Machine learning to assess the astrophysical origin of gravitational waves triggers

In this work, we explore a possible application of a machine learning classifier for candidate events in a template-based search for gravitational-wave signals from various compact system sources. We analyze data from the O3a and O3b data acquisition campaign, during which the sensitivity of ground-based detectors is limited by real non-Gaussian noise transient. The state-of-the-art searches for such signals typically rely on the signal-to-noise ratio (SNR) and a chi-square test to assess the consistency of the signal with an inspiral template. In addition, a combination of these and other statistical properties are used to build “reweighted SNR” statistics. We evaluate a Random Forest classifier on a set of double-coincidence events identified using the multiband template analysis pipeline. The new classifier achieves a modest but consistent increase in event detection at low false positive rates relative to the standard search. Using the output statistics from the Random Forest classifier, we compute the probability of astrophysical origin for each event, denoted as p astro . This is then evaluated for the events listed in existing catalogs, with results consistent with those from the standard search. Finally, we search for new possible candidates using these new statistics, with p astro > 0.5 , obtaining a new subthreshold candidate (inverse false alarm rate = 0.05 ) event at g p s : 1240423628 .