preliminary results from the supermacho survey
DESCRIPTION
Preliminary Results from the SuperMACHO Survey. Arti Garg Institute of Geophysics and Planetary Physics. 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. LLNL-PRES-411078. Outline. - PowerPoint PPT PresentationTRANSCRIPT
Lawrence Livermore National Laboratory
Arti GargInstitute of Geophysics and Planetary Physics
Preliminary Results from the SuperMACHO Survey
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.LLNL-PRES-411078
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
NGC 4216 in a simulated halo from:John Kormendy (http://chandra.as.utexas.edu/~kormendy/dm-halo-pic.html)
Dark MatterHalo
Visible GalaxyDisk
Galactic Dark Matter Halo:What’s it made of?
• MOND? • Dark Matter?
– Non-baryonic– Baryonic
Dark Matter on Many ScalesObservational evidence for Dark Matter on many
scales…not a priori necessary that the solution is the same on all scales
Jason Ware
The Entire Universe:Large Scale Structure
Abel 2218 (NASA HST)
Galaxy Clusters
Galaxy Halos2dF Galaxy Redshirt Survey
Baryons in Galaxies•Gas?
-Hot gas emits-Cold gas collapses
•MAssive Compact Halo Objects (MACHOs)?
Microlensing to Detect MACHOs
• In 1986, B. Paczynski suggested using gravitational microlensing toward the Magellanic Clouds to detect MACHOs
Large Magellanic Cloud
Milky Way Halo
Us
MACHOs
Light PathMW illustration: Mark Garlick (Space-art) Earth Image: Apollo 17
Anglo-Australian Observatory/Royal Observatory Edinburgh
Microlensing Primer
Lens L with Mass M
Source S
Image 1
Observer O
DOL DLSDOS
Microlensing Primer
Lens L with Mass M
Source S
Image 1
Observer O
DOL DLSDOS
2
4
c
GM
DD
D
OLOS
LSE =θDimensionless
Einstein angle
Microlensing: • Source and image are unresolved
- Source appears amplified• Relative motion between source and lens
- Temporal effect
Geometrical factorLens Mass
Microlensing Light Curve
Time
Flux
time of maximum
characteristic time(θE and vrel)
source brightness
amplification(umin, θE )
Source
Lens
Lens Trajectory
impact parameter = umin
Fraction of sources within rE of a lens at any time
Microlensing Survey Observables
i i
imeas t
t
E )ˆ(
ˆ
41
E
(Mollerach & Roulet 2002, Alcock et al. 2000)
Ensemble of events has a uniform distribution of umin
Spatial Distribution “Screen-” vs. “Self-” lensing
ii tt ˆ event withan detectingfor efficiency )ˆ(
survey of timeexposure total E
E
Optical Depth -
Previous Microlensing Surveys• 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%
EROS-2, Tisserand et al. 2007
• POINT-AGAPE survey (Calchi Novati 2005)
- 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%
MW
Hal
o (t
owar
d Cl
ouds
)M
31 H
alo
log Mlens (M))M
AC
HO
fra
ctio
n of
Hal
o
MW Halo Results
SuperMACHO ProjectLLNL/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 setPrimary field set
Secondary field setSecondary field set
Difference Imaging
Detection Imageflux(timage)
Difference Imageflux(timage) – flux(ttempl)
Reference Imageflux(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
Time
Inte
nsity
(flux
)Microlensing Candidate Selection
• Microlensing events have a predictable light curve
Time
Inte
nsity
(flux
)Microlensing Candidate Selection
• But many other things have a similar light curve (e.g. type Ia supernovae)
Microlensing Candidate Selection
• And if your nights off from the telescope and the weather conspire in the wrong way, discrimination is difficult
Use simulations to reduce
andquantify
contamination. Use follow-up observations to
identifycontamination and
develop better selection
criteria.
Classifying events using follow-up
• Spectroscopic Observations
Spectrum of a supernova Spectrum of the Sun, a typical star(How microlensing might look)
Source: http://homepages.wmich.edu/~korista/sun-images/solar_spec.jpg
Wavelength Wavelength
Inte
nsity
Inte
nsity
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 -Year-to-Year change in baseline
Variable Star Active Galactic Nucleus (AGN)
-Need more data after peak
Significant and well-sampled?
Microlensing-Like?
-This is a Supernova
-Fit well by microlensing and supernova models
Unlike other phenomena?
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
Pass
SimulationsModels ofLight Curves
Simulated Light Curves
Selection Criteria
Contamination Rate
Detection Efficiency
Supernovae
Simulated MicrolensingSimulated
Supernovae Ia
Fail
Microlensin
g
Supernovae
Microlensing
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 of Microlensing
events
Simulations
Simulations of type Ia
Supernovae
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
PreliminaryEvent Rates
Inner fields(yellow)
Outer fields(green)
sparser
Rest et al., in prep.
Number of events and distribution consistent with expected type Ia SNcontamination plus ~20% MACHO fraction, but some caveats:
-Did we underestimate the SN rate?-Other forms of contamination (e.g. other types of SNe, CV’s, ???)?
Still a Work in Progress!!
Armin Rest, 02/13/08, UCSD
The SuperMACHO survey was undertaken as part of the NOAO Survey Program.