
16
17
17
17
17
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
Diameter Velocity
fHα
σf
(arcsec) (km/s) ×10−14 erg/s/cm2
26.0
3559
0.882
0.119
42.0
4992
5.257
0.233
16.0
4087
0.461
0.064
26.0
4832
5.522
0.156
72.0
4687 23.185
0.595
66.0
4872 21.475
0.588
48.0
4778 10.073
0.241
0.0
4648
0.000
0.001
18.0
4730
0.892
0.071
18.0
3302
5.723
0.062
46.0
5269
1.913
0.166
26.0
3499
0.470
0.082
0.0
3780
0.000
0.001
52.0
4528
3.806
0.169
0.0
3548
0.000
0.001
0.0
5179
0.000
0.001
104.0
2116 21.568
1.132
24.0
2962
0.449
0.231
36.0
2872 11.624
0.257
32.0
3066 12.778
0.259
60.0
2087
2.386
0.657
32.0
1788
2.327
0.394
82.0
1990 17.744
1.046
0.0
2969
0.000
0.001
46.0
2010
7.252
0.521
60.0
2988 13.844
0.561
56.0
2794 17.499
0.467
18.0
1636
0.547
0.135
18.0
1888
0.673
0.152
22.0
2740
2.239
0.212
0.0
1921
0.000
0.001
18.0
2646
0.883
0.129
192.0
1828 118.266
3.268
48.0
2070 62.473
0.575
32.0
1544
2.421
0.439
140.0
2773 66.415
2.019
40.0
1819
1.137
0.332
Continued on next page
5. From Magnitude to SFRD
Table 5.3 – continued from previous page
Run Filter Diameter Velocity
fHα
σf
−14
(arcsec) (km/s) ×10
erg/s/cm2
AGC262396
17
2
20.0
2640
0.433
0.144
AGC009908
17
2
120.0
1901 89.143
1.232
AGC205073
10
2
0.0
3061
0.000
0.001
AGC005454
32
2
70.0
2790 10.170
0.190
AGC005964
32
2
12.0
3072
0.000
0.001
AGC200496
32
2
26.0
2859
6.958
0.112
AGC211006
32
2
50.0
1479 34.384
0.272
AGC220336
32
2
34.0
1566
2.569
0.162
AGC005271
33
1
150.0
1438 153.959
9.990
AGC200543
33
1
26.0
1256
3.360
0.232
AGC200598
33
1
24.0
1321 20.778
1.346
AGC192137
34
2
12.0
1621
0.439
0.061
AGC005832
35
1
50.0
1217 29.266
0.217
AGC008091
35
1
74.0
213 39.642
0.360
AGC210459
35
1
40.0
1195
9.055
0.170
AGC210968
35
1
40.0
1448
2.832
0.079
HI1049+1347
35
1
0.0
1322
0.000
0.001
HI1037+1228
36
2
22.0
2831
3.262
0.125
HI1056+1452
36
2
6.0
3139
0.336
0.022
AGC005758
36
2
20.0
2957
1.267
0.066
AGC007476
36
2
60.0
2316 42.378
0.360
AGC202043
36
2
40.0
2739
1.207
0.209
AGC005633
37
1
60.0
1382
3.359
0.449
AGC005889
37
1
90.0
573
4.614
0.727
AGC202244
37
1
30.0
1288
3.082
0.216
AGC212837
37
1
40.0
880
1.328
0.212
AGC220739
37
1
10.0
907
0.214
0.046
AGC222046
37
1
32.0
931 27.371
0.225
AGC220977
37
1
0.0
925
0.000
0.001
AGC007520
41
2
110.0
2515 277.980
1.717
AGC009941
41
2
100.0
1861
2.790
0.601
AGC009943
41
2
150.0
1955 152.459
1.295
AGC220074
41
2
18.0
1512
2.287
0.056
AGC244562
41
2
18.0
1924
0.964
0.073
AGC006653
42
3
56.0
3219 23.003
0.271
AGC007192
42
3
70.0
4017 11.674
0.310
AGC009002
42
3
90.0
4091 23.307
0.423
AGC009867
42
3
74.0
4012 146.821
1.412
Continued on next page
Galaxy Name
49
5. From Magnitude to SFRD
Table 5.3 – continued from previous page
Run Filter Diameter Velocity
fHα
σf
−14
(arcsec) (km/s) ×10
erg/s/cm2
AGC010176
42
3
60.0
4626
4.421
0.232
AGC010384
42
3
72.0
4967 27.060
0.429
AGC233633
42
3
4.0
4404
0.045
0.012
AGC006634
43
3
70.0
3293 84.612
0.499
AGC225846
43
3
30.0
4153
1.289
0.095
AGC009005
44
3
50.0
5239
1.469
0.144
AGC009901
44
3
70.0
3163
6.291
0.128
AGC010387
44
3
40.0
4920
3.905
0.107
AGC233654
44
3
26.0
4518
1.636
0.063
AGC260232
45
3
44.0
3276
4.587
0.148
AGC260615
45
3
50.0
5138
2.153
0.157
AGC006627
46
3
50.0
3547
2.153
0.200
AGC260449
46
3
30.0
4991
1.509
0.085
AGC261620
46
3
28.0
4848 14.198
0.187
AGC262397
46
3
0.0
4954
0.000
0.000
AGC009330
47
3
50.0
5128 23.807
0.229
AGC009333
47
3
46.0
5217 43.497
0.269
AGC009919
47
3
80.0
3185 14.525
0.350
AGC010363
47
3
82.0
4962 19.798
0.351
AGC242319
47
3
30.0
5245
0.954
0.132
AGC260281
47
3
40.0
4838
2.623
0.119
AGC233615
47
3
0.0
3474
0.000
0.000
AGC233617
47
3
0.0
3470
0.000
0.000
AGC009092
48
3
50.0
4275 36.003
0.332
AGC010051
48
3
30.0
4394 20.588
0.148
HI1550+1229
48
3
12.0
4394
0.439
0.029
AGC230859
48
3
40.0
4497
8.261
0.166
AGC233714
48
3
20.0
4384
0.987
0.069
AGC240459
48
3
40.0
5235
1.975
0.191
AGC242321
48
3
28.0
4622
2.177
0.137
HI1405+1214
48
3
12.0
5263
1.919
0.066
AGC242351
48
3
26.0
5024
1.028
0.106
AGC251308
48
3
52.0
4481
9.261
0.203
Galaxy Name
50
5. From Magnitude to SFRD
51
5.3 Flux Corrections
The values of the Hα fluxes tabulated above require a series of corrections before
they can be used to compute the Hα luminosity and SFR for each galaxy. The
measured value is decreased by the absorption and scattering of light by gas and
dust in the light’s path. It is also increased by the presence of the flux from [NII]
lines positioned adjacent to the Hα line. The width of the narrow-band filter is
large enough that all three lines are detected as one. The relevant corrections are
described below.
5.3.1
Galactic Absorption
The gravitational well in which the Milky Way lies includes not only stars, but
also gas and dust. Although the gas and dust are widely scattered, the path length
through the Milky Way is sufficiently long for some of the light to interact with
the gas and dust. Small grains of dust absorb photons and re-radiate the light in
the IR. In addition, the dust grains scatter photons.
Absorption due to dust in the Galaxy artificially decreases the derived SFRs for
galaxies. This absorption is dependent on wavelength as the ratio of wavelength
to dust grain size affects absorption and scattering. Absorption is defined by the
following equation (Osterbrock 1989):
Iλ = Iλ0 e−τλ
(5.11)
Iλ is the intensity of the light observed, Iλ0 is the unabsorbed or intrinsic intensity,
and τλ is the optical depth in the observed direction. Optical depth is a measure of
the opacity of a medium and has been calculated for the Milky Way in a number of
5. From Magnitude to SFRD
52
different ways. One method, for example, is to compare stars of identical spectral
classes. Spectral classes can be determined independent of reddening; they are an
observable attribute of stars. The absolute magnitude of stars of a given spectral
class is constant and therefore observed magnitudes of the same stellar classes are
dependent only on the relative distances and relative absorption. By comparing
distant stars (higher absorption) to the nearest stars (low absorption) of the same
spectral type, the opacity can be determined.
Emission-line ratios can also be used to estimate the amount of absorption.
The intrinsic, unabsorbed ratio of emission line fluxes will be related to the ratio
of observed, or apparent, emission line fluxes, via:
Iλ 0
Iλ1
= 1 eτλ1 −τλ2
Iλ2
Iλ2 0
(5.12)
It is common practice to specify this ratio relative to a specific emission line. The
hydrogen recombination lines are particularly good, so Hβ is most often selected.
We will also convert to base ten logarithms:
Iλ0 0.434(τλ −τHβ )
Iλ
=
10
IHβ
IHβ0
And manipulate the equation to get:
Iλ0 −cHβ [f (λ)−f (Hβ)]
Iλ
=
10
IHβ
IHβ0
(5.13)
In equation 5.13, common practice has been followed, whereby τλ has been replaced with cHβ f (λ). Here f (λ) is a parameterization of the wavelength dependence of the dust absorption. It is a commonly adopted assumption that f (λ) is
constant for all sight lines. The parameter cHβ measures the magnitude of the
5. From Magnitude to SFRD
53
absorption and varies between sight lines.
