Improved detection statistics for non Gaussian gravitational wave stochastic backgrounds

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.Comment: 12 pages, 5 figures (published on 23 June 2023

Detecting non-Gaussian gravitational wave backgrounds: a unified framework

We describe a novel approach to the detection and parameter estimation of a non\textendash Gaussian stochastic background of gravitational waves. The method is based on the determination of relevant statistical parameters using importance sampling. We show that it is possible to improve the Gaussian detection statistics, by simulating realizations of the expected signal for a given model. While computationally expensive, our method improves the detection performance, leveraging the prior knowledge on the expected signal, and can be used in a natural way to extract physical information about the background. We present the basic principles of our approach, characterize the detection statistic performances in a simplified context and discuss possible applications to the detection of some astrophysical foregrounds. We argue that the proposed approach, complementarily to the ones available in literature might be used to detect suitable astrophysical foregrounds by currently operating and future gravitational wave detectors.Comment: 12 Pages, 4 Figures, Supplemental material (published on 24 March 2023

Upper limits on persistent gravitational waves using folded data and the full covariance matrix from Advanced LIGO′'s first two observing runs

The stochastic gravitational wave background (SGWB) created by astrophysical sources in the nearby universe is likely to be anisotropic. Upper limits on SGWB anisotropy have been produced for all major data taking runs by the ground-based laser interferometric detectors. However, due to the challenges involved in numerically inverting the pixel-to-pixel noise covariance matrix, which is necessary for setting upper limits, the searches accounted for angular correlations in the map by using the spherical harmonic basis, where regularization was relatively easier. This approach is better suited though for extended sources.Moreover, the upper limit maps produced in the two different bases are seemingly different. While the upper limits may be consistent within statistical errors, it was important to check whether the results would remain consistent if the full noise covariance matrix was used in the pixel basis. Here, we use the full pixel-to-pixel Fisher information matrix to create upper limit maps of SGWB anisotropy. We first perform an unmodeled search for persistent, directional gravitational wave sources using folded data from the first (O1) and second (O2) observing runs of Advanced LIGO and show that the results are consistent with the upper limits published by the LIGO-Virgo Collaboration (LVC). We then explore various ways to account for the pixel-to-pixel Fisher information matrix using singular value decomposition and Bayesian regularization schemes. We also account for the bias arising from regularization in the likelihood. We do not find evidence for any SGWB signal in the data, consistent with the LVC results and, though the upper limits differ significantly. Through an injection study we show that they are all valid $95\%$ upper limits, that is, the upper limit in a pixel is less than the injected signal strength in less than $5\%$ of the pixels.Comment: 17 pages, 7 figure