Efficient Search for Detection Candidates Using a Peak Finder Strategy for All-Sky-All-Frequency Gravitational Wave Radiometer

TL;DR

This paper introduces a Peak Finder algorithm to improve the detection candidate selection process in all-sky gravitational wave radiometer searches, reducing false dismissals and computational load.

Contribution

The novel Peak Finder algorithm effectively reduces correlated candidates and false dismissals, enhancing the efficiency of unmodeled gravitational wave searches.

Findings

Peak Finder reduces false dismissal rate by a factor of 3 at 30 Hz.

The method enables follow-up of more independent candidates with limited resources.

Significantly decreases the number of false positives due to noise or artifacts.

Abstract

The first all-sky-all-frequency (ASAF) radiometer search was conducted using data from the first three observing runs of the Advanced LIGO and Advanced Virgo detectors. The significance of this search lies in its fast and unmodeled approach, leveraging a cross-correlation technique to identify common signals across the detector network. As a result, this method serves as an excellent alternative to search for unknown or poorly modeled continuous wave sources and narrowband components of the gravitational wave (GW) background. For continuous wave sources whose waveform can be modeled, this method can serve as the first stage of a hierarchical scheme by identifying sub-threshold candidates to be followed up with more optimal but computationally expensive searches. The ASAF search, however, presently suffers from beam smearing, where multiple candidates may arise due to the same noise fluctuations, detector artifact, or a GW source. This can reduce the detection probability in follow-up analyzes, especially with limited computing resources. To mitigate this issue and reduce the number of correlated and unnecessary candidates, we introduce a novel Peak Finder algorithm. This algorithm helps identifying the most representative candidates while preserving detection sensitivity, thereby allowing follow up of a much larger number of independent candidates. The reduction in correlated samples leads to a significant reduction in False Dismissal Rate (FDR) using the Peak Finder method compared to the Full-sky method. For instance, following up 2 Peak Finder candidates at 30 Hz reduces FDR by a factor of 3.