Rapid Identification and Classification of Eccentric Gravitational Wave Inspirals with Machine Learning

TL;DR

This paper presents a machine learning approach using a neural network to rapidly identify and classify eccentric gravitational wave signals, reducing computational costs compared to traditional Bayesian methods.

Contribution

It introduces a separable-convolutional neural network trained on synthetic data to detect and classify eccentric GW signals, a novel application in this context.

Findings

Detects eccentricity with 91.4% accuracy

Classifies signals with 85.3% accuracy

Achieves high true positive rate for eccentric signals

Abstract

Current templated searches for gravitational waves (GWs) emanated from compact binary coalescences (CBCs) assume that the binaries have circularized by the time they enter the sensitivity band of the LIGO-Virgo-KAGRA (LVK) network. However, certain formation channels predict that in future observing runs (O4 and beyond), a fraction of detectable binaries could enter the sensitivity band with a measurable eccentricity $e$. Constraining $e$ for each GW event with Bayesian parameter estimation methods is computationally expensive and time-consuming. This motivates the need for a machine learning based identification and classification scheme, which could weed out the majority of GW events as non-eccentric and drastically reduce the set of candidate eccentric GWs. As a proof of principle, we train a separable-convolutional neural network (SCNN) with spectrograms of synthetic GWs added to Gaussian noise characterized by O4 representative \texttt{PSD}s. We use the trained network to (i) segregate candidates as either eccentric or non-eccentric (henceforth called the detection problem) and (ii) classify the events as non-eccentric $(e = 0)$, moderately eccentric $(e \in (0, 0.2])$, and highly eccentric $(e \in (0.2, 0.5])$. On the detection problem, our best performing network detects eccentricity with $0.914$ accuracy and true and false positive rates of $0.862$ and $0.138$, respectively. On the classification problem, the best performing network classifies signals with $0.853$ accuracy. We find that our trained detector displays close to ideal behavior for the data we consider.