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

Understand

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.