AJW, Caltech, LIGO Project 1LIGO-G02XXXX-00-Z

Aspects of the LIGO burst search analysis

Alan Weinstein, CaltechFor the LSC Burst ULWGLIGO 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

AJW, Caltech, LIGO Project 2LIGO-G02XXXX-00-Z

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 3LIGO-G02XXXX-00-Z

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 4LIGO-G02XXXX-00-Z

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 5LIGO-G02XXXX-00-Z

Burst search goals

AJW, Caltech, LIGO Project 6LIGO-G02XXXX-00-Z

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 7LIGO-G02XXXX-00-Z

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 8LIGO-G02XXXX-00-Z

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 9LIGO-G02XXXX-00-Z

Analysis Pipeline

AJW, Caltech, LIGO Project 10LIGO-G02XXXX-00-Z

Analysis Pipeline

EventTool

• 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 11LIGO-G02XXXX-00-Z

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.

EventTool

AJW, Caltech, LIGO Project 12LIGO-G02XXXX-00-Z

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!

EventTool

AJW, Caltech, LIGO Project 13LIGO-G02XXXX-00-Z

Post-LDAS/DMT event processing

• Result of LDAS job on GW channel (with or without simulated injection) is an event trigger in metaDatabase table sngl_burst, with start time, duration, frequency band, SNR, amplitude…

• Result of DMT job on veto channel is a veto trigger in metaDatabase table gds_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 14LIGO-G02XXXX-00-Z

LIGO S1 data

AJW, Caltech, LIGO Project 15LIGO-G02XXXX-00-Z

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 H1 duty cycle 28.4%; Total coincident time: 116 hrs Double coincidence: L1 and H2 duty cycle 32.1%; Total coincident time: 131 hrs Double Coincidence: H1 and H2 duty cycle 46.1%; Total coincident time: 188 hrs

Triple Coincidence: L1, H1, and H2 duty cycle 23.4% ;Total coincident time: 95.7 hrs

Triple coincidence

AJW, Caltech, LIGO Project 16LIGO-G02XXXX-00-Z

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 17LIGO-G02XXXX-00-Z

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 18LIGO-G02XXXX-00-Z

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 19LIGO-G02XXXX-00-Z

Veto on BLRMS?

Histograms: 6-min segment BLRMS.

vertical lines at 1 and 3

Veto segments beyond, eg, 3?

(L. Cadonati)

AJW, Caltech, LIGO Project 20LIGO-G02XXXX-00-Z

Calibration of detector responseXext 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 21LIGO-G02XXXX-00-Z

Monitoring calibration lines

51.3 972.8

Veto epochs with low ?

AJW, Caltech, LIGO Project 22LIGO-G02XXXX-00-Z

Event trigger generators

AJW, Caltech, LIGO Project 23LIGO-G02XXXX-00-Z

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 24LIGO-G02XXXX-00-Z

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 25LIGO-G02XXXX-00-Z

Data flow in LDAS

User pipeline request

frameAPI datacondAPI mpiAPI wrapperAPI LAL code eventmonAPI metadataAPI metaDB

AJW, Caltech, LIGO Project 26LIGO-G02XXXX-00-Z

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 27LIGO-G02XXXX-00-Z

Burst simulations

AJW, Caltech, LIGO Project 28LIGO-G02XXXX-00-Z

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 29LIGO-G02XXXX-00-Z

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

ZM SN burst

chirp

merger

ringdown

ZM SN burstsBandwidth vs duration

burst waveforms: t-f character

AJW, Caltech, LIGO Project 30LIGO-G02XXXX-00-Z

Ad-hoc signals: (Sine)-Gaussians

These have no astrophysical significance;

But are well-defined in terms of waveform, duration, bandwidth, amplitude

SG 554,

Q = 9

AJW, Caltech, LIGO Project 31LIGO-G02XXXX-00-Z

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

damped sinusoid (“ringdown”) waveform

AJW, Caltech, LIGO Project 32LIGO-G02XXXX-00-Z

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 33LIGO-G02XXXX-00-Z

Zwerger Muller SN waveforms

The 78 ZM waveforms differ in

• initial angular momentum (A parameter), governing degree of differential rotation

• initial rotational energy (B, or = Erot/Epot). A,B roughly govern how many bounce peaks

•Within each (AB) set, the adiabatic index r is 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 34LIGO-G02XXXX-00-Z

