The SuperMACHO Project:
Using Gravity to Find Dark Matter
Arti Garg
November 1, 2007
Harvard University
Department of Physics and Harvard-Smithsonian Center for
Astrophysics
Outline
• What is Dark Matter?
• How can we detect DM with a telescope?
– Gravitational Microlensing
• The SuperMACHO survey
• My work
– Image-Processing Software Verification
– Microlensing Event Selection:
• “Follow-up” Observations
• “Light curve” Analysis
– Simulations
• Detection Efficiency
• Contamination Rate
Outline
• What is Dark Matter?
• How can we detect DM with a telescope?
– Gravitational Microlensing
• The SuperMACHO survey
• My work
– Image-Processing Software Verification
– Microlensing Event Selection:
• “Follow-up” Observations
• “Light curve” Analysis
– Simulations
• Detection Efficiency
• Contamination Rate
Outline
• What is Dark Matter?
• How can we detect DM with a telescope?
– Gravitational Microlensing
• The SuperMACHO survey
• My work
– Image-Processing Software Verification
– Microlensing Event Selection:
• “Follow-up” Observations
• “Light curve” Analysis
– Simulations
• Detection Efficiency
• Contamination Rate
What is Dark Matter?
• Well, we don’t really know
• What we do know:
– Objects in the Universe behave as if they feel
stronger gravitational forces than what the matter we
see could generate
– Most of the matter in the Universe is “dark”
– Places where dark matter might exist:
Permeating
the Universe
http://zebu.uoregon.edu/1999/ph123/lec08.html
Galaxy Clusters
Abel 2218 (http://spaceimages.northwestern.edu/p29-abel.html)
Galaxy “Halos”
Image Credit: Jason Ware
Galactic Halo Dark Matter
• Rotation velocities are too fast
Andromeda Galaxy
Image Credit: Jason Ware
Radial Profile of Rotation Velocity
From http://zebu.uoregon.edu/1999/ph123/lec08.html
Galactic Halo Dark Matter
• Rotation velocities are too fast
• Radial profile of rotation velocities
suggests spherical distribution of dark
matter – the Halo
NGC 4216 in a simulated halo
Visible Galaxy
Disk
Dark Matter
Halo
From http://chandra.as.utexas.edu/~kormendy/dm-halo-pic.html
Galactic Halo Dark Matter
• Rotation velocities are too fast
• Radial profile of rotation velocities
suggests spherical distribution of dark
matter – the Halo
• One proposed candidate for the dark
matter is in the form of “MAssive Compact
Halo Objects” (MACHOs)
– These can be detected through “gravitational
microlensing”
What is Gravitational Lensing?
• Light from a star or galaxy is bent by a
massive object between it and the
observer
Virtual Light
Path
Light Path
Images
Source
Observer
Lens
(e.g. galaxy)
Infrared Image of a Gravitational Lens System
Image
Lens
Galaxy
HE0435-1223
From CASTLES Survey: http://cfa-www.harvard.edu/castles/Individual/HE0435.html
What is microlensing?
• In microlensing, the separation between
the source and image is too small to be
resolved
– The lensed object just looks brighter
• Often the source, the lens, or both are
moving so the effect is temporal
– For SuperMACHO, the time scale is ~80 days
What is microlensing?
• In microlensing, the separation between
the source and image is too small to be
resolved
– The lensed object just looks brighter
• Often the source, the lens, or both are
moving so the effect is temporal
– For SuperMACHO, the time scale is ~80 days
Microlensing
Source
Lens Trajectory
Observed Source Brightness
Lens
Microlensing “Light Curve”
Time
Microlensing to Detect Dark Matter
• In 1986, B. Paczynski suggested using
microlensing to detect MACHOs by their
gravitational effect on stars in nearby
dwarf galaxies such as the Magellanic
Clouds
Milky Way Halo
Us
Large Magellanic Cloud
Light Path
From http://antwrp.gsfc.nasa.gov/apod/ap050104.html
Earth Image: Apollo 17
MACHOs
SuperMACHO Project
• More events:
– CTIO 4m
– Mosaic imager: big FOV
– 150 half nights over 5 years
• Completed Jan 2006
– blocks of ~3 months per year
• Observe every other night in
dark and gray time
– Single Filter: custom VR-band
• Spatial coverage:
– 68 fields, 23 sq deg.
