Deep Learning Model on Gravitational Waveforms in Merging and Ringdown Phases of Binary Black Hole Coalescences

TL;DR

This paper introduces a deep learning model capable of rapidly generating accurate gravitational waveforms for binary black hole mergers, significantly reducing computational costs compared to traditional numerical relativity methods.

Contribution

The paper presents a novel deep learning architecture that efficiently produces gravitational waveforms with high accuracy, enabling faster waveform generation for gravitational wave detection.

Findings

Model generates ~1500 waveforms in seconds

Achieves 99.9% match with state-of-the-art approximants

Successfully recovers event times in matched filtering

Abstract

The waveform templates of the matched filtering-based gravitational-wave search ought to cover wide range of parameters for the prosperous detection. Numerical relativity (NR) has been widely accepted as the most accurate method for modeling the waveforms. Still, it is well-known that NR typically requires a tremendous amount of computational costs. In this paper, we demonstrate a proof-of-concept of a novel deterministic deep learning (DL) architecture that can generate gravitational waveforms from the merger and ringdown phases of the non-spinning binary black hole coalescence. Our model takes ${\cal O}$(1) seconds for generating approximately $1500$ waveforms with a 99.9\% match on average to one of the state-of-the-art waveform approximants, the effective-one-body. We also perform matched filtering with the DL-waveforms and find that the waveforms can recover the event time of the injected gravitational-wave signals.