The SuperMACHO Project: Using Gravity to Find Dark Matter

1
The SuperMACHO ProjectUsing Gravity to Find
Dark Matter

• Arti Garg
• November 1, 2007
• Harvard University
• Department of Physics and Harvard-Smithsonian
  Center for Astrophysics

2
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

3
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

4
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

5
What is Dark Matter?

• Well, we dont 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
Galaxy Clusters
Galaxy Halos
Image Credit Jason Ware
Abel 2218 (http//spaceimages.northwestern.edu/p29
-abel.html)
http//zebu.uoregon.edu/1999/ph123/lec08.html
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Galactic Halo Dark Matter

• Rotation velocities are too fast

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Andromeda Galaxy
Image Credit Jason Ware
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Radial Profile of Rotation Velocity
From http//zebu.uoregon.edu/1999/ph123/lec08.html
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Galactic Halo Dark Matter

• Rotation velocities are too fast
• Radial profile of rotation velocities suggests
  spherical distribution of dark matter the Halo

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NGC 4216 in a simulated halo
Visible Galaxy Disk
Dark Matter Halo
From http//chandra.as.utexas.edu/kormendy/dm-hal
o-pic.html
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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

12
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)
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Infrared Image of a Gravitational Lens System
Image
Lens Galaxy
HE0435-1223
From CASTLES Survey http//cfa-www.harvard.edu/ca
stles/Individual/HE0435.html
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What is Gravitational Lensing?

• This can happen even in the case where the source
  is not obscured by the lens

Source
Lens
Light Path
Observer
Image
Virtual Light Path
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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

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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

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Microlensing
Source
Lens Trajectory
Lens
Microlensing Light Curve
Observed Source Brightness
Time
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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.htm
l Earth Image Apollo 17
MACHOs
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The MACHO project (1995-2000)
0.4

• Found t of 1.2 x 10-7 (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)
• Revised MACHO fraction estimate 16 (Bennett
  2005)
• EROS-2 find a MACHO fraction of lt7 (Tisserand et
  al. 2006)

- 0.3
Contamination
(Alcock et al. ApJ 542, 281 2000)
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The SuperMACHO Project

• SuperMACHO is a 5 year survey of the Large
  Magellanic Cloud (LMC) to search for microlensing
  events
• Fifth season of observations completed in January
  2006
• Observations conducted between Oct Jan on the
  Cerro Tololo InterAmerican Observatory (CTIO) 4m
  Blanco Telescope in Chile

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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

22
CTIO Blanco 4m
23
SuperMACHO fields
Primary field set Secondary field set
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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 AM University Nicholas B. Suntzeff

1. Now Harvard University, Department of Physics
2. Now NOAO North, Tucson
3. Now Johns Hopkins University

1. Now Argonne National Laboratory
2. Now University of Padova

25
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

26
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)

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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)

28
Empirical corrections to Difference flux errors
Error ratio of random positions on difference
image
sma5,amp 7 Jan 6, 2006
m -0.04 s 1.54
Should be 1.0, Errors are Underestimated!!
difference flux/flux err
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Empirical corrections to Difference flux errors

• Histograms of typical distributions of FDFSIG
• FDFSIG standard deviation of flux/flux err for
  a grid of random positions in a difference image
  (image keyword)

sm54 all amps
amp 1, all fields
FDFSIG for image
FDFSIG for image
m 1.47 s 0.2
m 1.43 s 0.2
30
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

31
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

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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

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Microlensing Event Selection

• Microlensing causes the brightness of a star to
  change in a predictable way

Brightness
Time
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Microlensing Event Selection

• But many other things also change in brightness
  such as supernovae
• these turn out to be much more common

Brightness
Time
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Microlensing Event Selection

• And if your nights off from the telescope and the
  weather conspire in the wrong way, its hard to
  tell whats microlensing

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Challenges to Event Classification

• High volume of events
• Need sophisticated software tools (25 million
  stars)
• High rate of contamination
• Supernovae outnumber microlensing by up to 10
  times
• Gaps in sampling and low S/N
• No bright time (near full moon) observations
• Difficult to discriminate microlensing from other
  phenomena

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Microlensing Event Selection

