Rapid Identification and Classification of eccentric binary blackhole mergers using Machine Learning

Abstract

The future of Gravitational Wave (GW) detectors [LVK] have made remarkable progress, with an expanding sensitivity band and for upcoming observing runs [O4 and beyond]. Among the various GW signals, eccentric binary mergers present an intriguing and computationally challenging aspect. We address the imperative need for efficient detection and classification of eccentric binary mergers using Machine Learning (ML) techniques. Traditional Bayesian Parameter estimation methods, while accurate, can be prohibitively time-consuming and computationally expensive. To overcome this challenge, we leverage the capabilities of ML to expedite the identification and classification of eccentric GW events. I will present our approach that employs Separable Convolutional Neural Networks (SCNN) to discern between non-eccentric and eccentric binary mergers and further classify the latter into categories of low, moderate, and high eccentricity mergers