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  • Astrophysical or Terrestrial: Machine Learning Classification of Gravitational-wave Candidates Using Multiple-search Information

This content will become publicly available on January 1, 2027

Title: Astrophysical or Terrestrial: Machine Learning Classification of Gravitational-wave Candidates Using Multiple-search Information
Abstract Low-latency gravitational-wave alerts provide the greater multi-messenger community with information about the candidate events detected by the International Gravitational-Wave Network. Prompt release of data products such as the sky localization, false alarm rate, andpastrovalues allow astronomers to make informed decisions on which candidate gravitational-wave events merit target of opportunity follow-up. However, false alarms where a gravitational-wave candidate, or trigger, is the result of terrestrial noise, are an inherent part of gravitational-wave searches. In addition, with the presence of multiple gravitational-wave searches, different searches may have varying assessments of the significance of a given trigger. As a complement to quantities such aspastro, we provide a Machine Learning (ML) based approach to determining whether candidate events are astrophysical or terrestrial in nature. This ML classifier utilizes information provided by multiple low-latency search pipelines and downstream data products in its feature space, and is trained on a realistic real-time simulation campaign called the Mock Data Challenge (MDC). The Random Forest implementation has an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.96 and accuracy of 0.90 on the MDC training set compared topastrowhich had an AUC of 0.72 and an accuracy of 0.75. We tested on events from Advanced LIGO (aLIGO)’s, Advanced Virgo (AdVirgo)’s, and KAmioka GRAvitational-wave observatory (KAGRA)’s third observing run (O3) and got an AUC of 0.93 and accuracy of 0.86 compared topastrowhich had an AUC of 0.82 and accuracy of 0.75.  more » « less
Award ID(s):
2409481
PAR ID:
10681991
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Publisher / Repository:
Astronomical Society of the Pacific
Date Published:
Journal Name:
Publications of the Astronomical Society of the Pacific
Volume:
138
Issue:
1
ISSN:
0004-6280
Page Range / eLocation ID:
014503
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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