Normalizing flows as an avenue to study overlapping gravitational wave signals

TL;DR

This paper explores using normalizing flows, a machine learning technique, to perform parameter inference on overlapping gravitational wave signals, addressing challenges faced by future detectors with high detection rates.

Contribution

It presents a proof-of-concept demonstrating normalizing flows as a novel approach for parameter estimation in overlapping gravitational wave signals.

Findings

Normalizing flows can estimate parameters of overlapping signals.

The method offers a fast alternative to traditional techniques.

Proof-of-concept shows potential for third-generation detectors.

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.