Preliminary Results from the SuperMACHO Survey

1. Preliminary Results from the SuperMACHO Survey Arti Garg Institute of Geophysics and Planetary Physics This work work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-PRES-411078

2. Outline • The Galactic dark matter problem • Using microlensing to detect Galactic dark matter • The SuperMACHO survey • Candidate selection • Follow-up observations • Light curve analysis • Simulations • Detection Efficiency • Contamination Rate • SuperMACHO candidates

3. Galactic Dark Matter Halo:What’s it made of? • MOND? • Dark Matter? • Non-baryonic • Baryonic Visible Galaxy Disk Dark Matter Halo NGC 4216 in a simulated halo from: John Kormendy (http://chandra.as.utexas.edu/~kormendy/dm-halo-pic.html)

4. Galaxy Clusters Abel 2218 (NASA HST) Dark Matter on Many Scales Observational evidence for Dark Matter on many scales…not a priori necessary that the solution is the same on all scales • Baryons in Galaxies • Gas? • Hot gas emits • Cold gas collapses • MAssive Compact Halo • Objects (MACHOs)? The Entire Universe: Large Scale Structure Galaxy Halos Jason Ware 2dF Galaxy Redshirt Survey

5. Microlensing to Detect MACHOs • In 1986, B. Paczynski suggested using gravitational microlensing toward the Magellanic Clouds to detect MACHOs Milky Way Halo Us Large Magellanic Cloud Light Path Anglo-Australian Observatory/ Royal Observatory Edinburgh MW illustration: Mark Garlick (Space-art) Earth Image: Apollo 17 MACHOs

6. Microlensing Primer Image 1 Source S Observer O Lens L with Mass M b DOL DLS DOS

7. Microlensing Primer Image 1 Source S Observer O Lens L with Mass M b DOL DLS DOS Dimensionless Einstein angle Lens Mass Geometrical factor • Microlensing: • Source and image are unresolved • Source appears amplified • Relative motion between source and lens • Temporal effect

8. Microlensing Light Curve Source Lens Trajectory impact parameter =umin Lens amplification (umin, θE) time of maximum characteristic time (θE and vrel) Flux source brightness Time

9. Microlensing Survey Observables Ensemble of events has a uniform distribution ofumin Fraction of sources within rE of a lens at any time  Optical Depth - t (Mollerach & Roulet 2002, Alcock et al. 2000) Spatial Distribution  “Screen-” vs. “Self-” lensing

10. MACHO survey (Alcock et al 2000, Bennett 2005) 13-17 microlensing event candidates MACHO fraction ~16% of Halo EROS-2 (Tisserand 2008) Only 1 event observed, 39 expected Upper limit: MACHO fraction <8% OGLE (Wyrzykowski et al. 2008) Upper limit: MACHO fraction <8% Previous Microlensing Surveys MW Halo Results MW Halo (toward Clouds) MACHO fraction of Halo • POINT-AGAPE survey (Calchi Novati 2005) • 6 microlensing event candidates • MACHO fraction ~20% (MWG and M31) • MEGA survey (de Jong et al. 2006) • 4 microlensing event candidates • Favors self-lensing • MACHO fraction <30% log Mlens (M) M31 Halo EROS-2, Tisserand et al. 2007 MACHO project, Alcock et al. 2000, ApJ 542, 281

11. SuperMACHO ProjectLLNL/IGPP: A. Garg, K.H. Cook, S.Nikolaev,Harvard: A. Rest, C.W. Stubbs (P.I.), P. Challis, G. Narayan, UPitt: W.M. Wood-Vasey, NOAO:R.C. Smith, K. Olsen, A. Zenteno, JHU:M.E. Huber,UW:A. Becker, A. Miceli, FNAL: G. Miknaitis, McMaster: D.L. Welch, Catolica: L. Morelli, A. Clocchiati, D. Minniti, OSU: J.L. Prieto, Texas A&M: N.B. Suntzeff • CTIO 4m • Mosaic Imager: big FOV • Monitor 68 LMC fields • 23 deg2 and ~50 million sources • 150 half-nights • 5 years (2001-2006) • Blocks of ~3 months/year • Near real-time detection • Single filter: custom VR • Difference imaging