• So what do you do?
• You get a graduate student!
• Follow-up Observations

Magellan III 6.5m Telescopes
38
Microlensing Event Selection

• So what do you do?
• You get a graduate student!
• Light Curve analysis tools

39
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

40
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 follow-up
  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

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Classifying events using follow-up

• Spectroscopic Observations

Intensity
Intensity
Wavelength
Wavelength
Source http//homepages.wmich.edu/korista/sun-im
ages/solar_spec.jpg
Spectrum of the Sun, a typical star (How
microlensing might look)
Spectrum of a supernova
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Magellan Spectroscopy

• Spectroscopy achieved to m21.5
• Positive identification of
• Supernovae (type Ia and type II), AGNs, CVs,
  other Variable Stars
• Many other objects with uncertain spectroscopic
  identifications but definitely extragalactic

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SM-2004-LMC-821
VR21
Spectral classification Broad Absorption Line AGN
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Classifying events using follow-up

• Spectroscopy is an excellent way to classify an
  event, but...
• It is time-consuming and cant be done for faint
  events
• Obtaining a spectrum for every interesting event
  is not feasible

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Classifying events using follow-up

• Multi-band observations - poor mans
  spectroscopy

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Classifying events using follow-up

• Multi-band observations - poor mans
  spectroscopy
• The ratio of brightness in different filters
  gives a crude measure of the events 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

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Stars have characteristic ratios of filter
intensities
49
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

50
A light curve describes an objects brightness as
a function of time
Brightness
Time
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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?

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Unique?
-Frequent and periodic variability
-Year-to-Year change in baseline
Active Galactic Nucleus (AGN)
Variable Star
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Significant and well-sampled?
-Need more data after peak
54
Microlensing-Like?
-This is a Supernova
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Unlike other phenomena?
-Fit well by microlensing and supernova models
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Passes all Criteria
57
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

58
Light Curve Analysis

• Estimating how many microlensing event we can
  detect (i.e. Detection Efficiency)
• Simulate a large number of microlensing events of
  all possible combinations of event parameters
• source brightness, event duration, and
  amplification
• Determine which of these events survive selection
  criteria
• Estimating how many events we should expect
• Multiply by distribution of event parameters
  consistent with various MACHO models to get
  expected number of microlensing events

59
Simulations

• Allows optimal tuning of selection criteria
• Allow the most microlensing events while
  rejecting the most contaminants
• Provides estimate of contaminant fraction
• Provides quantitative estimate of detection
  efficiency
• 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)

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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

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Simulations

• Obtain light curves for a grid of positions
  across our field-of-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

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Simulations
Simulations of Microlensing events
Simulations of Supernovae
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Detection Efficiency Depends on Source Brightness
Simulated
Number of events
Recovered
Source Brightness (-2.5log(Intensity))
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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

65
Summary

• Most of the matter in our Galaxy is dark
• We can detect Dark Matter with gravitational
  lensing

66
Summary

• SuperMACHO searches for Dark Matter in the form
  of MACHOs in the Milky Way
• Gravitational microlensing is easily confused
  with other things

67
Summary

• Additional observations and light curve analysis
  improve event classification
• Simulations allow for estimation of detection
  efficiency and contamination rate

68
Lens Equation
Source Blandford Narayan 1986
(Mollerach Roulet 2002)
69
Microlensing
rE projection of qE at lens distance
u impact parameter
Lens Trajectories
Magnification Due to Lensing Event
source
Source Michael Richmond (RIT)
Source Paczynski 1991
70
Microlensing Light Curve
fo x Amax a umin closest approach
to time of maximum brightness
t characteristic time ( )
Flux
fo baseline source flux
Time
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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)
G Distribution of
(Mollerach Roulet 2002)
72
The MACHO project (1995-2000)
0.4

• Found t of 1.2 x 10-7 (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)
• Revised MACHO fraction estimate 16 (Bennett
  2005)
• EROS-2 find a MACHO fraction of lt7 (Tisserand et
  al. 2006)

- 0.3
Contamination
(Alcock et al. ApJ 542, 281 2000)
73
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

74
RR Lyrae from MACHO (black) and SuperMACHO (red)