Astrophysical Source Classification and Distance Estimation for PyCBC Live

TL;DR

This paper introduces a rapid classification method for gravitational-wave sources during LIGO/Virgo's O3 run, enabling immediate follow-up observations by using search outputs to estimate source type and distance.

Contribution

It presents a novel low-latency classification technique based on chirp mass and redshift correction, improving real-time source identification during gravitational-wave detection.

Findings

Effective classification of simulated signals and real O3 events.

Improved distance estimation accounting for cosmological redshift.

Enhanced rapid follow-up capabilities for electromagnetic and neutrino observatories.

Abstract

During the third observing run (O3) of the Advanced LIGO and Advanced Virgo detectors, dozens of candidate gravitational-wave (GW) events have been catalogued. A challenge of this observing run has been the rapid identification and public dissemination of compact binary coalescence (CBC) signals, a task carried out by low-latency searches such as PyCBC Live. During the later part of O3, we developed a method of classifying CBC sources, via their probabilities of containing neutron star or black hole components, within PyCBC Live, in order to facilitate immediate follow-up observations by electromagnetic and neutrino observatories. This fast classification uses the chirp mass recovered by the search as input, given the difficulty of measuring the mass ratio with high accuracy for lower-mass binaries. We also use a distance estimate derived from the search output to correct for the bias in chirp mass due to the cosmological redshift. We present results for simulated signals, and for confirmed candidate events identified in low latency over O3.