The SuperMACHO Project:Using Gravity to Find Dark Matter

Arti GargNovember 1, 2007

Harvard UniversityDepartment 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:

Abel 2218 (http://spaceimages.northwestern.edu/p29-abel.html)Image Credit: Jason Ware

Permeating the Universe

Galaxy Clusters Galaxy “Halos”

http://zebu.uoregon.edu/1999/ph123/lec08.html

Galactic Halo Dark Matter

• Rotation velocities are too fast

Image Credit: Jason Ware

Andromeda Galaxy

From http://zebu.uoregon.edu/1999/ph123/lec08.html

Radial Profile of Rotation Velocity

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

From http://chandra.as.utexas.edu/~kormendy/dm-halo-pic.html

Dark MatterHalo

Visible GalaxyDisk

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

Observer

Lens(e.g. galaxy)

Source

Images

Light Path

Virtual LightPath

From CASTLES Survey: http://cfa-www.harvard.edu/castles/Individual/HE0435.html

HE0435-1223

Infrared Image of a Gravitational Lens System

LensGalaxy

Image

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

Time

Obs

erve

d S

ourc

e B

right

ness

Source

Lens

Lens Trajectory

Microlensing “Light Curve”

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

Large Magellanic Cloud

Milky Way Halo

Us

MACHOs

Light Path From http://antwrp.gsfc.nasa.gov/apod/ap050104.htmlEarth Image: Apollo 17

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 setPrimary field set

Secondary field setSecondary 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 NikolaevUniversity of Washington – Andrew Becker, Antonino Miceli4

FNAL – Gajus MiknaitisP. Universidad Catolica – Alejandro Clocchiatti, Dante Minniti,

Lorenzo Morelli5

McMaster University – Douglas L. WelchOhio State University – Jose Luis PrietoTexas A&M University – Nicholas B. Suntzeff

1. Now Harvard University, Department of Physics2. Now NOAO North, Tucson3. Now Johns Hopkins University

4. Now Argonne National Laboratory5. 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

Time

Brig

htne

ss

Microlensing Event Selection

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

Time

Brig

htne

ss

Microlensing Event Selection

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

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 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

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 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

A light curve describes an object’s brightness as a function of time

Time

Brig

htne

ss

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 -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

• 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

1. Allows optimal “tuning” of selection criteria– Allow the most microlensing events while rejecting the most

contaminants– Provides estimate of contaminant fraction

2. Provides quantitative estimate of detection efficiency– Fraction of simulated events that are recovered– Differences between simulated population and recovered

population

3. 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 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

Simulations

Simulations of

Microlensing

events

Simulations of Supernovae

Detection Efficiency Depends on Source Brightness

Source Brightness

(-2.5*log(Intensity))

Nu

mb

er o

f ev

ents

Simulated

Recovered

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

• Most of the matter in our Galaxy is “dark”

• We can detect Dark Matter with gravitational lensing

Summary

Summary

• SuperMACHO searches for Dark Matter in the form of MACHOs in the Milky Way

• Gravitational microlensing is easily confused with other things

• Additional observations and light curve analysis improve event classification

• Simulations allow for estimation of detection efficiency and contamination rate

Summary

Lens Equation

Source: Blandford & Narayan 1986

(Mollerach & Roulet 2002)

Microlensing

Source: Michael Richmond (RIT)

Lens Trajectories Magnification Due to Lensing Event

Source: Paczynski 1991

rE = projection of E at lens distance

source

u =impact parameter

Microlensing Light Curve

Time

Flu

x

to = time of maximum brightness

t = characteristic time ( ) fo = baseline

source flux

fo x Amax umin = closest approach

Observables for Event Ensemble

= Optical depth toward source population– likelihood that a source is within

rE of a lens at any time

Γ = Distribution of

(Mollerach & Roulet 2002, Alcock et al. 2000)

(Mollerach & Roulet 2002)

Ensemble of events has a uniform distribution of umin

The MACHO project (1995-2000)

• Found 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 <7% (Tisserand et al. 2006)

- 0.3+0.4

(Alcock et al. ApJ 542, 281 2000)

Contamination

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)