Aspects of the LIGO burst search analysis

1. Alan Weinstein, Caltech For the LSC Burst ULWG LIGO Science Seminar November 12, 2002 Outline: LSC Burst ULWG Burst search goals Analysis pipeline LIGO S1 data Event Trigger Generators Burst simulations Tuning thresholds Applying vetos Finding coincident events Averaging over source direction “Representative” S1 results Aspects of the LIGO burst search analysis AJW, Caltech, LIGO Project

2. Burst ULWG Status • The LSC Burst Upper Limits working group has prepared a draft report on a burst search with E7 data (no final results) LIGO-T-020114-00-Z • Intense effort in progress to generate a report on burst search with S1 data, with real results, by Nov 1 ;) • This report will be available soon; and we can hope to work towards publication by S2. • SO, no real results can be presented here & now. • AND, this isn’t even an “official” status report; just my impressions of where things stand, with emphasis on aspects on the analysis that I know the most about. • Expect more, and more interesting, burst search reports soon! AJW, Caltech, LIGO Project

3. Burst upper limits working group • At least 35 members from LIGO and LSC • Chairs: Sam Finn and Peter Saulson • Web page: http://www.ligo.caltech.edu/~ajw/bursts/bursts.html • Active, archived email list: bursts@gravity.phys.psu.edu • Elog of E7 & related activities: http://cosmos.nirvana.phys.psu.edu/enote/ • Weekly telecons, sub-group meetings • Subgroups and principles: • LDAS-based burst filters (“DSO’s”) – Erik Katsavounidis, Ed Daw, Julien Sylvestre • Data conditioning – Sam Finn • IFO diagnostics – David Shoemaker • Trigger and Vetos – John Zweizig, Stefan Ballmer • Simulations, efficiency evaluation – Alan Weinstein , Laura Cadonati • Coincident Event analysis tools – Daniel Sigg, Laura Cadonati • External Triggers – Szabi Marka, Rauha Rahkola AJW, Caltech, LIGO Project

4. Burst Group membership Rana Adhikari, Warren Anderson, Stefan Ballmer, Barry Barish, Biplab Bhawal, Jim Brau, Kent Blackburn, Laura Cadonati, Joan Centrella, Ed Daw, Ron Drever, Sam Finn, Ray Frey, Ken Ganezer, Joe Giaime, Gabriela Gonzalez, Bill Hamilton, Ik Siong Heng, Masahiro Ito, Warren Johnson, Erik Katsavounidis, Sergei Klimenko, Albert Lazzarini, Isabel Leonor, Szabi Marka, Soumya Mohanty, Benoit Mours, Soma Mukherjee, David Ottoway, Fred Raab, Rauha Rahkola, Peter Saulson, Robert Schofield, Peter Shawhan, David Shoemaker, Daniel Sigg, Amber Stuver, Tiffany Summerscales, Patrick Sutton, Julien Sylvestre, Alan Weinstein, Mike Zucker, John Zweizig Almost certainly incomplete – apologies! AJW, Caltech, LIGO Project

5. Burst search goals AJW, Caltech, LIGO Project

6. Burst search goals • Search for short-duration bursts with unknown waveforms • Short duration: < 1 second; more typically, < 0.2 seconds. • Matched filtering techniques are appropriate for waveforms for which a model exists. Explicitly exclude, here! • Although the waveforms are a-priori unknown, we must require them to be in the LIGO S1 sensitivity band (~ 100-3000 Hz) • Search for gravitational wave bursts of unknown origin • Bound on the rate of detected gravitational wave bursts, viewed as originating from fixed strength sources on a fixed distance sphere centered about Earth, expressed as a region in a rate v. strength diagram. • Search for gravitational wave bursts associated with gamma-ray bursts • The result of this search is a bound on the strength of gravitational waves associated with gamma-ray bursts. • Work in progress by Marka, Rahkola, etc – not reported on today! AJW, Caltech, LIGO Project