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 35LIGO-G02XXXX-00-Z

ZM supernova

ringdown Hermite-gaussian

chirp

Menagerie of burst waveformsburied in E2 noise, including calibration/TF

AJW, Caltech, LIGO Project 36LIGO-G02XXXX-00-Z

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 37LIGO-G02XXXX-00-Z

Tuning thresholds and evaluating efficiency

AJW, Caltech, LIGO Project 38LIGO-G02XXXX-00-Z

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 39LIGO-G02XXXX-00-Z

Search code triggers vs timefor Z-M waveform injected at 75 seconds

SN at 0.1 pc (ouch!) 0.2 pc 1.0 pc

slope

tfclusters

slope slope

tfclusters

tfclusters

Time

Tri

gger

“po

wer

”

threshold

* With signal; o without signal injected. NO VETOES APPLIED. Vetoes get rid of most of these triggers!

AJW, Caltech, LIGO Project 40LIGO-G02XXXX-00-Z

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 41LIGO-G02XXXX-00-Z

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 42LIGO-G02XXXX-00-Z

Sine-Gaussians - efficiencies

tfclusters

tfclusters

tfclusters

slope

L. Cadonati

AJW, Caltech, LIGO Project 43LIGO-G02XXXX-00-Z

1 ms Gaussians

tfclusters slope

L. Cadonati

AJW, Caltech, LIGO Project 44LIGO-G02XXXX-00-Z

Comparison between E7 and S1

L. Cadonati

AJW, Caltech, LIGO Project 45LIGO-G02XXXX-00-Z

Applying vetos

AJW, Caltech, LIGO Project 46LIGO-G02XXXX-00-Z

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

EventTool

AJW, Caltech, LIGO Project 47LIGO-G02XXXX-00-Z

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 48LIGO-G02XXXX-00-Z

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 49LIGO-G02XXXX-00-Z

Efficacy of vetoes in E7 – H2

MICH glitch some use at H2.broad tail of events is cleaned up by L1 veto.

H2 was much cleaner to start with, so only tail is removed.

S. Ballmer

AJW, Caltech, LIGO Project 50LIGO-G02XXXX-00-Z

S1 - H1 veto efficiency / deadtime

H1:LSC-POB_I

H1:LSC-PRC_CTRL

H1:LSC-REFL_I

H1:LSC-REFL_Q

Effect of REFL_I veto improves for larger power tfcluster triggers!

S. Ballmer

AJW, Caltech, LIGO Project 51LIGO-G02XXXX-00-Z

S1-H2 veto efficiency / deadtime

H2:LSC-POB_I

H2:LSC-PRC_CTRL

H2:LSC-REFL_I

H2:LSC-REFL_Q

None much better than random…

S. Ballmer

AJW, Caltech, LIGO Project 52LIGO-G02XXXX-00-Z

Veto for S-H1

Veto: H1:LSC-REFL_I filtered at 30 Hz HP. Threshold=11.6Tuned to remove large glitches every ~ 20 minutes, very evident on REFL_I.Except for these large glitches, the veto is random-behaved. This is visible in the power histogram for TFCLUSTER below.

AJW, Caltech, LIGO Project 53LIGO-G02XXXX-00-Z

Vetos for S1 - L1, H2

Veto: L1:LSC-AS_DC filtered at 30 Hz HP. Threshold=21

H2:LSC-PRC_CTRL filtered at 30 Hz HP. Threshold=52.15.This veto is barely time-correlated with GW channel; not used.

AJW, Caltech, LIGO Project 54LIGO-G02XXXX-00-Z

Cuts on coincident events

AJW, Caltech, LIGO Project 55LIGO-G02XXXX-00-Z

Cuts on coincident events

Require temporal coincidence tfclusters ETG (currently) finds clusters in t-f plane

with 1/8 second time bins. Can’t establish coincidences to better than that

granularity. Currently, coincidence window is 0.5 seconds. slope ETG has no such limitation; currently,

coincidence window is 0.05 seconds. More work required to tighten this to a fraction of

10 msec light travel time between LHO/LLO

AJW, Caltech, LIGO Project 56LIGO-G02XXXX-00-Z

Frequency test of temporal coincidences

• In addition to temporal coincidence of events, we require that tfclusters give a central frequency at the two IFOs that are within 500 Hz of each other.

• Frequency information not yet available in slope.

AJW, Caltech, LIGO Project 57LIGO-G02XXXX-00-Z

Coherence test for coincident bursts

Distribution of coherence (300-1200 Hz)

For H2-L1 coincidences

Distribution, in presence of

simulated faint bursts

With 10 msec time delay

With no time delaySimulated faint bursts are coherent

Between H2-L1 (E7), in (300-1200 Hz) band

Cross-correlation (coherence) statistic

(CCS).

