Parameter estimates in binary black hole collisions using neural networks

TL;DR

This paper introduces a neural network-based algorithm to estimate the mass ratio of binary black hole mergers from gravitational wave signals, demonstrating its effectiveness on simulated data with varying noise levels.

Contribution

The paper presents a novel neural network approach for estimating black hole binary parameters directly from gravitational wave data, including noise robustness analysis.

Findings

Accurate mass ratio estimation in noise-free signals

Robust performance with noisy signals at different SNR levels

Effective interpolation and extrapolation capabilities

Abstract

We present an algorithm based on artificial neural networks (ANNs), that estimates the mass ratio in a binary black hole collision out of given Gravitational Wave (GW) strains. In this analysis, the ANN is trained with a sample of GW signals generated with numerical simulations. The effectiveness of the algorithm is evaluated with GWs generated also with simulations for given mass ratios unknown to the ANN. We measure the accuracy of the algorithm in the interpolation and extrapolation regimes. We present the results for noise free signals and signals contaminated with Gaussian noise, in order to foresee the dependence of the method accuracy in terms of the signal to noise ratio.