7. Search goals (2) • Rely on multi-detector coincidences (in time and in burst properties) to eliminate most or all fake bursts. • Use S1 triple-coincidence data (H2, H1, L1) • GEO data being analyzed in parallel, and final results will include them. Not yet! • Have not yet decided how to make use of double-coincidences, or triple coincidences with 4 detectors… • Given the S1 sensitivity, we do not expect to detect GWs • Assume that any coincident bursts are fake, estimate background through time-lag analysis, set upper limits only • Plan to work much harder on demanding consistency amongst observed coincident bursts before claiming detection • Analysis pipeline designed to work for both upper limits and detection, but have not worked out detailed (blind) criteria for detection AJW, Caltech, LIGO Project

8. GEO - goals • Demonstrate ability to run GEO and LIGO data through equivalent data processing pipelines, up to and through the construction of temporal coincidences. • GEO data can be passed through an analysis pipeline on both sides of the Atlantic. • The incorporation of GEO data into the LIGO analysis (quadruple coincidence) in the “Nov. 1” upper limit is viewed to be unrealistic; however, demonstrating the mechanics of the analysis with an hour of GEO may be feasible. AJW, Caltech, LIGO Project

9. Analysis Pipeline AJW, Caltech, LIGO Project

10. Event Tool Analysis Pipeline • For E7 and S1, “GW” channel is *:LSC-AS_Q • This channel is passed to LDAS, conditioned & whitened, then passed to one of several “Event Trigger Generators” (ETG’s, aka DSOs) • Event Triggers (burst candidates) are stored in the LDAS metaDataBase (sngl_burst table) with start time, duration, SNR, frequency band, amplitude/power, … • A subset of IFO diagnostic and PEM channels are passed to the DMT, where one or more monitors generate “Veto Triggers” • Both GW event and veto trigger generation require careful optimization of thresholds • Veto: Event triggers that overlap in time with veto triggers are excluded – loss of livetime. • Event triggers and veto triggers are each characterized by a start time and a duration. • Resulting list of “IFO Triggers” are the final product of a single IFO analysis. AJW, Caltech, LIGO Project

11. Event Tool Pipeline, continued • Bring together IFO triggers from multiple interferometers, require temporal coincidence: • coincident in time if their start times coincide to better than the greater of the light travel time between the IFO sites and the start time resolution. • Other parameters that characterize the events (e.g., characteristic amplitude, frequency or bandwidth) may also be required to be consistent. • The result is a list of coincident events (divide by livetime to get coincident event rate.) • Store in LDAS metaDataBase (multi_burst table) • Accidental coincidence (background or fake) rate is evaluated via time-delayed coincidence (“lag plots”). • Coincident event rate and background rate estimate are combined (Feldman-Cousins) to get excess event rate (or rate upper limit). • This is then interpreted in terms of the efficiency for detecting astrophysical signals. AJW, Caltech, LIGO Project

12. Event Tool Analysis Pipeline - comments • Use of LDAS for GW channel and DMT for “veto” channels is arbitrary • This choice is largely historical • Both tools have their strengths and weaknesses, and their loyal user base • I think it would have been cleaner to have one tool for both applications • Veto channels, and their efficacy, change from run to run. • Vetos much less efficacious for S1 than for E7. This is a good thing! • Much overlap with Inspiral UL, DetChar working groups. • For current analysis, coincidence processing done with EventTool in ROOT • For current analysis, only the simplest coincidence criteria (time, crude frequency overlap) are applied. Much more work is needed! AJW, Caltech, LIGO Project

13. Post-LDAS/DMT event processing • Result of LDAS job on GW channel (with or without simulated injection) is an event trigger in metaDatabasetablesngl_burst, with start time, duration, frequency band, SNR, amplitude… • Result ofDMT job onveto channel is a veto trigger in metaDatabasetablegds_trigger, with similar information. • Manipulate these triggers, apply vetos, find coincidences, etc, using Event Class (Sigg, Ito) in ROOT • Plots from L Cadonati use this tool • Can alternatively use matlab, PAW, or your favorite tool. AJW, Caltech, LIGO Project

