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.

Gerosa, D., Pratten, G., Vecchio, A. (2020). Gravitational-wave selection effects using neural-network classifiers. PHYSICAL REVIEW D, 102(10) [10.1103/PhysRevD.102.103020].

Gravitational-wave selection effects using neural-network classifiers

Gerosa D.
;
2020

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.
Articolo in rivista - Articolo scientifico
black holes, gravitational waves, general relativity, relativistic astrophysics
English
2020
102
10
103020
none
Gerosa, D., Pratten, G., Vecchio, A. (2020). Gravitational-wave selection effects using neural-network classifiers. PHYSICAL REVIEW D, 102(10) [10.1103/PhysRevD.102.103020].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/325559
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