First results from the S1 Science Run Searches for Burst...

G. Gonzalez, Lousiana State University 1LIGO-G030142-00-Z

First results from the S1 Science RunSearches for Burst and Inspiral

Gravitational Waves

Gabriela GonzálezLouisiana State University

On behalf of the LIGO Scientific Collaborationhttp://www.ligo.org

Moriond 2003Gravitational Waves and Experimental Gravity

Les Arcs , France (March 22-29, 2003)


LIGO Sensitivity for S1LIGO Sensitivity for S1

LIGOS1 Run-----------“First

Upper Limit Run”

!Aug – Sept 2002!17 days


InIn--Lock Data Summary from S1Lock Data Summary from S1Red lines: integrated up time Green bands (w/ black borders): epochs of lock

•August 23 – September 9, 2002: 408 hrs (17 days).•H1 (4km): duty cycle 57.6% ; Total Locked time: 235 hrs •H2 (2km): duty cycle 73.1% ; Total Locked time: 298 hrs •L1 (4km): duty cycle 41.7% ; Total Locked time: 170 hrs

•Double coincidences: •L1 && H1 : duty cycle 28.4%; Total coincident time: 116 hrs •L1 && H2 : duty cycle 32.1%; Total coincident time: 131 hrs •H1 && 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

H1: 235 hrs H2: 298 hrs L1: 170 hrs 3X: 95.7 hrs


Non-Stationarity and Epoch Veto

Strategy:Veto certain epochs based on excessive BLRMS noise in some bandsCut: 10σ in 320-400Hz ; 3σ cut in 400-600, 600-1600, 1600-3000 Hz; σ=68-percentile

Band-Limited RMS(BLRMS)

(6 min segments)Non-stationary noiseHere shown for S1:Hanford-4km (H1)

Cut for 600-1600 Hz bandCut for 1600-3000 Hz band


Big Glitches in H1

Found by inspiral searchcode with SNR=10.4

These occurred ~4 timesper hour during S1

REFL_I channel (frequency noise or common mode length) has a very clear transient

Use glitchMon to generate veto triggers


Veto Safety

Have to be sure there aren’t couplings between channels which would cause a real gravitational wave to veto itself !

Look at large injections

No sign of signal in H1:LSC-REFL_I for inspiral injections; some signs for bursts injections. Burst group does not use REFL_I veto.

Best veto channel for L1 (AS_I) was disallowed because there was a small coupling: neither burst or inpiral groups used any vetos for L1.


Gravitational wave burst searchesBurst Working Group

Elements of the analysis:

• Identification of detector events» Event Trigger Generation and coincidence → observed number of events Nobs

• Estimation of expected contribution from background» Time shift analysis → expected number of background events Nb

• Estimation of efficiency» Simulations → efficiency as a function of signal amplitude ε(h0)

• Determination of live-time T» Triple-time subject to vetos

• Result: Event Rate = I(Nobs,Nb,p)/ε(h0)T» I(Nobs,Nb,p): Interval in expected number of foreground events (Feldman & Cousins)

with confidence p.We expect an upper limit, but this method allows a detection.

Have to be careful with: • Systematic error estimation and propagation

» Calibrations, background estimation, efficiency• Parameter tuning: all done in playground dataset.


Burst Analysis Pipeline

Dataqualitycheck

Candidate Event TriggersLDAS

(LIGO Data Analysis System)Burst Analysis Algorithms

Diagnostics TriggersDMT

(Data Monitor Tool)Glitch Analysis Algorithms

GW/VetoanticoincidenceEvent Analysis Tools

IFO1events

Multi-IFO coincidence

and clustering

IFO 1Strain Data

IFO 1 Auxiliary dataFrom diagnostics channels

(non GW)

Interpretation:Quantify Upper Limit

Quantify Efficiency (via simulations)

IFO3events

IFO2eventsImplemented in LIGO Science run 1 (S1)

3 interferometers: LLO-4k LHO-4k LHO-2k


TFCLUSTERS: An Event Trigger Generator

Amplitude Spectrogram• 360 seconds of data• 16384 Hz sample rate• 8 Hz resolution• 125 ms time slice• Rectangular window• No overlap Thresholded Spectrogram

• Fit each frequency bin to a Rice distribution• Determine 99% amplitude threshold for each

frequency• “Black pixels” exceed the threshold

Black Pixel Clusters• Find contiguous clusters greater than or equal to 5

pixels• Apply set of rules to group nearby clusters• Report cluster properties:

start time durationfrequency bandwidthsize power

A different search was done with the SLOPE algorithm


Data Quality

• Most dramatic non-stationarity is removed by the epoch veto.

• Single-ifo vetoes would have made remaining data from L1, H1 yield histograms that are close to Gaussian.

• Declining to use single-ifo vetoesleaves some obvious non-Gaussian tails. Still, the overall event rates are not dramatically affected.


Efficiency

! Use Gaussians and Sine Gaussians

!Fit smooth sigmoid curves to efficiency measured above threshold.

