IceCube Search for Neutrinos Coincident with Gravitational Wave Events from LIGO/Virgo Run O3

Journal article

The promise of multi-messenger astronomy relies on the rapid detection of gravitational waves at very low latencies ($\mathcal{O}$(1\,s)) in order to maximize the amount of time available for follow-up observations. In recent years, neural-networks have demonstrated robust non-linear modeling capabilities and millisecond-scale inference at a comparatively small computational footprint, making them an attractive family of algorithms in this context. However, integration of these algorithms into the gravitational-wave astrophysics research ecosystem has proven non-trivial. Here, we present the first fully machine learning-based pipeline for the detection of gravitational waves from compact binary coalescences (CBCs) running in low-latency. We demonstrate this pipeline to have a fraction of the latency of traditional matched filtering search pipelines while achieving state-of-the-art sensitivity to higher-mass stellar binary black holes

Authors: Abbasi, R; Ackermann, M; Adams, J; Aggarwal, N; Aguilar, JA

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Astronomical Society
• Journal: The Astrophysical Journal
• Volume: 944
• Issue: 1
• Pages: 80-80
• Publication date: 2023-02-14
• DOI: 10.3847/1538-4357/aca5fc
• EISSN: 1538-4357
• ISSN: 0004-637X
• Language: English
• Keywords: Astronomy; Neutrino detector; Solar neutrino; Observatory; Astrophysics; Neutrino astronomy; Gravitational wave; Physics; Gravitational-wave observatory; Solar neutrino problem; LIGO; Neutrino; Neutrino oscillation; Particle physics; Neutron star