The ratio of equation 5.13 with λ = ∞ (where the absorption is zero and
Iλ = Iλ0 ) to equation 5.13 with λ = Hα leads to an equation without the Hβ
term:
fHα0 = fHα 10cHβ [f (Hα)−f (∞)]
(5.14)
In the equation above, fHα0 is the corrected, unabsorbed flux and fHα is the
observed flux. The absorption parameter, cHβ , has been tabulated by multiple
groups in the form of the absorption coefficient, AB . The absorption coefficient,
AB , is related to cHβ by the equation cHβ = AB /2.9. Rosenberg & Schneider
(2000) catalogued the absorption coefficients for the initial ADBS survey and
all-sky dust maps have been created by multiple groups.
A modern convenient measurement of Galactic absorption has been performed
by Schlegel et al. (1998). As described above, dust absorbs photons and re-radiates
the energy in the form of heat, or IR photons. Using IR imaging, then, the levels
of dust can be observed in IR data such as those of the Infrared Astronomy
Satellite (IRAS). In addition to the dust, many stars are observed. Schlegel et al.
(1998) removed stars from the IRAS sky maps and used IR temperature and the
position of the dust emission to determine the column density of dust. Finally,
the column density of dust was converted into the level of reddening (E(B-V)).
Values are tabulated in an easily accessible file using code from Schlegel et al.
(1998). Using calibrations tabulated in Schlegel et al. (1998), the reddening E(BV) can be converted into the R and B-band absorption coefficients AR and AB ,
respectively.
Using the standard interstellar absorption curve tabulated in Osterbrock (1989),
one can find the value of f (Hα) − f (∞) by taking [f (Hα) − f (Hβ)] − [f (H∞) −
5. From Magnitude to SFRD
54
f (Hβ)] = 0.74. Thus, the final equation is:
fHα0 = fHα 100.74(AB /2.9)
(5.15)
This equation was applied to all of the ADBS and ALFALFA galaxies using the
tabulated E(B-V) and the correction to AB , AB = 4.315E(B − V ).
5.3.2
Galaxian Absorption
In addition to the gas and dust absorbing light in the Milky Way Galaxy, there is
gas and dust absorbing light in the target galaxies. Gas and dust have been found
to relate to galaxy type (James et al., 2004). In particular, absorption values are
smaller in dwarf galaxies and larger in spiral galaxies. Previous calculations have
not always used a variable galaxian absorption value. In Kennicutt’s 1998 paper,
a constant absorption value of AHα = 0.8 − 1.1 magnitudes was used. Kennicutt sampled large, bright galaxies and did not have the range of morphologies
appearing in the ADBS or ALFALFA Hα projects. Therefore, the corrections for
the ADBS and ALFALFA Hα projects take into account galaxy morphology using
broadband magnitude.
The ratio of the strengths of the hydrogen Balmer Hα and Hβ lines can be
used to measure the absorption within a galaxy. Using equation 5.13, the ratio of
the intensities of the Hα line to the Hβ line can be calculated. Setting λ = Hα
allows one to determine cHβ .
IHα
IHα0 −cHβ [f (Hα)−f (Hβ)]
=
10
IHβ
IHβ0
From quantum mechanics, IHα0 /IHβ0 is known. For temperatures and densities
5. From Magnitude to SFRD
55
Figure 5.1: The linear least squares fit of the KISS galaxies to determine the absorption
coefficient, cHβ , from the absolute B-band magnitude
typically found in nebulae, this ratio has a value of 2.86 (Hummer & Storey 1987).
f (Hα) − f (Hβ) can be tabulated from absorption curves (Osterbrock 1989).
In practice, one would need to obtain a spectrum of each object of interest and
use the observed values of Hα and Hβ to measure cHβ for the galaxy. However,
spectra for all of the ADBS and ALFALFA Hα do not currently exist. Luckily,
cHβ is correlated with absolute magnitude.
This correlation between cHβ and absolute magnitude was worked out by John
Salzer for B-band magnitudes for the ADBS survey and for R-band magnitudes
for ALFALFA Hα (see Webb (2005) for ADBS derivation). This calculation was
based on a subset of galaxies from the KISS project (Salzer et al. 2000). The
subset of KISS galaxies used for these correlations fit three criteria: star forming,
low redshift (z < 0.095), and high quality spectral data.
For ADBS, the absorption constant (cHβ ) was plotted as a function of abso-
5. From Magnitude to SFRD
56
Figure 5.2: The linear least squares fit of the KISS galaxies to determine the absorption
coefficient, cHβ , from the absolute R-band magnitude
lute B-band magnitude and the points were fit using linear least squares fits (see
Figure 5.1). For low absolute magnitudes, cHβ was found to be small and roughly
constant. The linear least squares fit was applied for galaxies with absolute Bband magnitudes brighter than -17.25. From this best fit line, the absorption can
be determined using only the absolute B magnitude. While there is much scatter
in the value of cHβ at any given value of MB , the correction derived using this
method should be correct on average, and is suitable for a large statistical study
of this type.
The resulting cHβ derived from the KISS spectra for ADBS is described below
57
5. From Magnitude to SFRD
as calculated for the B-band:
cHβ = −0.40MB − 6.80 MB < −17.25
(5.16)
cHβ = 0.10
MB > −17.25
The KISS galaxies have only B and V-band magnitudes associated with them.
However, for the ALFALFA galaxies we only have the R band magntides. The
Century Survey (CS) had roughly 1200 galaxies that overlapped with the KISS
survey (Geller et al. 1997). Using the R-band magnitudes from CS and B-band
and V-band magnitudes from KISS, a conversion between V plus B-V and R
magnitudes was generated. Using this conversion and the distances to the KISS
galaxies, the apparent and absolute R-band magnitudes were computed. The
absorption constant was plotted as a function of absolute R-band magnitude and
fit similarly (see Figure 5.2).
The resulting cHβ derived from the KISS spectra for ALFALFA was calculated
for the R-band:
cHβ = −0.226MR − 3.924 MR < −18.0
(5.17)
cHβ = 0.15
5.3.3
MR > −18.0
[NII] Correction
As described above, the [NII] lines may lie within the narrow-band Hα filter.
The rest wavelength of Hα is 6563 Å while the rest wavelengths of the [NII] lines
are 6548 and 6584 Å. To determine the accurate flux from the galaxy, the [NII]
contamination must be removed (James et al., 2004).
Just as Hβ was related to broadband luminosity, so is the [NII] contamination. Previous studies such as Kennicutt (1983) and Kennicutt & Kent (1983)
5. From Magnitude to SFRD
58
Figure 5.3: The linear least squares fit of the KISS galaxies to determine the
log([N II]/Hα) ratio from the absolute B-band magnitude
have determined [NII]-broadband correlations. The sample from which the correlations were based included only spiral and irregular galaxies, not the range of
morphologies in the ADBS and ALFALFA samples. Because the samples were
not equivalent, the [NII]-broadband correlations were re-derived using the KISS
subset described above.
The [NII] contamination is best described by the [NII]/Hα ratio. As described
above, spectral data do not exist for the ADBS and ALFALFA galaxies. Therefore
the [NII] contamination ratio is unknown. However, the [NII] to Hα ratio is found
to be correlated with galaxian luminosity (due to metallicity effects). Thus, for
both samples, the log([N II]/Hα) ratio was plotted as a function of the absolute
broadband magnitude to derive this correlation.
For the ADBS sample, the relationship between log([N II]/Hα) and MB was
plotted in Figure 5.3. A linear least squares fit was performed that resulted in the
5. From Magnitude to SFRD
59
Figure 5.4: The linear least squares fit of the KISS galaxies to determine the
log([N II]/Hα) ratio from the absolute R-band magnitude
following relationship:
log([N II]/Hα) = −0.322MB − 6.686
(5.18)
This function was applied to all of the ADBS data tabulated below to correct
the observed Hα fluxes for [NII] contamination.
Similarly, for ALFALFA Hα the log([N II]/Hα) ratio was plotted as a function
of the absolute R-band magnitude, MR (see Figure 5.4) and a linear least squares
fit was performed:
log([N II]/Hα) = −0.272MR − 6.011
(5.19)
5. From Magnitude to SFRD
60
5.4 Accounting for Distance
From the magnitudes derived by the IRAF scripts described in Chapter 4, the
values are corrected for airmass and are standardized, corrected for Galactic absorption, corrected for galaxian absorption, and have the [NII] lines accounted
for and removed. The result is the corrected, calibrated flux of the galaxy. To
determine parameters of the galaxy such as SFR, the flux must be converted to
the luminosity of the galaxy by accounting for distance.
To account for distance, the observed flux of the galaxy is assumed to be
isotropic: the same in all directions. Therefore, the flux is multiplied by the
surface area of the sphere defined by the distance from the target galaxy to the
sun:
LHα = FHα 4πd2
(5.20)
Hα luminosities are tabulated for all ADBS and spring ALFALFA galaxies in
Tables 5.4 and 5.5.
5.5 The Star-Formation Rate
As described in Chapter 2, Kennicutt (1998) has developed a prescription for
computing the SFR from the Hα luminosity. His relationship is given by:
SF R(M /year) = 7.9 × 10−42 L(Hα) (ergs/s)
The SFRs for the 82 observed galaxies of the ADBS sample are tabulated
below in Table 5.4. Three of the galaxies have zero star formation. This result
is valid because HI, although necessary for star formation, is not an indicator for
61
5. From Magnitude to SFRD
it. Some HI-detected galaxies may have sparse hydrogen clouds not dense enough
for the formation of stars.
