Investigating all-sky Frequency Hough performances for neutron stars

TL;DR

This paper evaluates the all-sky Frequency Hough method for detecting neutron stars' continuous gravitational waves, introducing a machine learning follow-up strategy that improves detection performance on real interferometer data.

Contribution

It presents a novel machine learning approach for follow-up in the Frequency Hough pipeline, enhancing detection capabilities for neutron stars in gravitational wave data.

Findings

Machine learning follow-up improves detection sensitivity.

Performance demonstrated on real interferometer data.

Encouraging classification results at subthreshold levels.

Abstract

Between the estimated population of Neutron Stars (NSs) and the actual number present in the catalogs, there is a huge gap: O(10$^{8-9}$) vs O(10$^3$). Among the different search techniques for Continuous gravitational waves (CWs), the all-sky could help to reduce the discrepancy. We focus on the all-sky CW pipeline Frequency Hough (FH), which operates without prior knowledge of the source parameters ($f,\dot{f}, \lambda, \beta$). Here, we present a Machine Learning strategy, diverging from the standard follow-up(FU) of the FH pipeline. We study the performance with real interferometer data, until reaching $h$ value subthreshold for the standard FU procedure ($CR_{thr}=5$), with encouraging classification results.