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Incorporation of Statistical Data Quality Information into the GstLAL Search Analysis

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arxiv 2010.15282 v1 pith:DIP4RYI7 submitted 2020-10-28 gr-qc astro-ph.IM

Patrick Godwin , Reed Essick , Chad Hanna , Kipp Cannon , Sarah Caudill , Chiwai Chan , Jolien D. E. Creighton , Heather Fong
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Erik Katsavounidis Ryan Magee Duncan Meacher Cody Messick Soichiro Morisaki Debnandini Mukherjee Hiroaki Ohta Alexander Pace Iris de Ruiter Surabhi Sachdev Leo Tsukada Takuya Tsutsui Koh Ueno Leslie Wade Madeline Wade
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classification gr-qcastro-ph.IM
keywords gstlaldatainformationqualitygravitational-wavesearchstatisticaldiscuss
verification ladder T0 review T1 audit T2 compute T3 formal
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We present updates to GstLAL, a matched filter gravitational-wave search pipeline, in Advanced LIGO and Virgo's third observing run. We discuss the incorporation of statistical data quality information into GstLAL's multi-dimensional likelihood ratio ranking statistic and additional improvements to search for gravitational wave candidates found in only one detector. Statistical data quality information is provided by iDQ, a data quality pipeline that infers the presence of short-duration transient noise in gravitational-wave data using the interferometer's auxiliary state, which has operated in near real-time since before LIGO's first observing run in 2015. We look at the performance and impact on noise rejection by the inclusion of iDQ information in GstLAL's ranking statistic, and discuss GstLAL results in the GWTC-2 catalog, focusing on two case studies; GW190424A, a single-detector gravitational-wave event found by GstLAL and a period of time in Livingston impacted by a thunderstorm.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 12 citations worldwide. Full citation record

  1. Improving gravitational wave search sensitivity with TIER: Trigger Inference using Extended strain Representation

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    A machine learning classifier trained on the extended noise environment around gravitational wave candidates improves search sensitivity for heavy, unequal-mass black hole mergers by up to roughly 20 percent.

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