Dual Cutler-Vallisneri Corrections: Mitigating PSD Drift in Zero-Latency Gravitational-Wave Searches

TL;DR

This paper introduces a perturbative correction framework for zero-latency gravitational-wave searches using minimum-phase whitening, significantly reducing spectral drift errors and improving early-warning detection accuracy.

Contribution

It develops a generalized Cutler-Vallisneri formalism to analytically correct spectral drift-induced biases in zero-latency gravitational-wave data analysis.

Findings

Achieves less than 1% error in timing, phase, and SNR bias corrections.

Uncorrected drift causes detector-pair timing biases over 200 μs and sky-localization errors of 5°–10°.

Median SNR loss of 3-5%, with some outliers exceeding 8%.

Abstract

Maximizing pre-merger warning times in gravitational-wave searches requires minimizing algorithmic latency. While current pipelines typically rely on truncated linear-phase filters, minimum-phase whitening offers a zero-latency alternative that eliminates the acausal look-ahead buffer. However, this causal approach exposes the analysis to spectral drift, where the whitening operator applied to live data diverges from the static template bank, creating a functional perturbation of the matched-filter metric. We develop a perturbative framework generalizing the Cutler-Vallisneri formalism to address these metric errors, deriving analytic expressions for the resulting timing, phase, and SNR biases. Validated against exact stationary-phase models and numerical injections, these corrections achieve $<1\%$ error. Applying this framework to GWTC-4.0 events with realistic 1-week power spectral density (PSD) lags, we find that uncorrected drift induces severe systematics: detector-pair timing biases exceeding $200 \mu$s, phase shifts up to 0.2 rad, and sky-localization errors of $5^{\circ}-10^{\circ}$. Additionally, we observe a median signal-to-noise ratio (SNR) loss of $3-5\%$, with outliers exceeding $8\%$. These results demonstrate that while minimum-phase whitening maximizes the early-warning window, analytic drift corrections are essential to maintain detection volume and pointing accuracy in future observing runs.