14. LIGO S1 data AJW, Caltech, LIGO Project

15. LIGO S1 data • August 23 – September 9, 2002: 408 hrs (17 days). • L1: duty cycle 41.7% ; Total Locked time: 170 hrs H1: duty cycle 57.6% ; Total Locked time: 235 hrs H2: duty cycle 73.1% ; Total Locked time: 298 hrs • Double coincidence: L1 and H1duty cycle 28.4%; Total coincident time: 116 hrs Double coincidence: L1 and H2duty cycle 32.1%; Total coincident time: 131 hrs Double Coincidence: H1 and H2duty cycle 46.1%; Total coincident time: 188 hrs • Triple Coincidence: L1, H1, and H2duty cycle 23.4% ;Total coincident time: 95.7 hrs Triple coincidence AJW, Caltech, LIGO Project

16. Playground data • Gaby Gonzalez and Peter Saulson chose a representative sample of 13 locked segments, from the triple coincidence segments. They add up to 9.3 hours. • All tuning of ETG and veto trigger thresholds done on playground data only. • Will we include these 9.3 hours in the full analysis and results? AJW, Caltech, LIGO Project

17. Non-stationarity, and Epoch Veto • BLRMS noise in GW channel is not stationary. • Detector response to GW (calibration) is not stationary. • Bursty-ness of GW channel is not stationary. • Fortunately, these appear to vary much less in S1 than in E7, thanks to efforts of detector and DetChar groups. • Much of this is driven by gradual misalignment during long locked stretches. • Under much study! • Shall we veto some epochs of the data based on excess RMS noise? Burst rate? Degradation of response? AJW, Caltech, LIGO Project

18. BLRMS - Epoch Veto ? • BLRMS noise is far from stationary. • Playground data (pink vertical lines) are not very representative. • Veto certain epochs based on excessive BLRMS noise in some bands? AJW, Caltech, LIGO Project

19. Veto on BLRMS? Histograms: 6-min segment BLRMS. vertical lines at 1s and 3s Veto segments beyond, eg, 3s? (L. Cadonati) AJW, Caltech, LIGO Project

20. Calibration of detector response Xext can be a GW or a calibration line; Can be seen in AS_Q Or in DARM_CTRL. C(f) is not stationary! AJW, Caltech, LIGO Project

21. Monitoring calibration lines 51.3 972.8 Veto epochs with low a ? AJW, Caltech, LIGO Project

22. Event trigger generators AJW, Caltech, LIGO Project

23. Event Trigger Generators • Three LDAS filters (ETG’s or DSOs) are now being used to recognize candidate signals: • POWER - Excess power in tiles in the time-frequency plane • Flanagan, Anderson, Brady, Katsavounidis • TFCLUSTERS - Search for clusters of pixels in the time-frequency plane. • Sylvestre • SLOPE - Time-domain templates for large slope or other simple features • Daw, Yakushin • We will continue to carry all three in the hopes that the different approaches will have different strengths for different waveform morphologies, so that an “OR” will give us higher efficiency for GWs for a given fake rate. But, the jury is still out. • All three of these ETGs ran online during the 2nd half of S1 • NEW filter introduced last week: WaveBurst (Klimenko, Yakushin) • Performs t-f analysis in wavelet domain, working with pairs of IFOs. AJW, Caltech, LIGO Project

24. tfclusters • Compute t-f spectrogram, in 1/8-second bins • Threshold on power in a pixel, get uniform black-pixel probability • Simple pattern recognition of clusters in B/W plane; threshold on size, or on size and distance for pairs of clusters AJW, Caltech, LIGO Project

25. Data flow in LDAS User pipeline request • frameAPI • datacondAPI • mpiAPI • wrapperAPI • LAL code • eventmonAPI • metadataAPI • metaDB AJW, Caltech, LIGO Project

26. Data conditioning in datacond • All of our burst filters are expected to work best with (at least, approximately) whitened data. • This is not matched filtering: don’t need to know detector response function to find excess power. • In datacondAPI, we (approximately) whiten and HP (at 150 Hz) the data with pre-designed linear filters. • These filters were designed for E7, and have not yet been optimized for S1; but I don’t think this is critical. • No attempt (yet) at line removal: but we believe that this will eventually be very necessary. AJW, Caltech, LIGO Project

