An F-statistic based multi-detector veto for detector artifacts in continuous-wave gravitational wave data

TL;DR

This paper introduces a Bayesian line veto statistic to improve the robustness of continuous gravitational wave detection against noise artifacts, outperforming traditional methods in simulated data tests.

Contribution

It develops a systematic Bayesian framework for line vetoes in CW searches, enhancing noise artifact rejection beyond ad-hoc methods.

Findings

The LV-statistic is more robust against line artifacts than the F-statistic.

Testing on simulated data shows improved detection reliability.

The framework allows benchmarking of veto strategies.

Abstract

Continuous gravitational waves (CW) are expected from spinning neutron stars with non-axisymmetric deformations. A network of interferometric detectors (LIGO, Virgo and GEO600) is looking for these signals. They are predicted to be very weak and retrievable only by integration over long observation times. One of the standard methods of CW data analysis is the multi-detector F-statistic. In a typical search, the F-statistic is computed over a range in frequency, spin-down and sky position, and the candidates with highest F values are kept for further analysis. However, this detection statistic is susceptible to a class of noise artifacts, strong monochromatic lines in a single detector. By assuming an extended noise model - standard Gaussian noise plus single-detector lines - we can use a Bayesian odds ratio to derive a generalized detection statistic, the line veto (LV-) statistic. In the absence of lines, it behaves similarly to the F-statistic, but it is more robust against line artifacts. In the past, ad-hoc post-processing vetoes have been implemented in searches to remove these artifacts. Here we provide a systematic framework to develop and benchmark this class of vetoes. We present our results from testing this LV-statistic on simulated data.