arXiv subjects

Matteo Ballelli

Publications and source records attributed to Matteo Ballelli.

3 recordsLinked to original sources

arXiv2026

The geometry of lunar gravitational wave detection

The Lunar Gravitational Wave Antenna (LGWA) is a planned gravitational wave detector on the Moon, targeting the deci-Hertz band and expected to deliver breakthrough discoveries across several science cases, including the Moon's interior structure and astrophysics. In this work, we show that adopting a frame comoving with the Solar System barycenter (SSB), but with its origin at a location that minimizes timing uncertainty, reduces the sampling time by an order of magnitude. We present a systematic post-processing procedure to identify the optimal origin within the Solar System for any given signal. We explore alternative timing parametrizations beyond the merger time, and find that they have only a minor impact on parameter uncertainties. Using the stellar-mass black hole binary GW250114 as a case study, we illustrate how these geometrical considerations translate into improved parameter constraints. Two minutes before its merger, the LGWA would have measured its chirp mass to a precision of 0.0002 solar masses (90% symmetric) and constrained its sky position to within 65 square degrees (90% HPD area); these constraints are tighter than those obtained by the LIGO-Virgo-KAGRA (LVK) detectors, despite a lower signal-to-noise ratio. We connect our results to an analytical approximation proposed by Wen and Chen, which relates the area spanned by the orbital motion of a detector to its efficacy in constraining the sky position of a source. We verify its qualitative validity for compact binary sources with a series of injections, identifying the regimes in which its underlying assumptions break down. Our results demonstrate that inference for long-duration GW signals with the LGWA must be treated as a geometrical problem, in which detector motion, reference-frame choice, and signal evolution jointly determine both parameter constraints and computational efficiency.

gr-qc↗

arXiv2023

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.

gr-qc↗

arXiv2023

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.

gr-qc↗