Lawrence Livermore National Laboratory

Arti GargInstitute of Geophysics and Planetary Physics

Preliminary Results from the SuperMACHO Survey

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

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

NGC 4216 in a simulated halo from:John Kormendy (http://chandra.as.utexas.edu/~kormendy/dm-halo-pic.html)

Dark MatterHalo

Visible GalaxyDisk

Galactic Dark Matter Halo:What’s it made of?

• MOND? • Dark Matter?

– Non-baryonic– Baryonic

Dark Matter on Many ScalesObservational evidence for Dark Matter on many

scales…not a priori necessary that the solution is the same on all scales

Jason Ware

The Entire Universe:Large Scale Structure

Abel 2218 (NASA HST)

Galaxy Clusters

Galaxy Halos2dF Galaxy Redshirt Survey

Baryons in Galaxies•Gas?

-Hot gas emits-Cold gas collapses

•MAssive Compact Halo Objects (MACHOs)?

Microlensing to Detect MACHOs

• In 1986, B. Paczynski suggested using gravitational microlensing toward the Magellanic Clouds to detect MACHOs

Large Magellanic Cloud

Milky Way Halo

Us

MACHOs

Light PathMW illustration: Mark Garlick (Space-art) Earth Image: Apollo 17

Anglo-Australian Observatory/Royal Observatory Edinburgh

Microlensing Primer

Lens L with Mass M

Source S

Image 1

Observer O

DOL DLSDOS

Microlensing Primer

Lens L with Mass M

Source S

Image 1

Observer O

DOL DLSDOS

2

4

c

GM

DD

D

OLOS

LSE =θDimensionless

Einstein angle

Microlensing: • Source and image are unresolved

- Source appears amplified• Relative motion between source and lens

- Temporal effect

Geometrical factorLens Mass

Microlensing Light Curve

Time

Flux

time of maximum

characteristic time(θE and vrel)

source brightness

amplification(umin, θE )

Source

Lens

Lens Trajectory

impact parameter = umin

Fraction of sources within rE of a lens at any time

Microlensing Survey Observables

i i

imeas t

t

E )ˆ(

ˆ

41

E

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

Ensemble of events has a uniform distribution of umin

Spatial Distribution “Screen-” vs. “Self-” lensing

ii tt ˆ event withan detectingfor efficiency )ˆ(

survey of timeexposure total E

E

Optical Depth -

Previous Microlensing Surveys• 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%

EROS-2, Tisserand et al. 2007

• 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%

MW

Hal

o (t

owar

d Cl

ouds

)M

31 H

alo

log Mlens (M))M

AC

HO

fra

ctio

n of

Hal

o

MW Halo Results

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

SuperMACHO fields

Primary field setPrimary field set

Secondary field setSecondary field set

Difference Imaging

Detection Imageflux(timage)

Difference Imageflux(timage) – flux(ttempl)

Reference Imageflux(ttempl)

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

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

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)

Sample Light Curves

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

Time

Inte

nsity

(flux

)Microlensing Candidate Selection

• Microlensing events have a predictable light curve

Time

Inte

nsity

(flux

)Microlensing Candidate Selection

• But many other things have a similar light curve (e.g. type Ia supernovae)

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

andquantify

contamination. Use follow-up observations to

identifycontamination and

develop better selection

criteria.

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 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

Stars have characteristic ratios of filter intensities

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?

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

• 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

Pass

SimulationsModels ofLight Curves

Simulated Light Curves

Selection Criteria

Contamination Rate

Detection Efficiency

Supernovae

Simulated MicrolensingSimulated

Supernovae Ia

Fail

Microlensin

g

Supernovae

Microlensing

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)

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

Difference Images

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

Simulations of Microlensing

events

Simulations

Simulations of type Ia

Supernovae

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

Microlensing Candidates

Garg et al., in prep

PreliminaryEvent Rates

Inner fields(yellow)

Outer fields(green)

sparser

Rest et al., in prep.

Number of events and distribution consistent with expected type Ia SNcontamination 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!!

Armin Rest, 02/13/08, UCSD

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