Gravitational-wave selection effects using neural-network classifiers
- URL: http://arxiv.org/abs/2007.06585v2
- Date: Tue, 17 Nov 2020 17:55:53 GMT
- Title: Gravitational-wave selection effects using neural-network classifiers
- Authors: Davide Gerosa, Geraint Pratten, Alberto Vecchio
- Abstract summary: We train a series of neural-network classifiers to predict the LIGO/Virgo detectability of gravitational-wave signals from compact-binary mergers.
We include the effect of spin precession, higher-order modes, and multiple detectors.
Our approach is ready to be used in conjunction with full pipeline injections, thus paving the way toward including actual distributions of astrophysical and noise triggers into gravitational-wave population analyses.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a novel machine-learning approach to estimate selection effects in
gravitational-wave observations. Using techniques similar to those commonly
employed in image classification and pattern recognition, we train a series of
neural-network classifiers to predict the LIGO/Virgo detectability of
gravitational-wave signals from compact-binary mergers. We include the effect
of spin precession, higher-order modes, and multiple detectors and show that
their omission, as it is common in large population studies, tends to
overestimate the inferred merger rate in selected regions of the parameter
space. Although here we train our classifiers using a simple signal-to-noise
ratio threshold, our approach is ready to be used in conjunction with full
pipeline injections, thus paving the way toward including actual distributions
of astrophysical and noise triggers into gravitational-wave population
analyses.
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