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Detector characterization for burst searches by resonant detectors

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Detector characterization for burst searches by resonant detectors. F. Salemi Italy, INFN and University of Ferrara for the IGEC2 Collaboration The 10th Gravitational Wave Data Analysis Workshop (GWDAW-10) December 14 th to 17 th , 2005 Brownsville, Texas. Outline. AURIGA Spectrum - PowerPoint PPT Presentation
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1 Detector characterization for burst searches by resonant detectors F. Salemi Italy, INFN and University of Ferrara for the IGEC2 Collaboration The 10th Gravitational Wave Data Analysis Workshop (GWDAW-10) December 14 th to 17 th , 2005 Brownsville, Texas.
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Page 1: Detector characterization for burst searches by resonant detectors

1

Detector characterization for burst searches by

resonant detectors

F. Salemi Italy, INFN and University of Ferrara

for the IGEC2 Collaboration

The 10th Gravitational Wave Data Analysis Workshop (GWDAW-10)December 14th to 17th, 2005 Brownsville, Texas.

Page 2: Detector characterization for burst searches by resonant detectors

2

Outline

• AURIGA Spectrum Vetoes Event Search MonteCarlo Software Injections

• Raw Data exchange: First step toward the Cross-validation of analyses Results produced by AURIGA Data Analysis

IGEC2 Shh

Event Search MonteCarlo: Time of arrival uncertainties

estimation Hardware Injections

GWDAW10 - December 14th, 2005 - Brownsville, Texas

Page 3: Detector characterization for burst searches by resonant detectors

3

700 800 900 1000 1100-14

-13

-12

-11

-10

-9

-8

-7

-6

Residual spurious lines

HV plate modes

Spuriousmodes(unidentified)

Main modes

Lo

g P

ow

er S

pec

tral

Den

sity

(V

2 /Hz)

Frequency (Hz)

AURIGA output: the modes

Typical recent spectrum averaged on 8 consecutive hours

Main modes = mixing of bar, transducer and electrical modes:

865, 914, 955 Hz

at thermal noise level

0 500 1000 1500-14

-12

-10

-8

-6

-4

Power lines

Low frequency suspensions

Lo

g P

ow

er S

pec

tral

Den

sity

(V

2 /Hz)

Frequency (Hz)

GWDAW10 - December 14th, 2005 - Brownsville, Texas

Page 4: Detector characterization for burst searches by resonant detectors

4

Vetoes (1)

GWDAW10 - December 14th, 2005 - Brownsville, Texas

Page 5: Detector characterization for burst searches by resonant detectors

5

Vetoes

600 700 800 900 1000 1100 1200

[ Hz ]

Anticoincidence vetoes X

Epoch vetoes X

Vetoing System

Anticoincidence veto: “WideBands” veto

Epoch Veto: “F7” veto

GWDAW10 - December 14th, 2005 - Brownsville, Texas

Page 6: Detector characterization for burst searches by resonant detectors

6

Candidate Events: run687 (July 2005)

[ days ]

SN

R

SNR

TObs N Events Rate[Hz]

271.3 hr (11.3 days) 64010 66 mHz

“Wide band” veto 11829 12 mHz

(2087 s vetoed out)

“F7” veto 11765 12 mHz

(896 s vetoed out)

GWDAW10 - December 14th, 2005 - Brownsville, Texas

Page 7: Detector characterization for burst searches by resonant detectors

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• Measurement of detection efficiency by injections of software signals in real data

• crucial to cross-validate detectors in a network

• Submillisecond arrival time resolution

• crucial to check travel speed of the gw and to locate the source

arrival time estimation for signals

Detection efficiencyFor signals

MonteCarlo results

GWDAW10 - December 14th, 2005 - Brownsville, Texas

Page 8: Detector characterization for burst searches by resonant detectors

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IGEC2 Raw Data ExchangeAll Credits to Gabriele Vedovato

ALLEGRO• Lockin with reference frequency @ 904 Hz• Quadrature & Phase components sampled @ 250 Hz• 2 Binary files (1 day size)• A day of data-taking: AL05262.raw (19 September, 2005)• A day with Hardware Injections: AL05202.raw (22 July, 2005)

NAUTILUS and EXPLORER• Binary files: raw data sampled @ 5 kHz• A day of data-taking for both detectors: 11 December, 2004

AURIGA analizing raw data from other bar detectors.The Followed Procedure:• (Upconversion to the original frequency, for ALLEGRO Data only)• Conversion to FRAME Format• Ad-hoc Calibration to comply with validated Shh curves• Analysis with AURIGA Data Analysis (ADA)

Scope• Cross-validation• Comparison of different algorithms and pipelines

GWDAW10 - December 14th, 2005 - Brownsville, Texas

Page 9: Detector characterization for burst searches by resonant detectors

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IGEC2 Shh (obtained by ADA)

ALLEGRO: September 2005

AURIGA : July 2005

EXPLORER: December 2004

NAUTILUS: December 2004

Sigma[1/Hz 1e22]

FWHM [Hz]

ALLEGRO 2.0 4.5

AURIGA 1.4 26

EXPLORER 2.9 3.4

NAUTILUS 2.4 3.6

GWDAW10 - December 14th, 2005 - Brownsville, Texas

Page 10: Detector characterization for burst searches by resonant detectors

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Event Search on all the bars

ETG:

δ-matched filter

TH: SNR>4.0

N Evt=3146

N Evt=4650

N Evt=1698

N Evt=2859

GWDAW10 - December 14th, 2005 - Brownsville, Texas

Page 11: Detector characterization for burst searches by resonant detectors

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Sofware Injections: Time errors

ALLEGRO: September 2005

AURIGA : July 2005

EXPLORER: December 2004

NAUTILUS: December 2004

SNR

Ter

r [

s ]

GWDAW10 - December 14th, 2005 - Brownsville, Texas

Page 12: Detector characterization for burst searches by resonant detectors

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Time-Frequency : hw injected events at 0.02 Hz

ALLEGRO Hardware Injections (22 July 2005)

Spectrum : noise [red] – signal [yellow]840 860 880 900 920 940 960

GWDAW10 - December 14th, 2005 - Brownsville, Texas

Page 13: Detector characterization for burst searches by resonant detectors

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Hardware Injections (2)

hw injected : phase error time distributionTphs = 1/(* snr) = 1 / (6.28 * 910 * 31.12)

hw injected : error time distribution (time differences out-in times)hw injected : snr distribution

GWDAW10 - December 14th, 2005 - Brownsville, Texas

Page 14: Detector characterization for burst searches by resonant detectors

Extra slides

Page 15: Detector characterization for burst searches by resonant detectors

Hardware InjectionsAL05202.raw (22 July 2005)

The "837 oscillator" is set for brst count = 1freq = 899 Hzampl = 0.100 Vpp @ displaytrig src = ratetrig rate = 0.02 Hz

Page 16: Detector characterization for burst searches by resonant detectors

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Time of arrival error estimate (Monte Carlo)


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