In Table 5.4, the distance is given in terms of Mpc, the luminosity of Hα is
given as a logarithm in terms of erg/s, and the SFR is given as a logarithm in
terms of solar masses created per year.
Table 5.4: SFRs for ADBS galaxies
Galaxy Name
RA
Dec
000407+2234
000623+2347
000900+2348
002526+2136
003751+0838
003811+2523
004649+2134
011440+2708
014206+1235
014246+1309
014527+2531
014729+2719
014847+1034
015011+2309
015105+1235
015434+2312
015906+2523
020022+2434
020148+2632
020320+1837
020320+2345
020405+2412
020918+2534
070911+2036
071225+2342
071352+1031
071553+1207
071831+2709
00:04:14.8
00:06:22.8
00:08:54.8
00:25:26.9
00:37:57.8
00:38:11.6
00:46:55.9
01:14:45.7
01:42:09.7
01:42:48.4
01:45:32.5
01:47:31.4
01:48:52.5
01:50:13.1
01:51:03.4
01:54:34.4
01:59:09.6
02:00:23.5
02:01:46.5
02:03:20.3
02:03:20.9
02:04:05.3
02:09:14.3
07:09:18.2
07:12:25.2
07:13:51.8
07:15:52.6
07:18:31.9
22:35:16
23:47:18
23:49:00
21:36:13
08:38:05
25:23:45
21:35:17
27:08:09
12:36:07
13:09:20
25:31:15
27:19:37
10:35:23
23:09:28
12:35:30
23:12:17
25:23:09
24:34:48
26:32:47
18:37:46
23:45:38
24:12:28
25:34:14
20:38:08
23:42:56
10:31:16
12:06:54
27:09:29
Distance log(LHα ) log(SF R)
(Mpc)
(erg/s)
(M /yr)
62.622
40.838
-0.264
65.402
41.142
0.040
62.854
41.468
0.366
63.970
39.727
-1.375
72.463
41.677
0.575
72.496
40.483
-0.619
71.506
40.167
-0.935
50.791
40.370
-0.732
42.523
41.507
0.405
12.435
39.877
-1.225
53.452
40.043
-1.059
7.233
40.169
-0.933
71.917
40.200
-0.902
40.509
39.960
-1.142
45.291
40.062
-1.040
69.254
39.067
-2.035
69.580
41.080
-0.022
70.445
41.421
0.319
69.004
41.580
0.478
33.632
40.467
-0.635
39.989
39.870
-1.232
10.490
39.395
-1.707
67.835
41.211
0.109
68.526
42.386
1.284
58.771
41.317
0.215
2.591
38.514
-2.588
26.994
39.049
-2.053
67.164
41.168
0.066
Continued on next page
5. From Magnitude to SFRD
Galaxy Name
072858+2035
073445+2234
073533+1131
081538+2107
081707+2433
081726+2110
081821+2431
082551+2807
100352+1105
100500+2132
100508+2207
100735+1306
101421+2207
102922+2605
103937+2519
104208+2344
105204+1008
111032+1932
112134+2010
113115+2530
113119+2306
113845+2008
114921+2607
115004+2628
115040+2531
115840+2519
115906+2428
120351+2525
121206+2518
121233+1207
121437+1205
124930+2528
125156+1205
125850+1308
131051+1128
135822+2533
141453+1407
143307+1030
Table 5.4 – continued from previous page
RA
Dec
Distance log(LHα ) log(SFR)
(Mpc)
(erg/s)
(M /yr)
07:28:54.3 20:35:26
58.415
41.527
0.425
07:34:49.2 22:34:35
60.029
40.692
-0.410
07:35:38.2 11:31:22
67.391
40.575
-0.527
08:15:44.0 21:07:52
54.209
40.201
-0.901
08:17:08.0 24:33:44
26.546
38.906
-2.196
08:17:26.4 21:10:26
27.429
40.345
-0.757
08:18:19.8 24:31:36
28.260
39.743
-1.359
08:25:47.7 28:07:04
28.286
39.720
-1.382
10:03:51.9 11:05:59
42.016
39.620
-1.482
10:04:59.2 21:32:17
51.502
40.292
-0.810
10:05:08.3 22:07:12
52.320
39.869
-1.233
10:07:33.3 13:06:23
34.221
39.320
-1.782
10:14:21.8 22:07:28
20.512
39.538
-1.564
10:29:16.9 26:05:57
66.374
41.160
0.058
10:39:38.9 25:19:21
68.411
41.697
0.595
10:42:11.1 23:44:48
45.417
40.658
-0.444
10:52:04.0 10:08:52
34.406
40.977
-0.125
11:10:37.5 19:32:18
66.305
39.140
-1.962
11:21:43.2 20:10:08
56.727
42.462
1.360
11:31:22.1 25:30:03
37.549
40.133
-0.969
11:31:22.6 23:06:55
37.414
40.069
-1.033
11:38:45.3 20:08:35
40.427
39.854
-1.248
11:49:18.7 26:07:17
46.895
41.707
0.605
11:50:04.5 26:28:45
23.069
40.945
-0.157
11:50:39.9 25:31:34
23.574
39.179
-1.923
11:58:40.7 25:18:59
59.208
41.246
0.144
11:59:06.2 24:28:20
44.848
40.200
-0.902
12:03:53.8 25:26:00
42.648
41.252
0.150
12:12:06.7 25:18:34
34.178
39.549
-1.553
12:12:32.4 12:07:24
17.000
39.906
-1.196
12:14:32.9 12:06:10
17.000
37.698
-3.404
12:49:34.3 25:28:11
58.312
41.652
0.550
12:51:55.3 12:04:58
17.000
40.834
-0.268
12:58:52.9 13:09:08
17.000
39.660
-1.442
13:10:56.6 11:28:37
44.193
41.095
-0.007
13:58:23.8 25:33:00
35.336
40.616
-0.486
14:14:52.1 14:07:33
65.938
41.255
0.153
14:33:09.3 10:30:38
29.112
41.192
0.090
Continued on next page
62
63
5. From Magnitude to SFRD
Galaxy Name
143523+0930
144842+1226
145050+2519
145647+0930
153438+1510
153518+1203
153703+2009
223744+2347
225557+2610
230433+2709
231941+1011
234042+2613
234734+1836
Table 5.4 – continued from previous page
RA
Dec
Distance log(LHα )
(Mpc)
(erg/s)
14:35:23.4 09:30:05
27.463
38.792
14:48:42.6 12:27:25
23.914
40.577
14:50:50.3 25:19:26
56.549
39.829
14:56:47.9 09:30:32
40.719
40.229
15:34:38.9 15:10:16
25.510
41.776
15:35:23.3 12:02:50
25.228
40.969
15:37:08.4 20:08:45
42.237
39.522
22:37:46.9 23:47:11
21.286
40.356
22:55:58.7 26:10:06
38.983
39.625
23:04:33.9 27:09:22
17.877
39.562
23:19:41.5 10:11:05
50.201
41.177
23:40:40.1 26:14:11
13.120
40.098
23:47:41.6 18:35:58
60.053
39.816
log(SFR)
(M /yr)
-2.310
-0.525
-1.273
-0.873
0.674
-0.133
-1.580
-0.746
-1.477
-1.540
0.075
-1.004
-1.286
The corrections described above were also applied to the spring ALFALFA Hα
data, a total of 94 galaxies. The results are tabulated below. The columns are
the same as those of Table 5.4.
