A quantile regression neural network produces real-time credible intervals for chirp mass, mass ratio, and total mass of compact binary mergers, with coverage mostly above 90%, and these intervals serve as priors that reduce likelihood evaluations in parameter estimation by about 9%.
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A neural network for estimating compact binary coalescence parameters of gravitational-wave events in real time
A quantile regression neural network produces real-time credible intervals for chirp mass, mass ratio, and total mass of compact binary mergers, with coverage mostly above 90%, and these intervals serve as priors that reduce likelihood evaluations in parameter estimation by about 9%.