The SuperMACHO Project: Using Gravity to Find Dark Matter

1. 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

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 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 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

6. Galactic Halo Dark Matter • Rotation velocities are too fast

7. Andromeda Galaxy Image Credit: Jason Ware

8. Radial Profile of Rotation Velocity From http://zebu.uoregon.edu/1999/ph123/lec08.html

9. Galactic Halo Dark Matter • Rotation velocities are too fast • Radial profile of rotation velocities suggests spherical distribution of dark matter – the Halo

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

11. 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)

13. Infrared Image of a Gravitational Lens System Image Lens Galaxy HE0435-1223 From CASTLES Survey: http://cfa-www.harvard.edu/castles/Individual/HE0435.html

14. 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

15. 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

16. Microlensing Source Lens Trajectory Lens Microlensing “Light Curve” Observed Source Brightness Time

17. 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

18. 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

19. SuperMACHO fields Primary field set Secondary field set

20. 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 • Now Harvard University, Department of Physics • Now NOAO North, Tucson • Now Johns Hopkins University • Now Argonne National Laboratory • Now University of Padova

21. 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

22. 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)

23. 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)

24. 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

25. 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

26. 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

27. Microlensing Event Selection • Microlensing causes the brightness of a star to change in a predictable way Brightness Time

28. Microlensing Event Selection • But many other things also change in brightness such as supernovae • these turn out to be much more common Brightness Time

29. 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

30. Microlensing Event Selection • So what do you do? • You get a graduate student! • “Follow-up” Observations Magellan I&II 6.5m Telescopes

31. Microlensing Event Selection • So what do you do? • You get a graduate student! • Light Curve analysis tools

32. 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

33. 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

34. Classifying events using follow-up • Spectroscopic Observations Intensity Intensity Wavelength Wavelength Source: http://homepages.wmich.edu/~korista/sun-images/solar_spec.jpg Spectrum of the Sun, a typical star (How microlensing might look) Spectrum of a supernova

35. SM-2004-LMC-821 VR~21 Spectral classification: Broad Absorption Line AGN

36. 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

37. Classifying events using follow-up • Multi-band observations - “poor man’s spectroscopy”

39. 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

40. Stars have characteristic ratios of filter intensities

41. 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

42. A light curve describes an object’s brightness as a function of time Brightness Time

43. 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?

44. Unique? -Frequent and periodic variability -Year-to-Year change in baseline Active Galactic Nucleus (AGN) Variable Star

45. Significant and well-sampled? -Need more data after peak

46. Microlensing-Like? -This is a Supernova

47. Unlike other phenomena? -Fit well by microlensing and supernova models

48. Passes all Criteria

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. 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)