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Because of the electromagnetic radiation produced during the merger, compactbinary coalescences with neutron stars may result in multi-messengerobservations. In order to follow up on the gravitational-wave signal withelectromagnetic telescopes, it is critical to promptly identify the propertiesof these sources. This identification must rely on the properties of theprogenitor source, such as the component masses and spins, as determined bylow-latency detection pipelines in real time. The output of these pipelines,however, might be biased, which could decrease the accuracy of parameterrecovery. Machine learning algorithms are used to correct this bias. In thiswork, we revisit this problem and discuss two new implementations of supervisedmachine learning algorithms, K-Nearest Neighbors and Random Forest, which areable to predict the presence of a neutron star and post-merger matter remnantin low-latency compact binary coalescence searches across different searchpipelines and data sets. Additionally, we present a novel approach forcalculating the Bayesian probabilities for these two metrics. Instead of metricscores derived from binary machine learning classifiers, our scheme is designedto provide the astronomy community well-defined probabilities. This woulddeliver a more direct and easily interpretable product to assistelectromagnetic telescopes in deciding whether to follow up ongravitational-wave events in real time.

Bayesian real-time classification of multi-messenger electromagnetic and gravitational-wave observations