Gravitational-wave selection effects using neural-network classifiers

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...

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Bibliographic Details
Published in:Physical review. D 2020-11, Vol.102 (10), p.1, Article 103020
Main Authors: Gerosa, Davide, Pratten, Geraint, Vecchio, Alberto
Format: Article
Language:English
Subjects:
Classifiers
Gravitational effects
Gravitational waves
Image classification
Machine learning
Neural networks
Noise threshold
Object recognition
Pattern recognition
Signal to noise ratio
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Summary: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.
ISSN:2470-0010
2470-0029
DOI:10.1103/PhysRevD.102.103020