GPU-Accelerated Searches for Long-Transient Gravitational Waves from Newborn Neutron Stars

TL;DR

This paper introduces a GPU-accelerated search method for long-duration gravitational waves from newborn neutron stars, enabling faster, wide-area sky coverage to enhance detection prospects in upcoming gravitational wave observations.

Contribution

The paper presents a novel GPU-based computational approach using Python and JAX that significantly speeds up searches for long-transient gravitational waves from neutron stars.

Findings

Faster processing enables wider sky coverage.

Method is ready for deployment in upcoming LIGO-Virgo-KAGRA runs.

Improves detection capabilities for long-duration gravitational waves.

Abstract

We present a novel method to efficiently search for long-duration gravitational wave transients emitted by new-born neutron star remnants of binary neutron star coalescences or supernovae. The detection of these long-transient gravitational waves would contribute to the understanding of the properties of neutron stars and fundamental physics. Additionally, studying gravitational waves emitted by neutron stars can provide valuable tests of general relativity and offer insights into the neutron star population, of which only a small fraction appears to be observable through current electromagnetic telescopes. Our approach uses GPUs and the JAX library in Python, resulting in significantly faster processing compared to previous methods. The efficiency of this code enables wide regions of the sky to be covered, eliminating the need for precise pinpointing of mergers or supernovae. This method will be deployed in searches for long-transient gravitational waves following any detection of a binary neutron star system merger in the latest O4 science run of the LIGO-Virgo-KAGRA collaboration, which started in May 2023 with a significant improvement in sensitivity with respect to previous runs.