Merger-Ringdown Consistency: A New Test of Strong Gravity using Deep Learning

TL;DR

This paper introduces a new deep learning-based method to test strong gravity by analyzing binary black hole mergers, combining multiple observations for a statistical null test of general relativity.

Contribution

It proposes the merger-ringdown consistency test and a scheme to aggregate information from multiple ringdowns, enabling efficient, precision tests of gravity with neural networks.

Findings

Feasibility demonstrated with simulated data

Deep learning framework effectively performs the test

Sets a precedent for future precision gravity tests

Abstract

The gravitational waves emitted during the coalescence of binary black holes are an excellent probe to test the behaviour of strong gravity. In this paper, we propose a new test called the `merger-ringdown consistency test` that focuses on probing the imprints of the dynamics in strong-gravity around the black-holes during the plunge-merger and ringdown phase. Furthermore, we present a scheme that allows us to efficiently combine information across multiple ringdown observations to perform a statistical null test of GR using the detected BH population. We present a proof-of-concept study for this test using simulated binary black hole ringdowns embedded in the next-generation ground-based detector noise. We demonstrate the feasibility of our test using a deep learning framework, setting a precedence for performing precision tests of gravity with neural networks.