Table 5.5: SFRs for ALFALFA galaxies
Galaxy Name
AGC205073
AGC010108
AGC010218
AGC220292
AGC225879
AGC220201
AGC242316
AGC200496
AGC205072
AGC253922
AGC004845
RA
Dec
10:05:04.3
15:57:47.8
16:07:31.0
12:17:11.8
12:58:01.3
12:12:12.3
14:05:09.2
10:37:29.7
10:02:16.4
15:32:07.2
09:12:13.4
10:52:19
12:00:52
10:47:42
12:42:27
12:16:36
11:01:58
12:11:18
12:13:37
12:13:04
12:05:45
09:51:46
Distance log(LHα ) log(SF R)
(Mpc)
(erg/s)
(M /yr)
40.813
0.000
0.000
62.493
41.335
0.233
64.960
41.388
0.286
66.560
40.778
-0.324
54.493
39.376
-1.727
60.373
40.679
-0.424
69.053
0.000
0.000
38.293
40.705
-0.398
39.493
39.645
-1.458
39.587
0.000
0.000
28.213
41.445
0.343
Continued on next page
5. From Magnitude to SFRD
Galaxy Name
AGC008114
AGC243857
AGC240088
AGC253921
AGC262404
AGC182483
AGC190161
AGC225875
AGC200581
AGC213336
AGC220478
AGC224241
AGC233574
AGC253923
AGC004712
AGC005409
AGC007817
AGC009915
AGC223247
AGC262396
AGC004732
AGC005897
AGC009908
AGC005454
AGC005964
AGC200496
AGC211006
AGC220336
AGC005271
AGC200543
AGC200598
AGC192137
AGC005832
AGC008091
AGC210459
AGC210968
AGC005758
AGC007476
Table 5.5 – continued from previous page
RA
Dec
Distance log(LHα ) log(SFR)
(Mpc)
(erg/s)
(M /yr)
13:00:08.3 13:46:49
26.533
40.329
-0.773
14:08:37.1 10:51:01
64.427
40.607
-0.496
14:05:51.6 11:41:56
63.707
40.945
-0.157
15:51:16.2 11:17:23
61.973
0.000
0.000
16:11:10.0 09:56:07
63.067
39.814
-1.288
08:47:35.5 09:59:01
44.027
41.104
0.002
09:16:13.9 09:49:13
70.253
40.978
-0.124
12:36:50.5 12:17:51
47.307
0.000
0.000
10:47:50.8 10:55:29
37.253
40.901
-0.202
11:43:35.3 11:59:09
40.880
40.725
-0.378
12:22:46.6 13:41:20
25.173
38.908
-2.194
12:11:36.3 10:26:35
21.813
38.862
-2.240
13:00:32.6 13:00:31
25.613
0.000
0.000
15:34:11.3 12:11:40
35.280
39.333
-1.770
08:59:15.1 11:10:04
26.800
40.818
-0.285
10:02:49.0 10:52:19
39.840
41.468
0.366
12:38:50.9 13:26:36
36.533
39.825
-1.278
15:35:13.0 11:57:23
24.373
41.290
0.187
12:52:43.1 12:34:15
24.253
39.067
-2.035
15:59:55.6 11:32:44
35.200
38.998
-2.105
09:00:31.2 11:12:24
27.600
41.763
0.661
10:47:11.1 11:12:37
36.973
41.569
0.466
15:34:21.6 11:37:34
25.347
41.157
0.055
10:07:05.9 12:35:03
37.200
40.689
-0.413
10:51:07.3 13:56:45
40.960
0.000
0.000
10:37:08.9 12:11:59
38.120
40.504
-0.598
12:01:37.4 14:00:04
19.720
40.422
-0.680
12:18:34.0 12:40:08
20.880
39.383
-1.719
09:49:45.6 12:45:14
19.173
41.411
0.309
10:43:16.0 13:30:48
16.747
39.421
-1.682
10:49:08.7 12:08:59
17.613
40.297
-0.805
09:30:06.8 12:02:50
21.613
39.206
-1.897
10:42:43.5 13:24:34
16.227
40.303
-0.799
12:58:55.1 14:15:39
2.840
38.764
-2.338
11:34:08.7 13:18:55
15.933
39.732
-1.370
11:59:39.9 13:49:35
19.307
39.351
-1.751
10:36:26.8 13:26:51
39.427
39.689
-1.414
12:23:48.8 12:10:31
30.880
41.027
-0.075
Continued on next page
64
5. From Magnitude to SFRD
Galaxy Name
AGC202043
AGC005633
AGC005889
AGC202244
AGC212837
AGC220739
AGC220977
AGC222046
AGC006653
AGC007520
AGC009941
AGC009943
AGC220074
AGC244562
AGC007192
AGC009867
AGC010176
AGC010384
AGC233633
AGC006634
AGC225846
AGC009005
AGC010387
AGC233654
AGC260232
AGC260615
AGC006627
AGC261620
AGC262397
AGC009330
AGC009333
AGC009919
AGC010363
AGC233615
AGC233617
AGC242319
AGC260281
AGC009092
Table 5.5 – continued from previous page
RA
Dec
Distance log(LHα ) log(SFR)
(Mpc)
(erg/s)
(M /yr)
10:07:12.6 13:05:11
36.520
39.832
-1.270
10:24:41.1 14:51:50
18.427
39.399
-1.704
10:47:13.1 14:08:37
7.640
38.932
-2.170
10:31:43.6 13:54:33
17.173
39.374
-1.728
11:30:50.3 14:14:17
11.733
38.608
-2.495
12:32:15.0 11:45:21
12.093
37.782
-3.321
12:43:55.8 13:03:17
12.333
0.000
0.000
12:03:33.6 16:02:55
12.413
39.903
-1.200
11:41:33.1 16:03:19
42.920
41.110
0.008
12:25:11.6 12:45:25
33.533
41.809
0.707
15:38:16.9 12:52:32
24.813
39.514
-1.588
15:37:56.2 12:09:19
26.067
41.407
0.304
12:04:56.6 14:34:18
20.160
39.274
-1.829
14:34:35.8 13:08:20
25.653
39.056
-2.046
12:12:01.3 12:12:04
53.560
41.008
-0.094
15:30:09.1 12:54:06
53.493
41.903
0.801
16:04:50.2 13:40:00
61.680
40.593
-0.510
16:27:00.7 11:41:51
66.227
41.507
0.404
13:27:44.6 16:04:29
58.720
38.479
-2.624
11:40:06.5 15:22:23
43.907
41.695
0.593
12:22:28.3 14:37:57
55.373
39.840
-1.263
14:05:11.1 13:04:31
69.853
40.115
-0.987
16:26:50.7 13:00:52
65.600
40.704
-0.399
13:37:43.7 15:45:56
60.240
39.990
-1.113
16:06:28.9 11:45:06
43.680
40.201
-0.902
16:29:36.8 11:46:58
68.507
40.367
-0.735
11:39:58.8 13:20:04
47.293
40.029
-1.073
16:26:50.0 11:24:01
64.640
41.188
0.086
16:03:50.1 11:45:43
66.053
0.000
0.000
14:29:57.7 14:03:29
68.373
41.412
0.310
14:29:57.7 14:03:29
69.560
41.658
0.555
15:35:33.7 12:31:55
42.467
40.807
-0.296
16:23:10.8 11:42:57
66.160
41.360
0.258
13:10:50.0 15:28:18
46.320
0.000
0.000
13:11:58.1 15:21:46
46.267
0.000
0.000
14:05:31.0 12:14:37
69.933
39.887
-1.215
16:08:06.0 11:57:20
64.507
40.392
-0.710
14:11:55.4 13:14:09
57.000
41.463
0.360
Continued on next page
65
66
5. From Magnitude to SFRD
Galaxy Name
AGC010051
AGC230859
AGC233714
AGC240459
AGC242321
AGC242351
AGC251308
Table 5.5 – continued from previous page
RA
Dec
Distance log(LHα )
(Mpc)
(erg/s)
15:49:26.9 12:22:36
58.587
41.240
13:56:42.2 14:05:10
59.960
40.785
13:57:12.2 14:02:30
58.453
39.735
14:32:56.2 13:45:27
69.800
40.259
14:05:55.7 12:10:21
61.627
40.137
14:05:09.4 13:18:56
66.987
39.874
15:51:13.0 12:41:21
59.747
40.763
log(SFR)
(M /yr)
0.138
-0.317
-1.367
-0.843
-0.965
-1.228
-0.339
We plot histograms of the Hα luminosities for the two samples in Figure 5.5.
The distribution of Hα luminosities from the two surveys are similar, although the
ALFALFA data have a higher percentage of galaxies with high Hα luminosities.
This effect is likely due to the selection effects of the non-random sample of ALFALFA galaxies. However, the median log(LHα ) luminosities for the two samples
are very similar: the median of log(LHα ) for ADBS is 40.29 erg/s and the median
for ALFALFA is 40.39 erg/s.
5.6 The Star-Formation-Rate Density
The ADBS data represent a complete statistical sample, while the ALFALFA
Hα do not. A preliminary value of the star-formation-rate density (SFRD) may
be calculated from ALFALFA Hα only after roughly 250 more galaxies are observed. ADBS, however, is a completed pilot study in which the methodology for
ALFALFA Hα data-reduction has been tested. It is important to compare the
ADBS data with those of previous studies to consider the possible advantage of
selecting galaxies by the 21-cm line.
5. From Magnitude to SFRD
67
Figure 5.5: Histograms of the luminosities of the ADBS (upper histogram) and ALFALFA galaxies (lower histogram), respectively. The vertical mark at the top of each
graph reflects the median value.
68
5. From Magnitude to SFRD
The calculation of the star-formation-rate density (SFRD) is a simple step
beyond the SFR. The SFRD is the total SFR divided by the volume.
ADBS was performed using the drift-scan technique at Arecibo. The total
survey covered 420 square degrees of which 206.6 were imaged in Hα. The resulting
82 galaxies contained a total of 105.4 M /yr being formed. To calculate the
volume of the survey, only the distance limits (0 and 75 Mpc, respectively) and
the total area covered are necessary. A sphere of the radius of the inner distance
limit, 19 Mpc, is subtracted from that of the outer distance limit, 71 Mpc, to form
the spherical shell in which we observe. Then, this volume is multiplied by the
ratio of the total area covered (A) to the total area of the sky:
A
4
V = π(D3 − d3 )
3
41253
(5.21)
V refers to the total volume, A to the area covered in degrees, D to the far distance
limit, and d to the near distance limit. The volume of the ADBS sample is 7423
Mpc3 .
While the total area is not yet known, the volume of ALFALFA Hα will be
calculated similarly. To find the total area covered by the survey, the number of
grids, or two by two degree fields, is added. The distance limits are 71 Mpc and
19 Mpc, as described above.
From the values of the total SFR and volume derived above, the SFRD can
be calculated by the following equation:
3
SF RD (M /yr/Mpc ) =
P
SF R
V olume
(5.22)
The SFRD for ADBS is 0.0142 M /year/Mpc3 . This value is somewhat lower
5. From Magnitude to SFRD
69
than that of previous studies such as Hanish et al. (2006) which made use of the
HIPASS HI-sample. The SFRD calculated by Hanish et al. (2006) was calculated
to be 0.0158 M /year/Mpc3 when corrected to the same Hubble constant of H0 =
75 km/s/Mpc. The higher SFRD of Hanish et al. (2006) is likely due to the
selection bias of their sample. By sampling galaxies mainly within the Local
Supercluster, they likely have an inflated SFRD due to the high density of galaxies.
