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GWSkyNet-Multi is a machine learning model developed for classification of candidate gravitational-wave events detected by the LIGO and Virgo observatories. The model uses limited information released in the low-latency Open Public Alerts to produce prediction scores indicating whether an event is a merger of two black holes, a merger involving a neutron star, or a non-astrophysical glitch. This facilitates time sensitive decisions about whether to perform electromagnetic follow-up of candidate events during LIGO-Virgo-KAGRA (LVK) observing runs. However, it is not well understood how the model is leveraging the limited information available to make its predictions. As a deep learning neural network, the inner workings of the model can be difficult to interpret, impacting our trust in its validity and robustness. We tackle this issue by systematically perturbing the model and its inputs to explain what underlying features and correlations it has learned for distinguishing the sources. We show that the localization area of the 2D sky maps and the computed coherence versus incoherence Bayes factors are used as strong predictors for distinguishing between real events and glitches. The estimated distance to the source is further used to discriminate between binary black hole mergers and mergers involving neutron stars. We leverage these findings to show that events misclassified by GWSkyNet-Multi in LVK's third observing run have distinct sky area, coherence factor, and distance values that influence the predictions and explain these misclassifications. The results help identify the model's limitations and inform potential avenues for further optimization., Comment: 22 pages, 11 figures, submitted to ApJ

Compact object mergers which produce both detectable gravitational waves and electromagnetic emission can provide valuable insights into the neutron star equation of state, the tension in the Hubble constant, and the origin of the r-process elements. However, electromagnetic follow-up of gravitational wave sources is complicated by false positive detections, sources which do not emit light, and the transient nature of the associated electromagnetic emission. GWSkyNet-Multi is a machine learning model that attempts to resolve these issues by providing real-time predictions of the source of a gravitational wave detection. The model uses information from Open Public Alerts (OPAs) released by LIGO-Virgo within minutes of a gravitational wave detection. GWSkyNet was first introduced in Cabero et al. (2020) as a binary classifier and uses the OPA skymaps to classify sources as either astrophysical or as glitches. In this paper, we introduce GWSkyNet-Multi, a new version of GWSkyNet which further distinguishes sources as binary black hole mergers, mergers involving a neutron star, or non-astrophysical glitches in the detectors. GWSkyNet-Multi is a sequence of three one-versus-all classifiers trained using a class-balanced and physically-motivated source mass distribution. Training on this data set, we obtain 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 a hierarchical classification scheme. Furthermore, we correctly identify 36 of the 40 gravitational wave detections from the first half of LIGO-Virgo's third observing run (O3a) and present predictions for O3b sources. We also briefly discuss the interpretability of our models. As gravitational wave detections increase in number and frequency, GWSkyNet-Multi will be a powerful tool for prioritizing successful electromagnetic follow-up., Comment: 12 pages, 7 figures, submitted to ApJ