GWSkyNet II : a refined machine learning pipeline for real-time classification of public gravitational wave alerts

TL;DR

This paper enhances GWSkyNet, a machine learning classifier, for real-time gravitational wave event identification, improving accuracy and integration into low-latency pipelines to support electromagnetic follow-up observations.

Contribution

The authors update and fine-tune GWSkyNet for LIGO-Virgo-KAGRA's fourth run and demonstrate its effectiveness in real-time classification and noise rejection.

Findings

Improved classifier performance with updated architecture and features.

Effective noise rejection and true signal identification in mock data.

Seamless integration into low-latency alert systems.

Abstract

Electromagnetic follow-up observations of gravitational wave events offer critical insights and provide significant scientific gain from this new class of astrophysical transients. Accurate identification of gravitational wave candidates and rapid release of sky localization information are crucial for the success of these electromagnetic follow-up observations. However, searches for gravitational wave candidates in real time suffer a non-negligible false alarm rate. By leveraging the sky localization information and other metadata associated with gravitational wave candidates, GWSkyNet, a machine learning classifier developed by Cabero et al. (2020), demonstrated promising accuracy for the identification of the origin of event candidates. We improve the performance of the classifier for LIGO-Virgo-KAGRA's fourth observing run by reviewing and updating the architecture and features used as inputs by the algorithm. We also retrain and fine-tune the classifier with data from the third observing run. To improve the prospect of electromagnetic follow-up observations, we incorporate GWSkyNet into LIGO-Virgo-KAGRA's low-latency infrastructure as an automatic pipeline for the evaluation of gravitational wave alerts in real time. We test the readiness of the algorithm on a LIGO-Virgo-KAGRA mock data challenge campaign. The results show that by thresholding on the GWSkyNet score, noise masquerading as astrophysical sources can be rejected efficiently and the majority of true astrophysical signals correctly identified.