The advantage of selecting galaxies beyond the Local Supercluster is clear: density
fluctuations lead to less accurate estimates of the SFRD “now”. This new, more
complete measurement of the SFRD can alter the perception of star formation
activity in the Local Universe.
Chapter 6
Conclusion
6.1 Summary
This thesis has focused on the development of the methodology for data-acquisition
and reduction. In addition, those methods have been applied to both the ADBS
sample of galaxies and have been prepared for the ALFALFA Hα sample of galaxies.
Four scripts were written to facilitate data-processing. Pipeline was written
to streamline the overall processing of the Hα imaging. It was created to pass
the names and outputs of one script to the next to make the process more efficient. In creating all of the image names based off of the galaxy name, the
script also introduces a uniform naming convention. In addition to passing and
creating file names, the stellar coordinate lists are passed between tasks whenever
possible. Equalize was written to perform the time-consuming process of equalizing FWHMs efficiently. The user is only required to select stars for one image
and remove outliers from FWHM calculations, while previously the user calculated a smoothing parameter by hand and selected stars in each image. Haphot
was written to allow easy selection of the photometry aperture for galaxies. It
also moves the resulting magnitude file to the appropriate location for faster processing. Finally, Sdssphot was written to determine the magnitude of stars in
70
6. Conclusion
71
broadband-R fields for calibration with the SDSS database. The output is in an
easy form for entry into the SDSS database, which facilitates the calculation of
an offset between instrumental and calibrated apparent R-band magnitudes. The
time saved by these scripts is likely to total hundreds of hours across the lifetime
of the ALFALFA Hα project. Perhaps more importantly, the use of these scripts
will improve the accuracy with which the work is done and the homogeneity of the
final results. The scripts are also applicable to other projects and may eventually
save more time.
Galaxies from four ALFALFA Hα observing runs were reduced and photometered. In addition, the flux corrections required for the calculation of a SFRD
from ALFALFA Hα were derived. When the sample becomes statistically complete, the SFRD can be rapidly calculated. As an example of the process, the
SFRD of the ADBS sample was calculated using the 82 observed and reduced
ADBS galaxies.
Once completed, the result of this work will be the calculation of the starformation-rate density of the Local Universe with an unprecedentedly low level of
error. This number can be used to understand better the change of the rate of
star formation over time and may increase our understanding of the formation of
galaxies.
6.2 Future Work
The ALFALFA Hα project is now beginning its period of primary data-acquisition.
The methods of data-acquisition and processing have been developed – in part in
this thesis – and approximately 500 more galaxies are now scheduled for observation.
6. Conclusion
72
Within the next year, observations of at least half of the remaining galaxies
are intended (weather permitting), as well as their data-processing. These results
will comprise a statistically complete sample from which a preliminary SFRD will
be calculated.
From a statistical sample of the size of ALFALFA Hα, much more than the
SFRD can be gleaned. Other topics of investigation include the study of the
star-formation modes of galaxies. This galaxy sample includes hundreds of lowsurface-brightness HI-rich galaxies, providing a different perspective of modes of
star formation. This sample will also be used to select sources for follow-up
spectroscopy, particularly of interesting sources, in order to determine nebular
abundances.
Three other ongoing projects are currently using the ALFALFA Hα dataset.
Ed Moran has used it to select large galaxies with extreme star-forming knots
for follow-up observations. It is possible that Low Ionization Nuclear Emission
Regions (LINERs) may be extranuclear, in areas of extremely hot stars. Such stars
may only be found in star-forming regions like many of those found in ALFALFA
Hα.
Ed Moran and Chris Dieck are also using the ALFALFA Hα dataset to search
for active galactic nuclei (AGN) with the lowest-mass black holes powering their
emission. Sources like this may be more easily detected by HI than by optical
surveys and their nuclear emission is bright in Hα. By looking for point-like Hαbright nuclei of ALFALFA galaxies, they may find new, extremely low-mass black
holes.
In addition to Ed Moran’s searches for extranuclear LINERs and small black
holes, Jessica Keller is using the ALFALFA Hα images to detect Hα sources far
from the imaged galaxies, called Hα Dots. The wide field-of-view of each image,
6. Conclusion
73
along with the accurate positions of the galaxies obtained by the data-reduction
pipeline, are ideally suited for Hα dot selection and follow-up spectroscopy.
ADBS is currently an underused survey but may be coupled with the ALFALFA Hα survey for the measurement of the SFRD. Because the two samples
are similar, the data can be merged for many of the projects listed in this section.
Appendix A
equalize.cl
1
2
3
procedure equalize(images)
string
images {"", prompt="At file containing images (Contin
uum First)"}
4
string
rtnm
{"",prompt= "File root name for output files"}
5
bool
update {no, prompt="Add field FWHMPSF to image header
?"}
6
real
radius {4, prompt="Object radius"}
7
real
buffer {5, prompt="Background buffer width"}
8
real
width
{5, prompt="Background width"}
9
real
rplot
{15, prompt="Plotting radius"}
10
struct
*imexlist, *imagelist, *imagelist2
11
12
13
14
15
16
17
18
19
20
21
begin
string image, imagefile, imlist, img, coordfile
real maxfwhm
int counter, frame
#-------------------------------------------------------------string imexfile, rootname, outname, outfile
int i, count, xaxis, yaxis
real a, b, c, d, e, f, g, h, j, k, l, m, fwhm, total, ave,
median
22
real currentfwhm, sig
23
bool display
24
25
26
27
28
29
30
31
32
33
frame = 1
display=yes
rootname=rtnm
imlist = images
imagefile= mktemp("tmp$equalize")
sections(imlist, option="fullname", > imagefile)
imagelist = imagefile
imagelist2 = imagefile
74
A. equalize.cl
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
75
outfile = rootname//"scl.in"
imexfile = rootname//".reg"
coordfile=rootname//".strloc"
imexlist = imexfile
#-------------------------------------------------------------maxfwhm = 0
imlist = images
imagefile= mktemp("tmp$test")
sections(imlist, option="fullname", > imagefile)
imagelist=imagefile
rimexam.radius=radius
rimexam.buffer=buffer
rimexam.width=width
rimexam.rplot=rplot
rimexam.fittype="gaussian"
counter = 0
if (access (outfile)) {
print ("Deleting old version of the outfile")
delete (outfile, ver-)
}
while (fscan(imagelist,image) != EOF) {
i = 1
count = 0
total = 0
img = image
if (access ("gscltemp.fits") || access ("gscltemp.imh")) {
print ("Deleting old version of gscltemp")
imdelete ("gscltemp", ver-)
}
if (access ("gsclout.fits") || access ("gsclout.imh")) {
A. equalize.cl
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
76
print ("Deleting old version of gsclout")
imdelete ("gsclout", ver-)
}
if (access (imexfile)) {
print ("Deleting old version of imexfile")
del(imexfile, ver-)
imexfile = rootname//".reg"
imexlist = imexfile
}
imgets(image=img, param="naxis1")
xaxis = int(imgets.value)
imgets(image=img, param="naxis2")
yaxis = int(imgets.value)
if (counter == 0) {
if (access (coordfile)) {
print ("Deleting old version of coordfile")
del(coordfile, ver-)
}
imstat(image=img//"[200:"//xaxis-200//", "//yaxis/2//":
"//(yaxis/2)+20//"]", fields="midpt", lower=-1000, upper=INDEF,
format=no) | scan(median)
100
display(img, frame=frame, zr-, zs-, z1=median-30,
z2=median+400)
101
102
print ("Mark a series of stars with ’r’, hit ’q’ when d
one.")
103
imexamine(frame=frame, logfile=imexfile, keep+, imagecu
r="", wcs="logical", use_dis=yes)
104
105
106
107
108
page(imexfile)
#
Create image to fit FWHMs.
imcopy("dev$pix[1:50,1:2]", "gscltemp", ver-)
chpixtype("gscltemp", "gscltemp", "real", ver-)
while(fscan(imexlist, a, b, c, d, e, f, g, h, j, k, l,
m, fwhm) != EOF){
A. equalize.cl
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
77
if (i == 3){
total = total + fwhm
count = count + 1
imreplace("gscltemp["//count//",2]", value=fwhm
, lower=INDEF, upper=INDEF)
\end{verbatim}%
\vspace{-.37in}
\begin{listing}[1]{113}
i = 1
print(a, b, "logical 1", >> coordfile)
}
i = i + 1
}
imcopy("gscltemp[1:"//count//",1:2]", "gscltemp", ver-)
sfit("gscltemp", "gsclout", lines=2, type="fit", inter+,
wavescale-,
122
overrid+, logfile="", function="legendre", order=1)
123
listpix("gsclout[1,2]") | scan(x,ave)
124
ave = real(int(ave * 1000)) / 1000
125
126
127
128
129
130
131
if (ave > maxfwhm) {
maxfwhm = ave
}
print("Average FWHM: ",ave)
hedit(images=img, fields="FWHMPSF", value=ave, add=yes,
verify=no, update=yes)
132
imdelete ("gscltemp", ver-)
133
imdelete ("gsclout", ver-)
134
}
135
136
137
138
139
#--------------------------------------------------------------
if (counter > 0) {
imexamine(input=img, image=img, logfile=imexfile, keep+
, imagecur=coordfile, use_dis=no)
140
#
Create image to fit FWHMs.
