Premerger Sky Localization of Gravitational Waves from Binary Neutron Star Mergers Using Deep Learning

Abstract

The simultaneous observation of gravitational waves (GW) and prompt electromagnetic counterparts from the merger of two neutron stars can help reveal the properties of extreme matter and gravity during and immediately after the final plunge. Rapid sky localization of these sources is crucial to facilitate such multimessenger observations. As GWs from binary neutron star (BNS) mergers can spend up to 10–15 minutes in the frequency bands of the detectors at design sensitivity, early-warning alerts and premerger sky localization can be achieved for sufficiently bright sources, as demonstrated in recent studies. In this work, we present premerger BNS sky localization results using GW-SkyLocator, a deep-learning model capable of inferring sky location posterior distributions of GW sources at orders of magnitude faster speeds than standard Markov Chain Monte Carlo methods. We test our model’s performance on a catalog of simulated injections from Sachdev, recovered at 0–60 s before the merger, and obtain comparable sky localization areas to the rapid localization tool BAYESTAR. These results show the feasibility of our model for premerger sky localization and the possibility of follow-up observations for precursor emissions from BNS mergers.

Dr. Chayan Chatterjee

AI for New Messengers Postdoctoral Fellow

Dr. Chayan Chatterjee is the A.I. for New Messengers Postdoctoral Fellow at Vanderbilt University. His research focuses on application of machine learning to study gravitational waves - tiny ripples in space-time created by colliding black holes and neutron stars.