Download Astrophysics in the Time Domain: Results and lessons

Lawrence Livermore National Laboratory
Preliminary Results from the
SuperMACHO Survey
Arti Garg
Institute of Geophysics and Planetary Physics
LLNL-PRES-411078
This work work was performed under the auspices of the U.S. Department of Energy by
Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
Outline
•
•
•
•
The Galactic dark matter problem
Using microlensing to detect Galactic dark matter
The SuperMACHO survey
Candidate selection
– Follow-up observations
– Light curve analysis
• Simulations
– Detection Efficiency
– Contamination Rate
• SuperMACHO candidates
Galactic Dark Matter Halo:
What’s it made of?
• MOND?
• Dark Matter?
– Non-baryonic
– Baryonic
Visible Galaxy
Disk
Dark Matter
Halo
NGC 4216 in a simulated halo from:
John Kormendy (http://chandra.as.utexas.edu/~kormendy/dm-halo-pic.html)
Dark Matter on Many Scales
Observational evidence for Dark Matter on many
scales…not a priori necessary that the solution is the
same on all scales
The Entire Universe:
Large Scale Structure
Galaxy Clusters
Baryons in Galaxies
•Gas?
- Hot gas emits
- Cold gas collapses
•MAssive Compact Halo
Objects (MACHOs)?
Galaxy Halos
2dF Galaxy Redshirt Survey
Abel 2218 (NASA HST)
Jason Ware
Microlensing to Detect MACHOs
• In 1986, B. Paczynski suggested using
gravitational microlensing toward the
Magellanic Clouds to detect MACHOs
Milky Way Halo
Us
Large Magellanic Cloud
Anglo-Australian Observatory/
Royal Observatory Edinburgh
Light Path
MW illustration: Mark Garlick (Space-art)
Earth Image: Apollo 17
MACHOs
Microlensing Primer
Image 1
Source S
Observer O
b
DOL
Lens L with Mass M
DOS
DLS
Microlensing Primer
Image 1
Source S
Observer O
b
DOL
Dimensionless
Einstein angle
Lens L with Mass M
DLS
DOS

E
D
GM
LS 4
2
D
D
c
OS
OL
Geometrical factor
Microlensing:
• Source and image are unresolved
- Source appears amplified
• Relative motion between source and lens
- Temporal effect
Lens Mass
Microlensing Light Curve
Source
impact parameter = umin
Lens
time of maximum
Flux
Lens Trajectory
amplification
(umin, θE )
characteristic time
(θE and vrel)
source brightness
Time
Microlensing Survey Observables
Ensemble of events has a uniform distribution of umin
Fraction of sources within rE of a lens
at any time
 Optical
 meas
Depth - 
tˆi
1