141
imcopy("dev$pix[1:50,1:2]", "gscltemp", ver-)
142
chpixtype("gscltemp", "gscltemp", "real", ver-)
143
while(fscan(imexlist, a, b, c, d, e, f, g, h, j, k, l,
m, fwhm) != EOF){
A. equalize.cl
78
144
145
146
147
if (i == 3){
total = total + fwhm
count = count + 1
imreplace("gscltemp["//count//",2]", value=fwhm
, lower=INDEF, upper=INDEF)
148
i = 2
149
}
150
151
152
153
154
155
i = i + 1
}
imcopy("gscltemp[1:"//count//",1:2]", "gscltemp", ver-)
sfit("gscltemp", "gsclout", lines=2, type="fit", inter+
, wavescale-,
156
overrid+, logfile="", function="legendre", order=1)
157
listpix("gsclout[1,2]") | scan(x,ave)
158
ave = real(int(ave * 1000)) / 1000
159
160
161
162
163
164
165
if (ave > maxfwhm) {
maxfwhm = ave
}
print("Average FWHM: ",ave)
hedit(images=img, fields="FWHMPSF", value=ave, add=yes,
verify=no, update=yes)
166
imdelete ("gscltemp", ver-)
167
imdelete ("gsclout", ver-)
168
}
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
counter = counter + 1
}
print("Max FWHM: ",maxfwhm)
if (access (imagefile)) {
delete (imagefile, ver-)
}
if (access (imexfile)) {
delete (imexfile, ver-)
}
#-------------------------------------------------------------# COMPARISON SECTION
A. equalize.cl
186
187
188
189
190
191
192
193
194
195
79
while (fscan(imagelist2,image) != EOF) {
img = image
hselect(images=img, fields="FWHMPSF", expr=yes) | scan(curr
entfwhm)
outname = "g"//img
if (currentfwhm < (maxfwhm - .2)) {
sig=((maxfwhm/2.354)**2-(currentfwhm/2.354)**2)**(1/2.)
gauss(input=image, output=outname, sigma=sig, ratio=1.,
theta=0.,nsigma=4.,boundary="nearest",constant=0.)
196
#-------------------------------------------------------------197
i = 1
198
count = 0
199
total = 0
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
img = outname
if (access("gscltemp.fits") || access("gscltemp.imh")){
print ("Deleting old version of gscltemp")
imdelete ("gscltemp", ver-)
}
if (access ("gsclout.fits") || access ("gsclout.imh")){
print ("Deleting old version of gsclout")
imdelete ("gsclout", ver-)
}
if (access (imexfile)) {
print ("Deleting old version of imexfile")
del(imexfile, ver-)
imexfile = rootname//".reg"
imexlist = imexfile
}
imexlist = imexfile
imgets(image=img, param="naxis1")
xaxis = int(imgets.value)
imgets(image=img, param="naxis2")
yaxis = int(imgets.value)
imexamine(input=img, image=img, logfile=imexfile, keep+
, imagecur=coordfile, use_dis=no)
A. equalize.cl
80
226
227
228
229
#
Create image to fit FWHMs.
imcopy("dev$pix[1:50,1:2]", "gscltemp", ver-)
chpixtype("gscltemp", "gscltemp", "real", ver-)
while(fscan(imexlist, a, b, c, d, e, f, g, h, j, k, l,
m, fwhm) != EOF){
230
if (i == 3){
231
total = total + fwhm
232
count = count + 1
233
imreplace("gscltemp["//count//",2]", value=fwhm
, lower=INDEF, upper=INDEF)
234
i = 2
235
}
236
i = i + 1
237
}
238
239
240
imcopy("gscltemp[1:"//count//",1:2]", "gscltemp", ver-)
sfit("gscltemp", "gsclout", lines=2, type="fit", inter+
, wavescale-,
241
overrid+, logfile="", function="legendre", order=1)
242
listpix("gsclout[1,2]") | scan(x,ave)
243
ave = real(int(ave * 1000)) / 1000
244
245
246
247
248
249
250
if (ave > maxfwhm) {
maxfwhm = ave
}
print("Average FWHM: ",ave)
hedit(images=img, fields="FWHMPSF", value=ave, add=yes,
verify=no, update=yes)
251
imdelete ("gscltemp", ver-)
252
imdelete ("gsclout", ver-)
253
#-------------------------------------------------------------254
img = image
255
print("The following image was gaussed: ",img)
256
print(outname, >> outfile)
257
}
258
if (currentfwhm >= (maxfwhm - .2)) {
259
imcopy(input=image, output=outname, verbose=no)
260
print("The following image was not gaussed: ",img)
261
print(outname, >> outfile)
262
}
263
}
264
end
Appendix B
pipeline.cl
1
2
3
4
5
6
7
8
9
10
11
procedure pipeline(images,rootname)
#
#
#
#
#
#
#
Pipeline is a procedure that runs a pipeline on data
reduction in iraf. It is designed particularly for three
images, two on band and one continuum image. Still, it can
accept up to 7 on-band images
Prefixes are hardcoded into the script so that continuity is
kept between the years of John’s data.
string images {"", prompt= "Images to be processed - continuum
image first"}
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string rootname {"", prompt= "File root name for output files"}
struct *imagelist
begin
string image, imagefile, imlist, newimages, sclims
string outname[10], outnamefinal, imcomb, eqfile, nms[10]
string newfile, rootn, allimages, nmfilename
bool alignyes
int i, j, cnt
allimages = images
rootn = rootname
eqfile="@"//rootn//"eq.in"
sclims="@"//rootn//"scl.in"
rimexam.center = yes
#**********************************************************
print(" ")
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print("**************************************************")
print(" ")
print("Step 1: Running image alignment script")
print(" ")
getshfts(images=allimages, rootname=rootn, zscale+, runimal
gn-, ver-)
40
doalign(images=allimages, rootname=rootn, prefix="sh", app, ver+)
41
42
43
print(" ")
print(" Would you like to continue and align the images? Y
es/No")
44
scan(alignyes)
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if (alignyes==yes) {
print(" ")
print(" Shifting Images....")
print(" ")
doalign(images=allimages, rootname=rootn, prefix="sh",
app+, ver-)
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#**********************************************************
print(" ")
print("**********************************************")
print(" ")
print("Step 2: Run Astrometry script on images")
print(" ")
rename(rootn//".com", rootn//"eq.in")
# GETASTROM runs GETCOORDS, then assigns astrometric
# solutions to the remaining input images
getastrom(images=eqfile)
#**********************************************************
print(" ")
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print("**********************************************")
print(" ")
print("Step 3: Measuring FWHMs in each image")
print(" ")
# Equalize will create a new image list for the next
# step and it will be rootname//scl.in
equalize(images=eqfile, rtnm=rootn)
#**********************************************************
#
#
Reorder input file for scaling - need to scale images
#
to first H-alpha image (not R image). Also determine
#
number of images for arithmatic stage, and create
#
output file names.
imlist = allimages
imagefile= mktemp("tmp$pl1")
newfile= mktemp("tmp$pl2")
sections(imlist, option="fullname", > imagefile)
imagelist=imagefile
i=1
while (fscan(imagelist,image) != EOF) {
nms[i]="gsh"//image
i+=1
}
delete (imagefile, ver-)
cnt=i-1
print(nms[2], >> newfile)
print(nms[1], >> newfile)
i=2
while (i < cnt) {
i=i+1
print(nms[i], >> newfile)
}
if (access (rootn//"scl.in")) {
delete (rootn//"scl.in", ver-)
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}
copy(newfile, rootn//"scl.in")
delete (newfile, ver-)
i=1
while (i < cnt) {
if(i == 1) {
outname[i] = rootn//"_HA_1.fits"
}
if(i == 2) {
outname[i] = rootn//"_HA_2.fits"
}
if(i == 3) {
outname[i] = rootn//"_HA_3.fits"
}
if(i == 4) {
outname[i] = rootn//"_HA_4.fits"
}
if(i == 5) {
outname[i] = rootn//"_HA_5.fits"
}
if(i == 6) {
outname[i] = rootn//"_HA_6.fits"
}
if(i == 7) {
outname[i] = rootn//"_HA_7.fits"
}
print(outname[i], >> "imcomb")
i=i+1
}
#**********************************************************
print(" ")
print("**********************************************")
print(" ")
print("Step 4: Scaling images to a common flux scale")
print(" ")
getscale(images=sclims, rootname=rootn, zscale+, app+,
uselocs+, prefix="sc", ver-)
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#**********************************************************
print(" ")
print("**********************************************")
print(" ")
print("Step 5: Creating continuum-subtracted images")
print(" ")
outnamefinal = rootn//"_HA.fits"
i=1
while (i < cnt) {
j=i+1
imarith(operand1="sc"//nms[j], op="-", operand2="sc
"//nms[1], result=outname[i])
171
i=i+1
172
}
173
imcombine(input="@imcomb", output=outnamefinal, combine
="average", reject="none")
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imcopy(nms[1], rootn//"_R.fits", ver-)
display(image=outnamefinal, frame=1)
}
delete ("imcomb", ver-)
delete ("logfile", ver-)
end
Appendix C
haphot.cl
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procedure haphot(image)
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
HAPHOT displays an image, then draws a number of circular
apertures centered on an object that the user selects.
