Enhancing the capabilities of LIGO time-frequency plane searches through clustering

TL;DR

This paper introduces a density-based clustering algorithm to enhance LIGO's time-frequency burst searches, improving detection sensitivity by aggregating significance over regions and reducing noise artifacts.

Contribution

The authors develop and implement a novel clustering extension to the QPipeline, improving detection of extended gravitational-wave burst signals in noisy data.

Findings

Clustering improves detection performance for extended signals.

The method reduces false alarms from noise artifacts.

Test results show enhanced sensitivity with simulated signals.

Abstract

One class of gravitational wave signals LIGO is searching for consists of short duration bursts of unknown waveforms. Potential sources include core collapse supernovae, gamma ray burst progenitors, and mergers of binary black holes or neutron stars. We present a density-based clustering algorithm to improve the performance of time-frequency searches for such gravitational-wave bursts when they are extended in time and/or frequency, and not sufficiently well known to permit matched filtering. We have implemented this algorithm as an extension to the QPipeline, a gravitational-wave data analysis pipeline for the detection of bursts, which currently determines the statistical significance of events based solely on the peak significance observed in minimum uncertainty regions of the time-frequency plane. Density based clustering improves the performance of such a search by considering the aggregate significance of arbitrarily shaped regions in the time-frequency plane and rejecting the isolated minimum uncertainty features expected from the background detector noise. In this paper, we present test results for simulated signals and demonstrate that density based clustering improves the performance of the QPipeline for signals extended in time and/or frequency.