27. Burst simulations AJW, Caltech, LIGO Project

28. Burst Simulations - GOALS • Test burst search analysis chain from: • IFO (ETM motion in response to GW burst)  • GW channel (AS_Q) data stream into LDAS  • search algorithms in LDAS  • burst triggers in database  • post-trigger analysis (optimizing thresholds and vetoes, clustering of multiple triggers, forming coincidences) • Evaluate pipeline detection efficiency for different waveforms, amplitudes, source directions, and different search algorithms • In burst search, simulated signal is injected at an early stage in LDAS (in datacondAPI) • Compare simulated signals injected into IFO with signals injected into data stream: make sure we understand IFO response AJW, Caltech, LIGO Project

29. burst waveforms: t-f character ZM SN bursts Bandwidth vs duration merger chirp ringdown ZM SN burst • Generic statements about the sensitivity of our searches to poorly-modeled sources can straightforwardly be made from the t-f “morphology”… • longish-duration, small bandwidth (ringdowns, Sine-gaussians) • longish-duration, large bandwidth (chirps, Gaussians) • short duration, large bandwidth (merger) • In-between (Zwerger-Muller or Dimmelmeier SN waveforms) • These SN waveforms are distance-calibrated; all others are parameterized by a peak or rms strain amplitude AJW, Caltech, LIGO Project

30. Ad-hoc signals: (Sine)-Gaussians SG 554, Q = 9 These have no astrophysical significance; But are well-defined in terms of waveform, duration, bandwidth, amplitude AJW, Caltech, LIGO Project

31. damped sinusoid (“ringdown”) waveform Damped sinusoid in 10 seconds of data from H2:LSC-AS_Q from E7 playground A series of damped sinusoids can be used as a “swept sine” calibration of burst search efficiency AJW, Caltech, LIGO Project

32. Zwerger-Müller SN waveforms • Rationale: • http://www.mpa-garching.mpg.de/Hydro/GRAV/grav1.html • These are astrophysically-motivated waveforms, computed from detailed simulations of axi-symmetric SN core collapses. • There are only 78 waveforms computed, in different classes: • “regular” infall, bounce, ringdown • Multiple bounces, on centrifugal barrier of rapidly-rotating core • “rapid collapse” – rapid change in adiabatic index • Work is in progress to get many more, including relativistic effects, etc. • These waveforms are a “menagerie”, revealing only crude systematic regularities. They are wholly inappropriate for matched filtering or other model-dependent approaches. • Their main utility is to provide a set of signals that one could use to compare the efficacy of different filtering techniques. • Parameters: • Distance D • Signals have an absolute normalization • Almost all waveforms have duration < 0.2 sec AJW, Caltech, LIGO Project

33. Zwerger Muller SN waveforms • The 78 ZM waveforms differ in • initial angular momentum (A parameter), governing degree of differential rotation • initial rotational energy (B, orb = Erot/Epot). A,B roughly govern how many bounce peaks • Within each (AB) set, the adiabatic indexGris varied from 1.325 to 1.28 (stiff to soft), and the peak amplitude of the wave depends strongly on this parameter, as seen in the right. AJW, Caltech, LIGO Project

34. Relativistic core collapse waveforms • Dimmelmeier, H., Font, J. A., and Müller, E., "Gravitational waves from relativistic rotational core collapse", Astrophys. J. Lett., 560, L163-L166, (2001),http://arxiv.org/abs/astro-ph/0103088. AJW, Caltech, LIGO Project

35. Menagerie of burst waveformsburied in E2 noise, including calibration/TF chirp ZM supernova Hermite-gaussian ringdown AJW, Caltech, LIGO Project

36. What we need to know about the IFOs • Transfer function for injection from GDS into ETMx/y • (counts/nm * pendulum TF) • Response function from ETMx To LSC-AS_Q Both of these are available from calibrations; but their variation in time has not yet been included in the analysis • For tfclusters & power, need IFO noise spectrum. Currently, this is estimated from the data read in to the LDAS job. This can, and does, bias the result. It’s not a big bias, for small signals; but a better way should be developed… AJW, Caltech, LIGO Project