E 4 i E (tˆi )
E  total exposure time of survey
E (tˆi )  efficiency for detecting an event with tˆi
(Mollerach & Roulet 2002, Alcock et al. 2000)
Spatial Distribution  “Screen-” vs. “Self-” lensing
• MACHO survey (Alcock et al 2000, Bennett 2005)
– 13-17 microlensing event candidates
– MACHO fraction ~16% of Halo
• EROS-2 (Tisserand 2008)
– Only 1 event observed, 39 expected
– Upper limit: MACHO fraction <8%
• OGLE (Wyrzykowski et al. 2008)
– Upper limit: MACHO fraction <8%
• POINT-AGAPE survey (Calchi Novati 2005)
M31 Halo
MW Halo Results
-
6 microlensing event candidates
MACHO fraction ~20% (MWG and
M31)
• MEGA survey (de Jong et al. 2006)
-
4 microlensing event candidates
Favors self-lensing
MACHO fraction <30%
MACHO fraction of Halo
MW Halo (toward Clouds)
Previous Microlensing Surveys
log Mlens (M)
EROS-2, Tisserand et al. 2007
SuperMACHO Project
LLNL/IGPP: A. Garg, K.H. Cook, S.Nikolaev, Harvard: A. Rest, C.W. Stubbs (P.I.), P. Challis, G. Narayan,
UPitt: W.M. Wood-Vasey, NOAO: R.C. Smith, K. Olsen, A. Zenteno, JHU: M.E. Huber, UW: A. Becker,
A. Miceli, FNAL: G. Miknaitis, McMaster: D.L. Welch, Catolica: L. Morelli, A. Clocchiati, D. Minniti,
OSU: J.L. Prieto, Texas A&M: N.B. Suntzeff
• CTIO 4m
• Mosaic Imager: big FOV
• Monitor 68 LMC fields
– 23 deg2 and ~50 million sources
• 150 half-nights
• 5 years (2001-2006)
– Blocks of ~3 months/year
• Near real-time detection
• Single filter: custom VR
• Difference imaging
SuperMACHO fields
Primary field set
Secondary field set
Difference Imaging
Reference Image
flux(ttempl)
Detection Image
flux(timage)
Difference Image
flux(timage) – flux(ttempl)
RR Lyrae from MACHO (black) and SuperMACHO (red)
Outline
•
•
•
•
The Galactic dark matter problem
Using microlensing to detect Galactic dark matter
The SuperMACHO survey
Candidate selection
– Follow-up observations
– Light curve analysis
• Simulations
– Detection Efficiency
– Contamination Rate
• SuperMACHO candidates
Determining Optical Depth
• Candidate Selection
– Establish a set of criteria for classifying an event as
microlensing
• Detection Efficiency
– Likelihood of including a real microlensing event
with a given set of parameters (t0, msource, t, umin)
Sample Light Curves
Challenges to Candidate Selection
• High number of events
– ~150,000 light curves identified as variable
• High rate of contamination
– Up to 1455 background type Ia supernovae during
survey
• Gaps in sampling and low S/N
– No bright time (near full moon) observations
– Majority of stars near detection limit
Microlensing Candidate Selection
Intensity (flux)
• Microlensing events have a predictable light
curve
Time
Microlensing Candidate Selection
Intensity (flux)
• But many other things have a similar light
curve (e.g. type Ia supernovae)
Time
Microlensing Candidate Selection
• And if your nights off from the telescope and
the weather conspire in the wrong way,
discrimination is difficult
Use follow-up
observations to
identify
contamination and
develop
better selection
criteria.
Use simulations to
reduce
and
quantify
contamination.
Classifying events using follow-up
Intensity
Intensity
• Spectroscopic Observations
Wavelength
Wavelength
Source: http://homepages.wmich.edu/~korista/sun-images/solar_spec.jpg
Spectrum of a supernova
Spectrum of the Sun, a typical star
(How microlensing might look)
SM-2004-LMC-821
VR~21
Spectral classification: Broad Absorption Line AGN
Classifying events using follow-up
• Spectroscopy is an excellent way to classify an
event, but...
– It is time-consuming and can’t be done for faint
events
• Obtaining a spectrum for every interesting
event is not feasible
Classifying events using follow-up
• Multi-band observations - “poor man’s
spectroscopy”
Classifying events using follow-up
• Multi-band observations - “poor man’s
spectroscopy”
• The ratio of intensity in different “filters” gives
a crude measure of the event’s wavelength
spectrum
– The ratios for “vanilla” stars (i.e. microlensing)
differ from supernovae
• This method is less precise but can be used for
faint events
Stars have characteristic ratios of filter
intensities
Classifying events using light curves
• Why do we need it?
– Only have follow-up for 2 out of 5 years
– Follow-up is incomplete and sometimes inconclusive
• What is it?
– Software analysis tools that calculate ~50 “statistics”
describing the light curve
•
•
•
•
Unique?
Significant and Well-sampled?
Microlensing-like?
Unlike other things?
Unique?
-Frequent and periodic variability
Variable Star
-Year-to-Year change in baseline
Active Galactic Nucleus (AGN)
Significant and well-sampled?
-Need more data after peak
Microlensing-Like?
-This is a Supernova
Unlike other phenomena?
-Fit well by microlensing and supernova models
Passes all Criteria
Outline
•
•
•
•
The Galactic dark matter problem
Using microlensing to detect Galactic dark matter
The SuperMACHO survey
Candidate selection
– Follow-up observations
– Light curve analysis
• Simulations
– Detection Efficiency
– Contamination Rate
• SuperMACHO candidates
Models of
Light Curves
Simulations
Fail
Simulated
Light Curves
Selection
Criteria
Pass
Contamination
Rate
Detection
Efficiency
Simulating Errors
• Multiple sources of error
– Random
• Poisson error
– Systematic
• What we do to the image
– Image differencing
» Image convolution
» Imperfect subtraction
• How we measure the flux
– Photometry (DoPhot)
Simulating Errors
• Multiple sources of error
– Random
• Poisson error – analytical model
– Systematic
• What we do to the image
– Image differencing
» Image convolution – empirical correction
» Imperfect subtraction
• How we measure the flux
– Photometry (DoPhot) – empirical model
Difference Images
Simulating Imperfect Subtractions: Add events
to a grid of light curves
• Obtain light curves for a grid of positions across FOV
• Add simulated event to light curve
Garg et al. 2008
Simulations
Simulations
Simulations of
of
Microlensing
type Ia
Supernovae
events
Simulating Imperfect Subtractions
• Faster and requires less storage than adding fake
stars to each image
– Also, do not need to model the PSF
– Simulations of >107 ML and SN Ia light curves
• Error Propagation
– Reproduces systematic effects from reduction pipeline
– Preserves correlations in observing conditions
• Straightforward to simulate other types of light
curves
Microlensing Candidates
Garg et al., in prep
Preliminary
Event Rates
Inner fields
(yellow)
sparser
Outer fields
(green)
Number of events and
distribution consistent with
expected type Ia SN
contamination plus ~20%
MACHO fraction, but some
caveats:
Rest et al., in prep.
-Did we underestimate the SN rate?
-Other forms of contamination (e.g.
other types of SNe, CV’s, ???)?
Still a Work in Progress!!
The SuperMACHO survey was undertaken as part of the NOAO Survey Program.
Armin Rest, 02/13/08, UCSD