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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"} 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 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(" ") 81 B. pipeline.cl 34 35 36 37 38 39 82 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) 45 46 47 48 49 50 51 52 if (alignyes==yes) { print(" ") print(" Shifting Images....") print(" ") doalign(images=allimages, rootname=rootn, prefix="sh", app+, ver-) 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 #********************************************************** 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(" ") B. pipeline.cl 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 83 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-) B. pipeline.cl 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 84 } 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-) B. pipeline.cl 157 158 159 160 161 162 163 164 165 166 167 168 169 170 85 #********************************************************** 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") 174 175 176 177 178 179 180 181 182 183 imcopy(nms[1], rootn//"_R.fits", ver-) display(image=outnamefinal, frame=1) } delete ("imcomb", ver-) delete ("logfile", ver-) end Appendix C haphot.cl 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 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"} 86 C. haphot.cl 35 36 37 38 39 40 41 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 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 imexfile = mktemp("tmp$lst1") imexlist = imexfile temp = mktemp("tmp$lst2") templist = temp img_name = image masked_image = img_name//"_masked" if(! access("../magfiles")) { mkdir(newdir="../magfiles") } #************************************************************** # 1) Check that the image exists if(!access(img_name)) { if(!access(img_name//".imh")) { if(!access(img_name//".fits")) { print("Image not found.") print("exiting...") bye } } } #************************************************************** # 2) Get median of image imstat(images=img_name, fields="midpt", lower=INDEF, upper= INDEF, binwidt=0.1, format=no) | scan(midpt) C. haphot.cl 76 77 78 79 80 81 82 83 84 85 86 87 88 zlow = midpt - 30 zhigh = zlow + 150 print("z1 = ", zlow, "z2 = ", zhigh) #************************************************************** # 3) Display image in Imtool print("Displaying image ", img_name) display(image=img_name, frame=frame, fill=fill, zscale=no, zrange=no, z1=zlow, z2=zhigh, >& "dev$null") 88 89 90 91 92 93 94 95 96 97 #************************************************************** # 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$null") 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 i = 1 while (fscan (imexlist, xc, yc) != EOF) { if (i == 3) { print(xc, yc, >> temp) i = 1 } i = i+1 } if (!access(temp)) error(11, "No positions marked.") #************************************************************** # 5) Draw concentric circles around galaxy j = 1 clr = 204 tvmark(frame=frame, coords=temp, logfile="", autolog=no, ou C. haphot.cl 89 timag="", deletio="", command="", mark="plus", txsize=2, color= clr, label=no, number=no, toleran=1.5, interactive=no) 117 while (j < numap+1) 118 { 119 ap = (smallap/scale)+((j - 1)*(deltap/scale)) 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 if ((j - 1)%2 == 0) { clr = 204 + j/2 } tvmark(frame=frame, coords=temp, logfile="", autolog=no , outimag="", deletio="", command="", mark="circle", radii=ap, color=clr, label=no, number=no, toleran=1.5, interactive=no) j = j+1 } #************************************************************** # 5.1) Check for success of measure and retry if necessary continuechoice = "" print(" ") print("_________________________________________________") while (continuechoice != "c" && continuechoice != "q") { continuechoice = "c" print("Step 2: Was the positioning successful? (press ’c’ to continue, ’l’ to relocate, ’s’ to resize inner ring, or ’q’ to quit) {c}") 139 scan(continuechoice) 140 141 142 143 144 145 146 147 148 149 150 151 152 153 if (continuechoice == "l") { delete(imexfile, ver-) delete(temp, ver-) imexfile = "imex.tmp" imexlist = imexfile temp = "tmpforha.tmp" templist = temp print(" ") print("Retry: Mark the galaxy (hit ’a’, then ’q’)") # display(image=img_name, frame=frame, fill=fill, zsc ale=no, erase=yes, zrange=no, z1=zlow, z2=zhigh, >& "dev$null") C. haphot.cl 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 90 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) 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 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 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 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.) 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 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) 277 278 279 280 281 282 283 284 285 286 287 } #************************************************************** # 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 294 295 296 297 298 299 300 301 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 2 3 4 5 6 7 8 9 10 11 12 13 14 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 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 96 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 61 62 63 64 65 66 67 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$null") D. sdssphot.cl 72 73 74 75 76 77 78 79 80 97 #************************************************************** # 4) Get stellar positions print(" ") print("Step 1: Mark solitary stars (hit ’a’, then ’q’).") rimexam.center = yes imexamine(frame=frame, logfile=imexfile, keeplog=yes, >& "d ev$null") 81 82 83 84 i = 1 j = 1 while (fscan (imexlist, xc, yc, xlocation, ylocation) != EO F) { 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 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 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 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. 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