TFCLUSTERS


Preliminary results

• Identification of detector events» Event Trigger Generation and coincidence TF clusters: 1.9/day

• Estimation of expected contribution from background» Time shift analysis

• Estimation of efficiency

» Simulations : with 50% efficiency, 1 msec, 1 10-17 Gaussian burst (optimal polarization, arrival direction) . Better for 554 Hz sine gaussians: 3 10-18

» Determination of live-time T : 35.5 hours


• Able to exclude gravitational wave bursts of peak strength h above rate r

• Upper limit in strain compared to prior (cryogenic bar) results:

» S1: h < 5 x 10-17 - this result» IGEC 20001 : h < 1 x 10-17

» Astone et al.2 2001: h ~ 2 x 10-18

• Upper limit in rate constrained by observation time:

» S1: 17d - this result» IGEC - 90d (2X coinc.), 260d (3X coinc.)» Astone - 90d

Excluded Regionat 90% upper

limit confidence bound

1Int.J.Mod.Phys. D9 (2000) 2372Class.Quant.Grav. 19 (2002) 5449

Result:Rate-strength diagram

Preliminary


Coalescing BinariesInspiral Sources Working Group

Three source targets:

Neutron star binaries (1-3 Msun)Neutron star search

completeBlack hole binaries (> 3 Msun)

Black hole search will be done in next science run, S2

MACHO binaries (0.5-1 Msun)MACHO search under way

11994 data, Allen et al., Phys.Rev.Lett. 83 (1999) 1498

S1:


Optimal Filtering Using FFTs

•Transform data to frequency domain : •Calculate template in frequency domain : •Combine, weighting by power spectral density of noise:

•Then inverse Fourier transform gives you the filter outputat all times:

•Find maxima of over arrival time and phase•Characterize event by signal-to-noise ratio, ρ

)(~ fh)(~ fs

|)(|)(~)(~ *

fSfhfs

h

dfefS

fhfstz tfi

h

π2

0

*

|)(|)(~)(~

4)( ∫∞

=

|)(| tz


Template Bank

•Calculatedbased on L1noise curve

•Templatesplaced formaximummismatchof δ = 0.03

2110 templatesSecond-orderpost-Newtonian


“Chi-Squared Veto”

•Any large glitch in the data can lead to a large filter output•The essence of a “chirp” is that the signal power is distributed over frequencies in a particular way•Divide template into sub-bands and calculate a χ2-like quantity:

•Correct for large signals which fall between points in the template bank:

•We use p = 8 and make the cut α2 ≤ 5

∑=

−=p

ll ptztztr

1

22 /)()()(

( )ptrt /1)()( 2222 δρα +=


Effect of Vetoeson Playground Data

Disallo

wed Deadtime = 0.3%


Analysis PipelineL1 triggers

Epoch veto

H1 triggers

Epoch vetoREFL_I veto

L1 distance <20 kpc?

Seen in H1 with consistent time and

total mass?

Event candidatesSNR from L1 SNR from H1

Only L1operating

Bothoperating Only H1

operating

Discard

Yes No

Yes No


Statistical Method

•Expected rate in Milky Way is very low !•Philosophy: concentrate on getting best upper limit⇒ Use all four categories of event candidates

»Yields 289 hours of observation time,vs. 116 hours of simultaneous L1+H1 operation

•Add together SNR distributions from each category•Use the “maximum-SNR statistic”

»Because it’s hard to know a priori where one should set a threshold»Useful since candidate events are so sharply peaked at low SNR»Yields a frequentist upper limit, R(90%) < 2.3 / ( ε T )

Efficiency of analysis pipeline above observed max SNR Observation time


Population Monte Carlo

• Mass distribution derived from population synthesis models

• Spatial distribution out to 200kpc including Milky Way, LMC and SMC

• LMC and SMC contribute about 12% of a Milky Way equivalent Galaxy

• Signals injected into data stream and used to determine efficiency of pipeline to detection of BNS population

H1

L1


Inspiral Search: preliminary result

•Use triggers from H 4km and L 4km interferometers: » T = 295.3 hours

» Max SNR observed: 15.9 An event seen in L1 only, with effective distance = 95 kpcThere are no event candidates in the coincidence category

» Monte Carlo simulation efficiency for SNR=15.9: ε = 35%

» 90% confidence limit = 2.3 / (ε T)

•Limit on binary neutron star coalescence rate (preliminary!):»R90% (Milky Way) < 2.3 / (0.35 x 295.3 hr) = 170 /yr

•Compare with:»26X lower than best published observational limit -- 40m prototype at Caltech:

R90% (Milky Way) < 4400 /yr» Many orders of magnitude higher than expected galactic rate: ~10-6 - 10-5 /yr

(Kalogera et al)


S2 run began 14 February• Will last through 14

April• Sensitivity is ~10x

better than S1• Duration will be ~ 4x

longer• Prospects:

» Bursts: 4X lower glitch rate, tighter coincidence testsbetter tuning to sweet spot

» Inspirals: reach will exceed 1Mpc --includes Andromeda, M33!

LIGO Science Has Started !

First results from the S1 Science Run Searches for Burst...

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