The centering is done by imexamine; make sure the "radius"
parameter in the parameter set "rimexam" is set large enough.
The display parameters ’z1’ and ’z2’ use the median pixel
value determined by imstat and some offsets set by the user.
The number and spacing of the apertures, and the size of the
smallest aperture, are input parameters to the procedure.
Original Code: Arthur Sugden
Modified:
14 September 2007
John Salzer 18 September 2007
Added PHOT parameter settings for sky fitting, etc.
Modified TVMARKs to include center and to not erase
until after a remark.
Turned centering off for IMEXAM
Changed renaming of output mag files to avoid deleting
old versions
string image
real
scale
int
numap
real
deltap
real
smallap
int
frame
bool
fill
string ccdread
ead Noise"}
32
string gain
33
string exposur
34
string airmass
{prompt = "Image to display"}
{0.6, prompt = "Image scale in units per pix"}
{5, prompt = "Number of apertures to draw"}
{5, prompt = "Aperture size increment"}
{20, prompt = "Size of smallest aperture"}
{1, prompt = "Frame to be written into"}
{no, prompt = "Scale image to fit"}
{"rdnoise", prompt = "Header keyword (HK) for R
{"gain", prompt = "HK for gain"}
{"exptime", prompt = "HK for Exposure time"}
{"airmass", prompt = "HK for Airmass"}
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string
string
struct
87
filter {"filters", prompt = "HK for Filter"}
obstime {"ut", prompt = "Header keyword for Time"}
*imexlist, *templist
begin
string img_name, imexfile, temp, continuechoice, masked_ima
ge, magname
42
real xc, yc, midpt, ap, zlow, zhigh, mag, merr
43
int i, j, clr, whichcircle, aperturesize
44
bool starmasking, testing
45
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imexfile = mktemp("tmp$lst1")
imexlist = imexfile
temp = mktemp("tmp$lst2")
templist = temp
img_name = image
masked_image = img_name//"_masked"
if(! access("../magfiles")) {
mkdir(newdir="../magfiles")
}
#**************************************************************
# 1) Check that the image exists
if(!access(img_name)) {
if(!access(img_name//".imh")) {
if(!access(img_name//".fits")) {
print("Image not found.")
print("exiting...")
bye
}
}
}
#**************************************************************
# 2) Get median of image
imstat(images=img_name, fields="midpt", lower=INDEF, upper=
INDEF, binwidt=0.1, format=no) | scan(midpt)
C. haphot.cl
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zlow = midpt - 30
zhigh = zlow + 150
print("z1 = ", zlow, "z2 = ", zhigh)
#**************************************************************
# 3) Display image in Imtool
print("Displaying image ", img_name)
display(image=img_name, frame=frame, fill=fill, zscale=no,
zrange=no, z1=zlow, z2=zhigh, >& "dev$null")
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#**************************************************************
# 4) Get galaxy position
print(" ")
print("Step 1:
Mark the galaxy (hit ’a’, then ’q’).")
rimexam.center = no
imexamine(frame=frame, logfile=imexfile, keeplog=yes, >& "d
ev$null")
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i = 1
while (fscan (imexlist, xc, yc) != EOF) {
if (i == 3) {
print(xc, yc, >> temp)
i = 1
}
i = i+1
}
if (!access(temp))
error(11, "No positions marked.")
#**************************************************************
# 5) Draw concentric circles around galaxy
j = 1
clr = 204
tvmark(frame=frame, coords=temp, logfile="", autolog=no, ou
C. haphot.cl
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timag="", deletio="", command="", mark="plus", txsize=2, color=
clr, label=no, number=no, toleran=1.5, interactive=no)
117
while (j < numap+1)
118
{
119
ap = (smallap/scale)+((j - 1)*(deltap/scale))
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if ((j - 1)%2 == 0) {
clr = 204 + j/2
}
tvmark(frame=frame, coords=temp, logfile="", autolog=no
, outimag="", deletio="", command="", mark="circle", radii=ap,
color=clr, label=no, number=no, toleran=1.5, interactive=no)
j = j+1
}
#**************************************************************
# 5.1) Check for success of measure and retry if necessary
continuechoice = ""
print(" ")
print("_________________________________________________")
while (continuechoice != "c" && continuechoice != "q") {
continuechoice = "c"
print("Step 2: Was the positioning successful? (press
’c’ to continue, ’l’ to relocate, ’s’ to resize inner ring, or
’q’ to quit) {c}")
139
scan(continuechoice)
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if (continuechoice == "l") {
delete(imexfile, ver-)
delete(temp, ver-)
imexfile = "imex.tmp"
imexlist = imexfile
temp = "tmpforha.tmp"
templist = temp
print(" ")
print("Retry: Mark the galaxy (hit ’a’, then ’q’)")
#
display(image=img_name, frame=frame, fill=fill, zsc
ale=no, erase=yes, zrange=no, z1=zlow, z2=zhigh, >& "dev$null")
C. haphot.cl
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imexamine(frame=frame, logfile=imexfile, keeplog=ye
s, >& "dev$null")
i = 1
while (fscan (imexlist, xc, yc) != EOF) {
if (i == 3) {
print(xc, yc, >> temp)
i = 1
}
i = i+1
}
display(image=img_name, frame=frame, fill=fill, zsc
ale=no, erase=yes, zrange=no, z1=zlow, z2=zhigh, >& "dev$null")
if (!access(temp))
error(11, "No positions marked.")
j = 1
clr = 204
tvmark(frame=frame, coords=temp, logfile="", autolo
g=no, outimag="", deletio="", command="", mark="plus", txsize=2
, color=clr, label=no, number=no, toleran=1.5, interactive=no)
while (j < numap+1)
{
ap = (smallap/scale)+((j - 1)*(deltap/scale))
if ((j - 1)%2 == 0) {
clr = 204 + j/2
}
tvmark(frame=frame, coords=temp, logfile="", au
tolog=no, outimag="", deletio="", command="", mark="circle", ra
dii=ap, color=clr, label=no, number=no, toleran=1.5, interactiv
e=no)
j = j+1
}
}
else if (continuechoice == "s") {
print("Retry: The previous smallest annulus was ",
C. haphot.cl
91
smallap, " arcseconds. What is the new desired size for the sma
llest aperture?")
191
scan(smallap)
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display(image=img_name, frame=frame, fill=fill, zsc
ale=no, erase=yes, zrange=no, z1=zlow, z2=zhigh, >& "dev$null")
j = 1
clr = 204
tvmark(frame=frame, coords=temp, logfile="", autolo
g=no, outimag="", deletio="", command="", mark="plus", txsize=2
, color=clr, label=no, number=no, toleran=1.5, interactive=no)
while (j < numap+1)
{
ap = (smallap/scale)+((j - 1)*(deltap/scale))
if ((j - 1)%2 == 0) {
clr = 204 + j/2
}
tvmark(frame=frame, coords=temp, logfile="", au
tolog=no, outimag="", deletio="", command="", mark="circle", ra
dii=ap, color=clr, label=no, number=no, toleran=1.5, interactiv
e=no)
j = j+1
}
}
}
#**************************************************************
# 5.2) Pass the required information to phot
if (continuechoice == "c") {
print("______________________________________________")
print(" ")
whichcircle = 3
print("Step 3: Which circle best represents the total
H-alpha of the galaxy? The circles are numbered out from the ce
nter, beginning with 1 {3}")
C. haphot.cl
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92
scan(whichcircle)
aperturesize = smallap + (whichcircle - 1)*5
datapars.scale = scale
datapars.fwhmpsf = 2.5
datapars.emissio = yes
datapars.sigma = INDEF
datapars.datamin = INDEF
datapars.datamax = 64000.
datapars.noise = "poisson"
datapars.ccdread = ccdread
datapars.gain = gain
datapars.exposur = exposur
datapars.airmass = airmass
datapars.filter = filter
datapars.obstime = obstime
centerpars.calgori = "none"
fitskypars.salgorithm = "median"
fitskypars.annulus = aperturesize
fitskypars.dannulus = 15
photpars.apertur = aperturesize
photpars.zmag = 0.0
print("______________________________________________")
print(" ")
starmasking = no
print("Step 4: Are there any stars within the circle a
round the galaxy? If so, they must be masked. (yes/no) {no}")
253
scan(starmasking)
254
255
256
257
258
259
if (starmasking == yes) {
print(" ")
print("To mask a star within the circle, position y
our cursor over the center of the star and press ’b’. To undo a
mask, press ’u’ immediately. In the unlikely even that the radi
us of 5 is not correct, press ’:r #’ when the cursor is over th
e image (where # is the new radius). Press ’q’ when you are don
e created star mask(s).")
imedit(input=img_name, output=masked_image, display
C. haphot.cl
93
=yes, autodis=yes, autosur=no, apertur="circular", radius=5., s
earch=0., buffer=1., width=2., xorder=2, yorder=2, value=INDEF,
sigma=0., angh=-33., angv=25.)
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img_name = masked_image
}
print("______________________________________________")
print(" ")
print("Step 5: Photometry being carried out with apert
ure size = ",aperturesize,"arcsec.")