• Difference Imaging
SuperMACHO fields
Primary field set
Secondary field set
SuperMACHO Team
Harvard/CfA – Arti Garg, Christopher W. Stubbs (PI),
W. Michael Wood-Vasey, Peter Challis, Gautham Narayan
CTIO/NOAO – Armin Rest1, R. Chris Smith, Knut Olsen2,
Claudio Aguilera
LLNL – Kem Cook, Mark E. Huber3, Sergei Nikolaev
University of Washington – Andrew Becker, Antonino Miceli4
FNAL – Gajus Miknaitis
P. Universidad Catolica – Alejandro Clocchiatti, Dante Minniti,
Lorenzo Morelli5
McMaster University – Douglas L. Welch
Ohio State University – Jose Luis Prieto
Texas A&M University – Nicholas B. Suntzeff
1.
2.
3.
Now Harvard University, Department of Physics
Now NOAO North, Tucson
Now Johns Hopkins University
4.
5.
Now Argonne National Laboratory
Now University of Padova
Outline
• What is Dark Matter?
• How can we detect DM with a telescope?
– Gravitational Microlensing
• The SuperMACHO survey
• My work
– Image-Processing Software Verification
– Microlensing Event Selection:
• “Follow-up” Observations
• “Light curve” Analysis
– Simulations
• Detection Efficiency
• Contamination Rate
Image Reduction Pipeline
• Implemented in Perl, Python, and C
• Images processed morning after observing
• Stages of image processing:
– Standard calibration (bias, flat field)
– Illumination correction
– Deprojection/Remapping (SWARP)
– Regular Photometry (DoPhot)
– Difference Imaging
– Photometry on Difference Images (Fixed PSF)
Image Reduction Pipeline
• Implemented in Perl, Python, and C
• Images processed morning after observing
• Stages of image processing:
– Standard calibration (bias, flat field)
– Illumination correction
– Deprojection/Remapping (SWARP)
– Regular Photometry (DoPhot)
– Difference Imaging
– Photometry on Difference Images (Fixed PSF)
Outline
• What is Dark Matter?
• How can we detect DM with a telescope?
– Gravitational Microlensing
• The SuperMACHO survey
• My work
– Image-Processing Software Verification
– Microlensing Event Selection:
• “Follow-up” Observations
• “Light curve” Analysis
– Simulations
• Detection Efficiency
• Contamination Rate
Microlensing Event Selection
• Detecting microlensing
– We monitor tens of millions of stars in the Large
Magellanic Cloud
– Tens of thousands of those appear to change
brightness
– Need to determine whether those changes are:
• Real, and not an artifact or cosmic ray
• Due to microlensing, or some other phenomenon
Microlensing Event Selection
• Detecting microlensing
– We monitor tens of millions of stars in the Large
Magellanic Cloud
– Tens of thousands of those appear to change
brightness
– Need to determine whether those changes are:
• Real, and not an artifact or cosmic ray
• Due to microlensing, or some other phenomenon
Microlensing Event Selection
Brightness
• Microlensing causes the brightness of a
star to change in a predictable way
Time
Microlensing Event Selection
• But many other things also change in
brightness such as supernovae
Brightness
– these turn out to be much more common
Time
Microlensing Event Selection
• And if your nights off from the telescope
and the weather conspire in the wrong
way, it’s hard to tell what’s microlensing
Microlensing Event Selection
•
So what do you do?
–
You get a graduate student!
1. “Follow-up” Observations
Magellan I&II 6.5m Telescopes
Microlensing Event Selection
•
So what do you do?
–
You get a graduate student!
2. Light Curve analysis tools
Outline
• What is Dark Matter?
• How can we detect DM with a telescope?
– Gravitational Microlensing
• The SuperMACHO survey
• My Work
– Image-Processing Software Verification
– Microlensing Event Selection:
• “Follow-up” Observations
• “Light curve” Analysis
– Simulations
• Detection Efficiency
• Contamination Rate
Follow-up Program
• Developed computational tools and protocols for
analyzing many GBs of nightly CTIO
observations in almost real time to pick out
interesting events and prioritize them for followup observation
– Follow-up is time critical because events are only
active for a few weeks
• Applied for many nights of Magellan telescope
time to follow interesting events as we
discovered them at CTIO
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 brightness 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
Outline
• What is Dark Matter?
