National Aeronautics and Space Administration

1. National Aeronautics and Space Administration The Mock LISA Data Challenges:report on Round 2 Michele Vallisneri (Jet Propulsion Laboratory) for the MLDC Task Force: Stas Babak, John Baker, Matt Benacquista,Neil Cornish, Curt Cutler, Shane Larson,Tyson Littenberg, Edward Porter, M.V., Alberto Vecchio Michele Vallisneri

2. MLDCs: why now? • For LISA, data analysis is integral to the measurement concept • We must demonstrate that we can meet the LISA science requirements • We need to understand data analysis quantitatively to translate requirements into design decisions • Encourage, track, and compare progress in LISA data-analysis development • Kickstart the development of a LISA data-analysis computational infrastructure MLDCs: how? • Coordinated, voluntary effort in GW community • Periodically issue datasets with synthetic noise and GW signals from sources of undisclosed parameters • Challenge participants return parameter estimates and descriptions of search methods Michele Vallisneri

3. Mock LISA Data Challenge Task Force • Specify pseudo-LISA model • Identify standard source models • Specify data format (lisaXML) • Plan challenge progression • Prepare and distribute training and challenge datasets • Develop software infrastructure • Evaluate challenge responses • Alberto Vecchio (co-chair) • Michele Vallisneri (co-chair) • Keith Arnaud • Stas Babak • John Baker (keymaster) • Matt Benacquista • Neil Cornish • Curt Cutler • Sam Finn • Shane Larson • Tyson Littenberg • Ed Porter • Sathyaprakash • Jean–Yves Vinet Michele Vallisneri

4. MLDC timeline (so far) Jan 2006 work begins! Dec 2006 Challenge 1 results due; presented at GWDAW-11 (procs: gr-qc/0701139, plus several by participants) Jun/Jul 2007 Challenge 2 results due; presented here. Challenge 1B released Jun 2006 Challenge 1 datasets released at 6th LISA Symposium (procs: gr-qc/0609105-6) Jan 2007 Challenge 2 datasets released (gr-qc/0701139) Dec 2007 Challenge 1B results due, to present at GWDAW-12. Challenge 3 released Michele Vallisneri

5. Challenge progression Challenge 1 • One-year dataset, Gaussian, stationary instrument noise • 1.1.X: Verification, isolated, and moderately interfering Galactic binaries (monochromatic, moderate SNR) – 7 datasets • 1.2.X: Isolated SMBH binaries (circular, 2PN quasi-adiabatic)– 2 datasets (mod. and high SNR) Challenge 2 • Two-year dataset, Gaussian, stationary instrument noise • 2.1: Full-scale 30-million-source Galaxy (monochromatic binaries) • 2.2: Galaxy + 4–6 SMBH binaries (SNR 20–2000) + 5 EMRIs • 1.3.X: isolated EMRIs (Barack-Cutler waveforms) in instrument noise – 5 datasets Challenge 1B • A re-do of Challenge 1, as a simpler entry point for research groups new to LISA data analysis • + 5 EMRI datasets Challenge 3 • Two-year dataset, Gaussian, stationary instrument noise • Isolated, more realistic waveforms (chirping Galactic binaries, SMBH binaries with spins, higher harmonics), wider priors • Bursts, stochastic backgrounds Michele Vallisneri

6.    Challenge 1 • Verification Galactic binaries • Unknown strong Galactic binaries • SMBH inspirals Michele Vallisneri

7. Challenge 1 results • 10 collaborations, a variety of methods (template-bank, stochastic and genetic matched filtering; TF; tomography; Hilbert transform), also in combination • Despite short timescale, all challenges“solved” by at least one group; some entries locked on strong secondary maxima • Set the field and tools for Challenge 2 Michele Vallisneri

8. Challenge 2 Michele Vallisneri

9. Challenge 2 13 collaborations, 40 researchers, 22 entries Gerard Auger, Stanislav Babak, Leor Barack, Arkadiusz Blaut, Ed Bloomer, Duncan Brown, Nelson Christensen, James Clark, Neil Cornish, Jeff Crowder, Curt Cutler, Stephen Fairhurst, Jonathan Gair, Hubert Halloin, Martin Hendry, Jean-Yves Vinet, Arturo Jimenez, Andrzej Królak, Tyson Littenberg, Ilya Mandel, Chris Messenger, Renate Meyer, Soumya Mohanty, Rajesh Nayak, Antoine Petiteau, Matt Pitkin, Eric Plagnol, Edward Porter, Reinhard Prix, Emma Robinson, Christian Roever, Pavlin Savov, Alexander Stroeer, Jennifer Toher, Michele Vallisneri, Alberto Vecchio, John Veitch, Linqing Wen, John Whelan, Graham Woan Michele Vallisneri