37. Tuning thresholds and evaluating efficiency AJW, Caltech, LIGO Project

38. Tuning of thresholds • Trigger thresholds for GW ETG’s and vetos were tuned using playground data only • For ETG’s, simulations used to obtain an efficiency • Figure of merit to minimize: background rate / efficiency • Could also use background rate / efficiency3 • POWER and TFCLUSTERS use adaptive thresholds: using power distribution over 6-minute intervals as a baseline, threshold on excess power with fixed probability, assuming Gaussian statistics (POWER) or a Rice distribution (TFCLUSTERS). • SLOPE uses an absolute threshold; trigger rate varies dramatically when noise power varies. • Can choose conservative (low) thresholds when running data through LDAS, limited only by ability of LDAS to handle large trigger rate; cut tighter in EventTool post-processing. AJW, Caltech, LIGO Project

39. Search code triggers vs timefor Z-M waveform injected at 75 seconds * With signal; o without signal injected. NO VETOES APPLIED. Vetoes get rid of most of these triggers! SN at 0.1 pc (ouch!) 0.2 pc 1.0 pc Trigger “power”  threshold slope slope slope Time  tfclusters tfclusters tfclusters AJW, Caltech, LIGO Project

40. Efficiency for injected signals • In this example, tfclusters is run on S1 playground data, with many injections of SG-554 Hz bursts for each amplitude. • Efficiency (at fixed threshold) is obtained by averaging over many injections at different times in playground. • Deadtime due to vetos not counted in efficiency. • Can also evaluate triple coincidence efficiency, assuming optimal response of all 3 detectors (unrealistic) – black curve. • Note that power (on which we threshold) tracks input peak strain amplitude well (true for all 3 ETG’s). AJW, Caltech, LIGO Project

41. Tuning ETG thresholds • Left: efficiency for triple-coincident-detection of SG burst with different peak strains, versus background event rate, as threshold is varied • Right: upper limit figure of merit: UL = (2.44+sqrt(b)) / livetime / efficiency • Threshold chosen near UL minimum (15 in this case). • This is for tfclusters; slope is similar in character. • Repeat for a variety of different simulated waveforms; choose a threshold that works ok for all of them. L. Cadonati AJW, Caltech, LIGO Project

42. Sine-Gaussians - efficiencies tfclusters tfclusters slope tfclusters L. Cadonati AJW, Caltech, LIGO Project

43. 1 ms Gaussians slope tfclusters L. Cadonati AJW, Caltech, LIGO Project

44. Comparison between E7 and S1 L. Cadonati AJW, Caltech, LIGO Project

45. Applying vetos AJW, Caltech, LIGO Project

46. Event Tool Veto Channels • LSC-AS_I • LSC-REFL_Q • LSC-REFL_I • LSC-POB_Q • LSC-POB_I • LSC-MICH_CTRL • LSC-PRC_CTRL • LSC-MC_L • LSC-AS_DC • LSC-REFL_DC • IOO-MC_F • IOO-MC_L • PSL-FSS_RCTRANSPD_F • PSL-PMC_TRANSPD_F • Run DMT monitors glitchMon and absGlitch on many different channels • Focus on IFO channels • PEM channels not observed to be useful • Look for channels and thresholds that: • are correlated in time with GW channel glitches • significantly reduce single-IFO background burst rate, while • producing minimal deadtime AJW, Caltech, LIGO Project

47. Vetoes from absGlitch absGlitch first filters the time series. (Here, 30 Hz HP.) Finds times when signal crosses fixed threshold. Calculates size and duration, recorded to database. AJW, Caltech, LIGO Project

48. Efficacy of vetoes in E7 – L1 PSL glitch is in time with AS_Q; really cleans up L1. broad tail of events is cleaned up by L1 veto. L1 had lots of PSL glitching, so bulk of histogram is affected. S. Ballmer AJW, Caltech, LIGO Project

49. Efficacy of vetoes in E7 – H2 broad tail of events is cleaned up by L1 veto. H2 was much cleaner to start with, so only tail is removed. MICH glitch some use at H2. S. Ballmer AJW, Caltech, LIGO Project

50. S1 - H1 veto efficiency / deadtime Effect of REFL_I veto improves for larger power tfcluster triggers! H1:LSC-POB_I H1:LSC-PRC_CTRL H1:LSC-REFL_I H1:LSC-REFL_Q S. Ballmer AJW, Caltech, LIGO Project