Advancing Gravitational-Wave Astronomy using Artificial Intelligence

We are in the midst of a revolution in astronomy. In 2015, the LIGO detectors first observed ripples in the fabric of spacetime caused by two colliding black holes. We call these ripples gravitational waves. This ground-breaking result opened a new form of astronomy that allows us to observe the dark Universe for the first time. The maturing field is entering a new era in the coming years: an improved international network of detectors, including Virgo and KAGRA, will see further, observing thousands of events with ever-greater clarity.
However, the marked increase in data quality poses a significant challenge for analysing compact binary coalescence (CBC) signals. This proposal outlines the vision and approach that the newly established RHUL GW group will take to address key issues in the field and contribute to the field's observational science. The RHUL group was established in 2021 by the project lead, Greg Ashton, who has played a central role in the collaboration, co-leading the CBC observational science working group (one of the largest and most active working groups) and delivering the flagship Bilby software used to measure the properties of all CBC signals during O3 and O4. In this Consolidated Grant, the RHUL group aims to cement its place in the field by developing cutting-edge new approaches to the search for and parameter estimation of CBC signals, melding together the best of traditional algorithms with the latest advances in Artificial Intelligence. Specifically, we aim to:
A-1 Enhance the search for signals by providing the first quantified and calibrated uncertainties to significance estimates for individual search pipelines. Introducing conformal prediction (a distribution-free approach to uncertainty quantification) to the field of gravitational-wave astronomy, we will provide the first robust uncertainty quantification to the false alarm rate and astrophysical probabilities produced by search pipelines.
A-2 Maximise search efficiency by...