#
#
#
if(access(img_name//".mag.1")) {
delete(img_name//".mag.1", ver-)
}
#
#
#
if(access("../magfiles/"//img_name//".mag")) {
delete("../magfiles/"//img_name//".mag", ver-)
}
phot(image=img_name, coords=temp, interactive=no, verif
y=no)
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285
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287
}
#**************************************************************
# 6) Delete temporary files
delete(imexfile, ver-, >& "dev$null")
delete(temp, ver-, >& "dev$null")
#**************************************************************
# 7) Print magnitude and magnitude error to make sure error is
not too great.
288
if (access(img_name//".mag.4")) {
289
magname=img_name//".mag.4"
290
}
291
if (access(img_name//".mag.3")) {
292
magname=img_name//".mag.3"
293
}
C. haphot.cl
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94
else if (access(img_name//".mag.2")) {
magname=img_name//".mag.2"
}
else {
magname=img_name//".mag.1"
}
copy(input=magname, output="../magfiles/.", verbose=no)
txdump(textfile=magname, fields="mag, merr", expr+, headers
-) | scan(mag,merr)
302
print("_________________________________________________")
303
print(" ")
304
print("RESULT: The magnitude found is "//mag//" with an err
or of "//merr)
305
306
end
Appendix D
sdssphot.cl
1
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7
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15
16
17
18
procedure sdssphot(image)
#
#
#
#
#
#
#
#
#
#
SDSSPHOT displays an image, then prompts the user to
select isolated stars around the image. The script
then asks the user if there were any mistakes, and
if not, performs photometry. The resulting magnitudes
are added to a file including all photometry for an
observing run. The script is designed for R-band
photometry for comparison with SDSS
Original Code: Arthur Sugden
29 March 2008
string image
{prompt = "Image to display"}
real
scale
{0.6, prompt = "Image scale in units per pix"}
real
ap
{15, prompt = "Size of the stellar aperture"}
int
frame
{1, prompt = "Frame to be written into"}
string ccdread {"rdnoise", prompt = "Header keyword (HK) for R
ead Noise"}
19
string gain
{"gain", prompt = "HK for gain"}
20
string exposur {"exptime", prompt = "HK for Exposure time"}
21
string airmass {"airmass", prompt = "HK for Airmass"}
22
string filter {"filters", prompt = "HK for Filter"}
23
string obstime {"ut", prompt = "Header keyword for Time"}
24
struct *imexlist, *templist, *magfilelist
25
26
27
28
begin
string img_name, imexfile, temp, magfile, continuechoice, m
agname, imagename, xairmass
29
real xc, yc, midpt, zlow, zhigh, xlocation, ylocation, xloc
[50], yloc[50], maglevel
30
int i, j, clr, whichcircle, aperturesize
95
D. sdssphot.cl
31
32
33
34
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imexfile = mktemp("tmp$lst1")
imexlist = imexfile
temp = mktemp("tmp$lst2")
templist = temp
magfile = mktemp("tmp$lst3")
magfilelist = magfile
img_name = image
#**************************************************************
# 1) Check that the image exists
if(!access(img_name)) {
if(!access(img_name//".imh")) {
if(!access(img_name//".fits")) {
print("Image not found.")
print("exiting...")
bye
}
}
}
#**************************************************************
# 2) Get median of image
imstat(images=img_name, fields="midpt", lower=INDEF, upper=
INDEF, binwidt=0.1, format=no) | scan(midpt)
60
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65
66
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68
69
70
71
zlow = midpt - 30
zhigh = zlow + 150
print("z1 = ", zlow, "z2 = ", zhigh)
#**************************************************************
# 3) Display image in Imtool
print("Displaying image ", img_name)
display(image=img_name, frame=frame, fill-, zscale=no, zran
ge=no, z1=zlow, z2=zhigh, >& "dev$null")
D. sdssphot.cl
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75
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77
78
79
80
97
#**************************************************************
# 4) Get stellar positions
print(" ")
print("Step 1:
Mark solitary stars (hit ’a’, then ’q’).")
rimexam.center = yes
imexamine(frame=frame, logfile=imexfile, keeplog=yes, >& "d
ev$null")
81
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84
i = 1
j = 1
while (fscan (imexlist, xc, yc, xlocation, ylocation) != EO
F) {
85
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if (i > 2) {
print(xc, yc, >> temp)
xloc[j] = xlocation
yloc[j] = ylocation
j = j + 1
}
i = i+1
}
if (!access(temp))
error(11, "No positions marked.")
#**************************************************************
# 5) Mark stellar apertures
tvmark(frame=frame, coords=temp, logfile="", autolog=no, ou
timag="", deletio="", command="", mark="circle", txsize=2, colo
r=204, radii=ap, label=no, number=yes, toleran=1.5, interactive
=no)
101
102
103
104
105
print(" ")
print("_________________________________________________")
continuechoice = ""
print("Step 2: Do any apertures overlap with another star?
(press ’q’ to quit or enter to continue)")
D. sdssphot.cl
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98
scan(continuechoice)
if (continuechoice == "q") {
bye
}
#**************************************************************
# 5) Do photometry
print("_________________________________________________")
print(" ")
print("Step 3: Photometry being carried out...")
datapars.scale = scale
datapars.fwhmpsf = 2.5
datapars.emissio = yes
datapars.sigma = INDEF
datapars.datamin = INDEF
datapars.datamax = 64000.
datapars.noise = "poisson"
datapars.ccdread = ccdread
datapars.gain = gain
datapars.exposur = exposur
datapars.airmass = airmass
datapars.filter = filter
datapars.obstime = obstime
centerpars.calgori = "none"
fitskypars.salgorithm = "median"
fitskypars.annulus = ap
fitskypars.dannulus = 10
photpars.apertur = ap
photpars.zmag = 0.0
phot(image=img_name, coords=temp, interactive=no,verify=no)
#**************************************************************
# 7) Copy positions and magnitudes to a single file for upload
if (access(img_name//".mag.4")) {
D. sdssphot.cl
148
149
150
151
152
153
154
155
156
157
158
159
160
99
magname=img_name//".mag.4"
}
else if (access(img_name//".mag.3")) {
magname=img_name//".mag.3"
}
else if (access(img_name//".mag.2")) {
magname=img_name//".mag.2"
}
else {
magname=img_name//".mag.1"
}
txdump(textfile=magname, fields="image, xairmass, mag", exp
r+, headers-, >> magfile)
161
162
163
j = 1
while (fscan (magfilelist, imagename, xairmass, maglevel) !
= EOF) {
164
print(imagename//":"//j//"
", airmass//"
", magleve
l//"
", xloc[j]//"
", yloc[j], >> "../../sdssOutput.txt")
165
xloc[j] = xlocation
166
yloc[j] = ylocation
167
j = j + 1
168
}
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
#**************************************************************
# 6) Delete temporary files
delete(imexfile, ver-, >& "dev$null")
delete(temp, ver-, >& "dev$null")
delete(magfile, ver-, >& "dev$null")
#**************************************************************
# 6) Demonstrate success
print("_________________________________________________")
print(" ")
print("Successful.")
end
Appendix E
Abbreviations
1. ADBS: the Arecibo Dual Beam Survey
2. ALFALFA: the Arecibo Legacy Fast Arecibo L-band Feed Array survey
3. B stars: hot, massive stars (see OBAFGKM)
4. CCD: Charge Coupled Device, the electronic equipment used to detect light
5. Dec: the position in the sky equivalent to latitude relative to the plane of
the earth’s rotation and the northern side of the plane
6. FIR: far infrared light ranging from 15 µm to 1000 µm
7. FWHM: the full width at half max of a Gaussian profile
8. GALEX: Galaxy Evolution Explorer, a UV space telescope
9. Hα: the Balmer transition of Hydrogen from n = 3 to n = 2 states
10. HI gas: neutral Hydrogen gas
11. HIPASS: the HI Parkes All Sky Survey, a survey performed at the Australian
Parkes telescope in the radio band
12. IR: infrared light ranging from 750 nm to 1 mm
13. IRAF: the Image Reduction and Analysis Facility, a group of astronomical
computer scripts intended to aid data reduction
14. ISM: the interstellar medium, made up of gas and dust
15. KPNO: Kitt Peak National Observatory
16. L : the luminosity of the sun
17. LINERs: Low Ionization Nuclear Emission Regions, galaxies that appear
similar to active galactic nuclei but with lower energy emission
18. M : the mass of the sun
19. Mpc: megaparsecs, a unit of distance equivalent to 3.2 million lightyears
100
E. Abbreviations
101
20. n: an electron orbital level
21. NII: the Nitrogen II emission or absorption line
22. O stars; hot, massive stars (see OBAFGKM)
23. OBAFGKM; the classification levels of stars in order from most hot and
massive to least hot and massive
24. PSF: point-spread function, describing the shape of a stellar profile
25. RA: right ascension, the position in the sky relative to the plane of the
Earth’s orbit around the sun
26. SDSS: the Sloan Digital Sky Survey, a survey covering the majority of the
northern sky
27. SINGG: the Survey for Ionization in Neutral Gas Galaxies, a survey within
the local supercluster
28. SFR: star formation rate
29. SFRD: star formation rate density
30. UV: ultra-violet light ranging from 400 nm to 10 nm
31. z: redshift, a measure of time and distance. Redshifts roughly are as follows:
z = 0 corresponds to now, z = 1 corresponds to 50% of the age of the
universe ago (6.9 of 13.7 billion years), z = 2 corresponds to 75% of the age
of the universe ago, etc.
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