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We develop a robust and self-consistent framework to extract and classifygravitational wave candidates from noisy data, for the purpose of assisting inreal-time multi-messenger follow-ups during LIGO-Virgo-KAGRA's fourth observingrun~(O4). Our formalism implements several improvements to the low latencycalculation of the probability of astrophysical origin~(\PASTRO{}), so as tocorrectly account for various factors such as the sensitivity change betweenobserving runs, and the deviation of the recovered template waveform from thetrue gravitational wave signal that can strongly bias said calculation. Wedemonstrate the high accuracy with which our new formalism recovers andclassifies gravitational wave triggers, by analyzing replay data from previousobserving runs injected with simulated sources of different categories. We showthat these improvements enable the correct identification of the majority ofsimulated sources, many of which would have otherwise been misclassified. Wecarry out the aforementioned analysis by implementing our formalism through the\GSTLAL{} search pipeline even though it can be used in conjunction withpotentially any matched filtering pipeline. Armed with robust andself-consistent \PASTRO{} values, the \GSTLAL{} pipeline can be expected toprovide accurate source classification information for assisting inmulti-messenger follow-up observations to gravitational wave alerts sent outduring O4.

When to Point Your Telescopes: Gravitational Wave Trigger Classification for Real-Time Multi-Messenger Followup Observations