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: 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

6. Galactic Halo Dark Matter Rotation velocities are too fast

7. Image Credit: Jason Ware Andromeda Galaxy

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

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 From http://chandra.as.utexas.edu/~kormendy/dm-halo-pic.html Dark Matter Halo Visible Galaxy Disk

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 Observer Lens (e.g. galaxy) Source Images Light Path Virtual Light Path

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

14. What is Gravitational Lensing? This can happen even in the case where the source is not obscured by the lens Observer Lens Source Image Light Path Virtual Light Path

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. 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

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

18. 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.html Earth Image: Apollo 17

19. 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

20. 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

21. 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

24. SuperMACHO Team Harvard/CfA – Arti Garg, Christopher W. Stubbs (PI), W. Michael Wood-Vasey, Peter Challis, Gautham Narayan CTIO/NOAO – Armin Rest 1, R. Chris Smith, Knut Olsen 2, Claudio Aguilera LLNL – Kem Cook, Mark E. Huber 3, Sergei Nikolaev University of Washington – Andrew Becker, Antonino Miceli 4 FNAL – Gajus Miknaitis P. Universidad Catolica – Alejandro Clocchiatti, Dante Minniti, Lorenzo Morelli 5 McMaster University – Douglas L. Welch Ohio State University – Jose Luis Prieto Texas A&M University – Nicholas B. Suntzeff 1.Now Harvard University, Department of Physics 2.Now NOAO North, Tucson 3.Now Johns Hopkins University 4.Now Argonne National Laboratory 5.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)

27. 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 difference flux/flux err Error ratio of random positions on difference image sma5,amp 7 Jan 6, 2006  =  -0.04  = 1.54 Should be 1.0, Errors are Underestimated!!

29. 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  = 1.47  = 0.2  = 1.43  = 0.2 FDFSIG for image

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

32. 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

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

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

35. 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

36. 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

37. Microlensing Event Selection So what do you do? –You get a graduate student! 1.“Follow-up” Observations Magellan I&II 6.5m Telescopes

38. Microlensing Event Selection So what do you do? –You get a graduate student! 2.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

41. 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 Intensity

42. Magellan Spectroscopy Spectroscopy achieved to m~21.5 Positive identification of: –Supernovae (type Ia and type II), AGNs, CVs, other Variable Stars Many other objects with uncertain spectroscopic identifications but definitely extragalactic

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

44. 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

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

47. 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

48. 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 object’s brightness as a function of time Time Brightness

51. 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?

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

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

54. Microlensing-Like? -This is a Supernova

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

56. 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 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)

60. 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

61. 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

62. Simulations Simulations of Microlensing events Simulations of Supernovae

63. Detection Efficiency Depends on Source Brightness Source Brightness (-2.5*log(Intensity)) Number of events Simulated Recovered

64. 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. Most of the matter in our Galaxy is “dark” We can detect Dark Matter with gravitational lensing Summary

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

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

68. Lens Equation Source: Blandford & Narayan 1986 (Mollerach & Roulet 2002)

69. Microlensing Source: Michael Richmond (RIT) Lens TrajectoriesMagnification Due to Lensing Event Source: Paczynski 1991 r E = projection of  E at lens distance source u = impact parameter

70. Microlensing Light Curve Time Flux t o = time of maximum brightness t = characteristic time ( ) f o = baseline source flux f o x A max  u min = closest approach

71. Observables for Event Ensemble  = Optical depth toward source population –likelihood that a source is within r E 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 u min

72. 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

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)