10.    Challenge 2 • Galaxy • SMBH inspirals (over Galaxy) • EMRIs Michele Vallisneri

11. Challenge 2: the Galaxy • GLIG (RJMCMC) • Królak/Blaut (iterative bank-based F-stat matched filtering) • Crowder/Cornish/Littenberg (MCMC + model comparison) • Prix/Whelan (bank-based F-stat matched filtering + coincidence) • Nayak/Jimenez/Mohanty (tomographic reconstruction) evaluation is tricky! • on the basis of evidence: amount of power removed • but need Galaxy prior or Occam factors) • on the basis of the (previously secret) true source information: parameter differences, but which source is which? • single-source correlation analysis against catalog of SNR>2 sources • or: Doppler distance (optimize over extrinsic parameters) • more generally, it is difficult to define when a source is resolvable • the richness of Bayesian methods is undercut by looking only at the mode of distributions Michele Vallisneri

12. Template and source blending • template blending (these graphs, courtesy of Jeff Crowder): multiple templates match single true source • source blending: multiple true sources matched by single template • moral (Crowder): in MCMC searches, less is not more, it is less Michele Vallisneri

13. Correlation analysis (2.1) 1777 403 19324 (deg) (deg) Michele Vallisneri

14. Correlation analysis, Doppler matching (2.1) 1777 403 19324 3862 (deg) (deg) Michele Vallisneri

15. Intrinsic-parameter errors (2.1) freq.(units of freq. bins) (deg) (deg) Michele Vallisneri

16. Extrinsic-parameter errors (2.1) (rad) (rad) (rad) Michele Vallisneri

17. Galaxy subtraction in Challenge 2.1 • Using the MT/JPL catalog (thanks to Jeff Crowder) of 19324 sources, except 1712 rejected by Bayesian Information Criterion Michele Vallisneri

18. Challenge 2: SMBH binaries • Babak/Porter (F-stat template-bank matched filtering + MCMC) • Cornish/Porter (frequency-annealed MCMC) • JPL/CIT (TF track search + template-bank matched filtering + MCMC) • LisaFrance (TF track search) SMBH-1 Michele Vallisneri

19. Challenge 2: SMBH binaries SMBH-4 Michele Vallisneri

20. Challenge 2: SMBH binaries SMBH-2 Michele Vallisneri

21. Challenge 2: EMRIs • Babak/Barack/Gair/Porter (time-annealed MCMC) • Cornish (segmented MCMC) • Gair/Mandel/Wen (TF track search) Michele Vallisneri

22. Conclusions • We’re happy: • Participation is strong (but we need more, try 1B!) • A lot of work is being accomplished • Results are comforting • We’re showing that LISA data analysis is possible • Difficulty is incremental, but no big issues of principleare foreseen • The MLDC infrastructure (LISAtools) can be used for many other experiments outside the mainline challenges Michele Vallisneri

23. Resources (see also the poster!) • MLDC official site:astrogravs.nasa.gov/docs/mldc • MLDC taskforce wiki:www.tapir.caltech.edu/dokuwiki/listwg1b:home • MLDC taskforce telecons:every other Friday, 8:15am PST (see wiki) • Mailing lists:lisatools-mldc@gravity.psu.edu (formulation)lisatools-challenge@gravity.psu.edu (participants) • LISAtools software (including full MLDC pipeline):sourceforge.net/projects/lisatools Michele Vallisneri

24. Challenge 2: SMBH Michele Vallisneri

25. Challenge 2: EMRIs Michele Vallisneri

26. Pseudo-LISA in one slide (see poster!) LISA orbits Inclined Keplerian ellipses around Sun (to 2nd order in e), with standard initial position and orientation LISA noises Gaussian, stationary, uncorrelated proof-mass S1/2 = 310-15 sqrt(1 + (10-4Hz/f)-2) m s-2 Hz-1/2) and optical-path S1/2 = 2010-12 m Hz-1/2 noises; no laser phase noise (assume TDI cancellation) TDI observables Use strain (LISA Simulator) or fractional-frequency-fluctuation (Synthetic LISA) response to GWs. Combine as TDI 1.5 X, Y, Z observables [Shaddock et al., PRD 68, 061303(R) (2003)]. Michele Vallisneri