12. SuperMACHO fields Primary field set Secondary field set

13. Difference Imaging Reference Image flux(ttempl) Difference Image flux(timage) – flux(ttempl) Detection Image flux(timage)

14. RR Lyrae from MACHO (black) and SuperMACHO (red)

15. Outline • The Galactic dark matter problem • Using microlensing to detect Galactic dark matter • The SuperMACHO survey • Candidate selection • Follow-up observations • Light curve analysis • Simulations • Detection Efficiency • Contamination Rate • SuperMACHO candidates

16. Determining Optical Depth • Candidate Selection • Establish a set of criteria for classifying an event as microlensing • Detection Efficiency • Likelihood of including a real microlensing event with a given set of parameters (t0, msource, t, umin)

17. Sample Light Curves

18. Challenges to Candidate Selection • High number of events • ~150,000 light curves identified as variable • High rate of contamination • Up to 1455 background type Ia supernovae during survey • Gaps in sampling and low S/N • No bright time (near full moon) observations • Majority of stars near detection limit

19. Microlensing Candidate Selection • Microlensing events have a predictable light curve Intensity (flux) Time

20. Microlensing Candidate Selection • But many other things have a similar light curve (e.g. type Ia supernovae) Intensity (flux) Time

21. Microlensing Candidate Selection • And if your nights off from the telescope and the weather conspire in the wrong way, discrimination is difficult Use simulations to reduce and quantify contamination. Use follow-up observations to identify contamination and develop better selection criteria.

22. 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

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

24. 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

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

27. Classifying events using follow-up • Multi-band observations - “poor man’s spectroscopy” • The ratio of intensity 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

28. Stars have characteristic ratios of filter intensities

29. Classifying events using light curves • 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?

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

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

32. Microlensing-Like? -This is a Supernova

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

34. Passes all Criteria

35. Outline • The Galactic dark matter problem • Using microlensing to detect Galactic dark matter • The SuperMACHO survey • Candidate selection • Follow-up observations • Light curve analysis • Simulations • Detection Efficiency • Contamination Rate • SuperMACHO candidates

36. Simulations Models of Light Curves Fail Simulated Light Curves Simulated Microlensing Microlensing Supernovae Simulated Supernovae Ia Selection Criteria Microlensing Pass Supernovae Detection Efficiency Contamination Rate

37. Simulating Errors • Multiple sources of error • Random • Poisson error • Systematic • What we do to the image • Image differencing • Image convolution • Imperfect subtraction • How we measure the flux • Photometry (DoPhot)

38. Simulating Errors • Multiple sources of error • Random • Poisson error – analytical model • Systematic • What we do to the image • Image differencing • Image convolution – empirical correction • Imperfect subtraction • How we measure the flux • Photometry (DoPhot) – empirical model

39. Difference Images

40. Simulating Imperfect Subtractions: Add events to a grid of light curves • Obtain light curves for a grid of positions across FOV • Add simulated event to light curve Garg et al. 2008

41. Simulations Simulations of Microlensing events Simulations of type Ia Supernovae

42. Simulating Imperfect Subtractions • Faster and requires less storage than adding fake stars to each image • Also, do not need to model the PSF • Simulations of >107 ML and SN Ia light curves • Error Propagation • Reproduces systematic effects from reduction pipeline • Preserves correlations in observing conditions • Straightforward to simulate other types of light curves

43. Microlensing Candidates Garg et al., in prep

44. Inner fields (yellow) sparser Outer fields (green) PreliminaryEvent Rates • Number of events and distribution consistent with expected type Ia SN • contamination plus ~20% MACHO fraction, but some caveats: • Did we underestimate the SN rate? • Other forms of contamination (e.g. other types of SNe, CV’s, ???)? • Still a Work in Progress!! Rest et al., in prep.

45. The SuperMACHO survey was undertaken as part of the NOAO Survey Program.