Improved detection statistics for non Gaussian gravitational wave stochastic backgrounds

TL;DR

This paper introduces an improved detection statistic for non-Gaussian gravitational wave backgrounds that outperforms previous methods and approaches optimality as non-Gaussianity increases, enhancing robustness without sacrificing performance.

Contribution

The paper presents a new detection statistic that maintains robustness against noise imperfections and outperforms previous methods across the parameter space, moving towards near-optimal detection.

Findings

New detection statistic outperforms previous and Gaussian methods.

Performance approaches Neyman-Pearson optimal with increased non-Gaussianity.

Demonstrated effectiveness through a simple toy model.

Abstract

In a recent paper we described a novel approach to the detection and parameter estimation of a non-Gaussian stochastic background of gravitational waves. In this work we propose an improved version of the detection procedure, preserving robustness against imperfect noise knowledge at no cost of detection performance: in the previous approach, the solution proposed to ensure robustness reduced the performances of the detection statistics, which in some cases (namely, mild non-Gaussianity) could be outperformed by Gaussian ones established in literature. We show, through a simple toy model, that the new detection statistic performs better than the previous one (and than the Gaussian statistic) everywhere in the parameter space. It approaches the optimal Neyman-Pearson statistics monotonically with increasing non-Gaussianity and/or number of detectors. In this study we discuss in detail its efficiency. This is a second, important step towards the implementation of a nearly--optimal detection procedure for a realistic non-Gaussian stochastic background. We discuss the relevance of results obtained in the context of the toy model used, and their importance for understanding a more realistic scenario.