AJW, Caltech, LIGO Project 58LIGO-G02XXXX-00-Z

CCS Efficiency vs fake rate reduction

• Vary threshold on CCS.

• For each threshold,evaluate efficiency vs fake rate reduction.

• Very effective, even for delayed coincidences.

• Next step: use CCS to derive estimate of time delay.

• Note: time delay in, eg, tfclusters, can not be measured to better than 1/8 sec.

• CCS not yet implemented in burst search.

No time delay

10 msec time delay between H2,L1

AJW, Caltech, LIGO Project 59LIGO-G02XXXX-00-Z

Averaging over the sky

AJW, Caltech, LIGO Project 60LIGO-G02XXXX-00-Z

Averaging over source direction and polarization

Generate single-IFO efficiency curve vs signal amplitude, assuming optimal direction / polarization.

» Different for each data epoch» Different for each IFO» Different for each waveform.» Different for each ETG / threshold

Assuming source population is isotropic, determine single-IFO efficiency versus amplitude, averaged over source direction and polarization, using single-ifo response function.

This is easily accomplished with simple Monte Carlo; no need to go back to detailed LDAS simulations.

But, this is wrong, if both polarizations are present, with different waveforms.

hRdddh ),,(cos)(

AJW, Caltech, LIGO Project 61LIGO-G02XXXX-00-Z

Coincidence efficiency, averaged over source direction & polarization

Efficiency for coincidences is the product of single-IFO efficiencies, evaluated with the appropriate response to GWs of a given source direction / polarization.

Once again, this can be easily accomplished with simple Monte Carlo, using knowledge of detector position and orientation on Earth. No need to go back to detailed LDAS simulations.

Must estimate any additional loss of efficiency due to post-coincidence event processing.

Again, a different plot for each data epoch, waveform, ETG/threshold,set of coincident detectors.

Finally, obtain event rate upper limit, divide by efficiency curve to get rate vs strain amplitude. hRhRdddhc bbaa ),,(),,(cos)(

AJW, Caltech, LIGO Project 62LIGO-G02XXXX-00-Z

“Representative” S1 results

AJW, Caltech, LIGO Project 63LIGO-G02XXXX-00-Z

Results from S1 playground(preliminary!)

triple coincidencesTFCLUSTER

TFCLUSTER w/ veto

SLOPE SLOPE w/ veto

Live Time (seconds) 30240 29128 30240 29128

Triple coincidences (no clustering) 91 38 0 0

clustered 41 27 0 0

frequency match cut 0 0 0 0

background1.64 +/- 0.01

0.61 +/- 0.01 0.18 +/- 0.004

0.03 +/- 0.001

90% upper limit (Feldman Cousins) on events in the playground

1.31 1.87 2.26 2.41

90% upper limit (Feldman Cousins) on the rate (mHz)

0.043 0.064 0.075 0.083

L. CadonatiBest we can hope for: 2.44/29128 = 0.08 mHz.

Embargoed!TO: LIGO Scientific Collaboration

FROM: R. Weiss November 12, 2002

CONCERNING: Release of scientific data from collaboration runs

…

In keeping with our responsibility, there should be no release of scientific data and astrophysical results until the

Collaboration has agreed on the validity of the methods and the results…

AJW, Caltech, LIGO Project 64LIGO-G02XXXX-00-Z

S1 triple-coincidences

Rate upper limits.

Best we can do is observe 0

triple-coincident events:

~> 2.44/223560 sec

~ 0.01 mHz

Embargoed!

TO: LIGO Scientific Collaboration

FROM: R. Weiss November 12, 2002

CONCERNING: Release of scientific data from collaboration runs

…

In keeping with our responsibility, there should be no release of scientific data and astrophysical results until the

Collaboration has agreed on the validity of the methods and the results…

AJW, Caltech, LIGO Project 65LIGO-G02XXXX-00-Z

Rate upper limit vs strengthfrom LIGO S1 triple-coincidence

MUCH more work to do! Complete “Nov 1” report Continue optimization of vetos,

epoch veto Continue optimization of ETGs,

thresholds Optimize post-coincidence

consistency cuts Incorporate time-varying

calibration Consider more waveforms Include GEO! Consider 2/3 , 3/4, …

coincidences. …

Representative

example

excluded