Mitigation of Incoherent Spectral Lines via Adaptive Coherence Analysis for Continuous Gravitational-Wave Searches

TL;DR

This paper introduces an unsupervised adaptive coherence analysis framework that selectively mitigates spectral artefacts in gravitational-wave data, enhancing sensitivity without sacrificing potential signals.

Contribution

The novel adaptive network coherence analysis pipeline effectively suppresses spectral lines in LIGO data while preserving astrophysical signals, improving continuous gravitational-wave search sensitivity.

Findings

Mitigates 89% and 77% of spectral lines in Hanford and Livingston detectors.

Modifies less than 7% of the analysis bandwidth across 20 Hz to 2000 Hz.

Effectively suppresses non-Gaussian noise while preserving signal integrity.

Abstract

The sensitivity of continuous gravitational-wave searches is strictly limited by non-Gaussian spectral artefacts that accumulate coherent power over long observation baselines. In this paper, we present an unsupervised mitigation framework based on adaptive network coherence analysis. Unlike traditional veto methods that discard entire frequency bands, our pipeline selectively suppresses local artefacts while preserving global potentially astrophysical signals. We validate the method using Advanced LIGO O3 data, analysing the cleaning performance across integration times of 1, 3, and 5 days. For the 5-day dataset, the pipeline identifies and mitigates 89\% and 77\% of the total spectral lines in the Hanford and Livingston detectors, respectively, while effectively preserving the coherent population consistent with astrophysical morphologies. This is achieved while modifying less than 7\% of the analysis bandwidth spanning 20~Hz to 2000~Hz. Rigorous statistical verification demonstrates that the mitigation effectively suppresses the non-Gaussian tail of the noise distribution while strictly preserving the statistical integrity of coherent signal candidates. By recovering detector sensitivity in parameter spaces previously contaminated by the spectral forest, this framework provides a robust preprocessing strategy for all-sky searches.