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Compact object mergers which produce both detectable gravitational waves andelectromagnetic emission can provide valuable insights into the neutron starequation of state, the tension in the Hubble constant, and the origin of ther-process elements. However, electromagnetic follow-up of gravitational wavesources is complicated by false positive detections, sources which do not emitlight, and the transient nature of the associated electromagnetic emission.GWSkyNet-Multi is a machine learning model that attempts to resolve theseissues by providing real-time predictions of the source of a gravitational wavedetection. The model uses information from Open Public Alerts (OPAs) releasedby LIGO-Virgo within minutes of a gravitational wave detection. GWSkyNet wasfirst introduced in Cabero et al. (2020) as a binary classifier and uses theOPA skymaps to classify sources as either astrophysical or as glitches. In thispaper, we introduce GWSkyNet-Multi, a new version of GWSkyNet which furtherdistinguishes sources as binary black hole mergers, mergers involving a neutronstar, or non-astrophysical glitches in the detectors. GWSkyNet-Multi is asequence of three one-versus-all classifiers trained using a class-balanced andphysically-motivated source mass distribution. Training on this data set, weobtain test set accuracies of 94% for BBH-versus-all, 94% for NS-versus-all,and 95% for glitch-versus-all. We obtain an overall accuracy of 93% using ahierarchical classification scheme. Furthermore, we correctly identify 36 ofthe 40 gravitational wave detections from the first half of LIGO-Virgo's thirdobserving run (O3a) and present predictions for O3b sources. We also brieflydiscuss the interpretability of our models. As gravitational wave detectionsincrease in number and frequency, GWSkyNet-Multi will be a powerful tool forprioritizing successful electromagnetic follow-up.

GWSkyNet-Multi: A Machine Learning Multi-Class Classifier for LIGO-Virgo Public Alerts