Data quality studies for burst analysis of Virgo data acquired during Weekly Science Runs

被引:4
作者
Acernese, F. [1 ]
Amico, P.
Alshourbagy, M.
Antonucci, F.
Aoudia, S.
Astone, P.
Avino, S.
Babusci, D.
Ballardin, G.
Barone, F.
Barsotti, L.
Barsuglia, M.
Bauer, Th S.
Beauville, F.
Bigotta, S.
Birindelli, S.
Bizouard, M. A.
Boccara, C.
Bondu, F.
Bosi, L.
Bradaschia, C.
Braccini, S.
van den Brand, F. J.
Brillet, A.
Brisson, V.
Buskulic, D.
Calloni, E.
Campagna, E.
Carbognani, F.
Cavalier, F.
Cavalieri, R.
Cella, G.
Cesarini, E.
Chassande-Mottin, E.
Christensen, N.
Corda, C.
Corsi, A.
Cottone, F.
Clapson, A-C
Cleva, F.
Coulon, J-P
Cuoco, E.
Dari, A.
Dattilo, V.
Davier, M.
del Prete, M.
De Rosa, R.
Di Fiore, L.
Di Virgilio, A.
Dujardin, B.
机构
[1] Ist Nazl Fis Nucl, Sez Napoli, I-80125 Naples, Italy
[2] Univ Naples Federico II, Naples, Italy
[3] Univ Salerno, Fisciano, SA, Italy
[4] Ist Nazl Fis Nucl, Sez Perugia, I-06100 Perugia, Italy
[5] Univ Perugia, I-06100 Perugia, Italy
[6] Ist Nazl Fis Nucl, Sez Pisa, Pisa, Italy
[7] Univ Pisa, I-56100 Pisa, Italy
[8] Ist Nazl Fis Nucl, Sez Rome, Rome, Italy
[9] Univ Roma La Sapienza, Rome, Italy
[10] Observ Cote Azur, Dept Artemis, F-06304 Nice 4, France
[11] Ist Nazl Fis Nucl, Lab Nazl Frascati, I-00044 Frascati, Rm, Italy
[12] EGO, Cascina, PI, Italy
[13] Univ Paris 11, CNRS, IN2P3, LAL, F-91405 Orsay, France
[14] NIKHEF, NL-1009 DB Amsterdam, Netherlands
[15] Vrije Univ Amsterdam, NL-1081 HV Amsterdam, Netherlands
[16] Univ Savoie, CNRS, IN2P3, LAPP, Annecy Le Vieux, France
[17] ESPCI, Paris, France
[18] Ist Nazl Fis Nucl, Sez Firenze Urbino, Sesto Fiorentino, Italy
[19] Univ Florence, I-50121 Florence, Italy
[20] Univ Urbino, I-61029 Urbino, Italy
[21] LMA, Lyon, France
关键词
D O I
10.1088/0264-9381/24/19/S05
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
Virgo started collecting science data during weekends in order to not interfere with commissioning activities. The goal of Weekly Science Runs is to ease the transition between commissioning periods and data taking periods, in addition to providing data sets exploiting the stationary behavior of the detector. The detection of gravitational wave (GW) bursts emitted by core collapse of supernovae is one of the most difficult tasks for the GW community due to the fact that there are uncertainties in the exact shape of the waveforms, as we do not have complete models. A major task for this kind of detection effort is the cleaning of the event triggers found by the detection pipelines, namely the removal of accidental transient signals due to noise source events. In order to clean our data from false GW events, we need to define a strategy for data quality cut and veto of auxiliary and environmental monitoring channels. In this paper we report on the analysis we performed on data acquired during Weekly Science Runs to explore and define the data quality cut and veto studies for burst analysis.
引用
收藏
页码:S415 / S422
页数:8
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