• How can we detect DM with a telescope?
– Gravitational Microlensing
• The SuperMACHO survey
• My work
– Image-Processing Software Verification
– Microlensing Event Selection:
• “Follow-up” Observations
• “Light curve” Analysis
– Simulations
• Detection Efficiency
• Contamination Rate
Brightness
A light curve describes an object’s
brightness as a function of time
Time
Light Curve Analysis
• 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
• What is Dark Matter?
• How can we detect DM with a telescope?
– Gravitational Microlensing
• The SuperMACHO survey
• My Work
– Image-Processing Software Verification
– Microlensing Event Selection:
• “Follow-up” Observations
• “Light curve” Analysis
– Simulations
• Detection Efficiency
• Contamination Rate
Simulations
Allows optimal “tuning” of selection criteria
1.
–
–
2.
Allow the most microlensing events while rejecting the most
contaminants
Provides estimate of contaminant fraction
Provides quantitative estimate of detection efficiency
–
–
3.
Fraction of simulated events that are recovered
Differences between simulated population and recovered
population
Estimate how many events we should expect from
various models
–
Multiply by distribution of event parameters consistent with
various microlensing models to get expected number of
microlensing events (Rest et al. 2005)
Simulations
• Simulate a large number of events
– Microlensing: all combinations of source star
brightness, event duration, and amplification
• Determine which events survive selection criteria
 Detection Efficiency
– Supernovae: all combinations of redshift,
extinction by dust, intrinsic shape
• Determine which events survive selection criteria
 Contamination Rate
Simulations
• Obtain light curves for a grid of positions across our fieldof-view
• Add simulated event to each position
– Can add multiple events to the same light curve
– We simulated ~57 million ML events and ~4 million SNe
Simulations
Simulations of
Simulations of
Microlensing
Supernovae
events
Number of events
Detection Efficiency Depends on
Source Brightness
Simulated
Recovered
Source Brightness
(-2.5*log(Intensity))
Next Steps
• We are finalizing our selection criteria
– Final set of Candidates
– Final Detection Efficiencies
– Final Contamination Rate
• We will distinguish between microlensing
models by comparing the predicted rate of
ML events with our observed rate
Summary
• Most of the matter in our Galaxy is “dark”
• We can detect Dark Matter with
gravitational lensing
Summary
• SuperMACHO searches for Dark Matter in
the form of MACHOs in the Milky Way
• Gravitational microlensing is easily
confused with other things
Summary
• Additional observations and light curve
analysis improve event classification
• Simulations allow for estimation of
detection efficiency and contamination rate
Lens Equation
Source: Blandford & Narayan 1986
(Mollerach & Roulet 2002)
Microlensing
rE = projection of qE at lens distance
u=
impact
parameter
source
Lens Trajectories
Source: Michael Richmond (RIT)
Magnification Due to
Lensing Event
Source: Paczynski 1991
Microlensing Light Curve
Flux
to = time of maximum brightness
fo x Amax a umin =
closest approach
t = characteristic time
(
)
fo = baseline
source flux
Time
Observables for Event Ensemble
Ensemble of events has a uniform distribution of umin
t = Optical depth toward
source population
– likelihood that a source is within
rE of a lens at any time
(Mollerach & Roulet 2002, Alcock et al. 2000)
Γ = Distribution of
(Mollerach & Roulet 2002)
The MACHO project (1995-2000)
-7
• Found t of 1.2 -+0.4
x
10
0.3
(Alcock et al 2000)
– Consistent with Milky Way Halo
composed of ~8-50% MACHOs
– Event time scales ~80 days
• Recent results from
EROS-2 indicate some
events were not
microlensing (Miltsztajn & Tisserand 2005)
Contamination
– Revised MACHO fraction estimate
~16% (Bennett 2005)
– EROS-2 find a MACHO fraction of
<7% (Tisserand et al. 2006)
(Alcock et al. ApJ 542, 281 2000)
SuperMACHO Project
• More events:
– CTIO 4m
– Mosaic imager: big FOV
– 150 half nights over 5 years
• Completed Jan 2006
– blocks of ~3 months per year
• Observe every other night in
dark and gray time
– Single Filter: custom VR-band
• Spatial coverage:
– 68 fields, 23 sq deg.
• Difference Imaging
RR Lyrae from MACHO (black) and SuperMACHO (red)