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

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.

Figures (5)

• Figure 1: Visualisation of the first three stages of the pipeline applied to Advanced LIGO H1 and L1 data. Top and middle panels: spectral line detection for H1 and L1, respectively, showing the robust baseline fit (red line) and detected lines (orange triangles). Bottom panel: adaptive matching of coherent lines between detectors (red circles).
• Figure 2: Spectral impact of adaptive coherence mitigation on a 5-day subset of Advanced LIGO O3 data. Top and middle panels: ASDs for H1 and L1, respectively. Bottom panel: $\mathcal{F}$-statistic values. All plots show results before (blue) and after (orange) cleaning.
• Figure 3: Global cleaning statistics versus subset time-span, aggregated across all subsets ($n=312$, $111$, $68$ subsets for 1, 3, 5 days). Top panel: mean count of identified spectral lines. Bottom panel: percentage of the analysis band (20-2000 Hz) modified by the mitigation. Error bars represent 95% confidence intervals.
• Figure 4: Morphological distributions of all detected spectral features across all subsets ($n=491$). Top panel: Distribution of frequencies of coherent feature (blue) and incoherent artefacts (orange). Bottom panel: Distribution of line-widths of coherent features (blue) and incoherent artefacts (orange). Shaded regions represent 95% confidence intervals.
• Figure 5: Q-Q analysis of the $\mathcal{F}$-statistic for all subsets ($n=491$). Left panel: for frequency bins associated with incoherent lines and the background (orange). Right panel: for frequency bins associated with coherent features (blue). Shaded regions indicate 95% bootstrap confidence intervals.