Normalizing flows as an avenue to study overlapping gravitational wave signals

Abstract

Due to its speed after training, machine learning is often envisaged as a solution to a manifold of the issues faced in gravitational-wave astronomy. Demonstrations have been given for various applications in gravitational-wave data analysis. In this work, we focus on a challenging problem faced by third-generation detectors: parameter inference for overlapping signals. Due to the high detection rate and increased duration of the signals, they will start to overlap, possibly making traditional parameter inference techniques difficult to use. Here, we show a proof-of-concept application of normalizing flows to perform parameter estimation on overlapped binary black hole systems.

7 pages, 6 figures

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• Extracting overlapping gravitational-wave signals of galactic compact binaries: a mini review
• Comparing next-generation detector configurations for high-redshift gravitational wave sources with neural posterior estimation
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• Lightweight posterior construction for gravitational-wave catalogs with the Kolmogorov-Arnold network
• Conditional variational autoencoders for cosmological model discrimination and anomaly detection in cosmic microwave background power spectra
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