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Open AccessArticle

Swarm Intelligence Methods for Extreme Mass Ratio Inspiral Search: First Application of Particle Swarm Optimization

by Xiao-Bo Zou, Soumya D. Mohanty, Hong-Gang Luo and Yu-Xiao Liu

Universe 2024, 10(2), 96; https://doi.org/10.3390/universe10020096 - 17 Feb 2024

Cited by 4 | Viewed by 2114

Swarm intelligence (SI) methods are nature-inspired metaheuristics for global optimization that exploit a coordinated stochastic search strategy by a group of agents. Particle swarm optimization (PSO) is an established SI method that has been applied successfully to the optimization of rugged high-dimensional likelihood functions, a problem that represents the main bottleneck across a variety of gravitational wave (GW) data analysis challenges. We present results from the first application of PSO to one of the most difficult of these challenges, namely the search for the Extreme Mass Ratio Inspiral (EMRI) in data from future spaceborne GW detectors such as LISA, Taiji, or Tianqin. We use the standard Generalized Likelihood Ratio Test formalism, with the minimal use of restrictive approximations, to search 6 months of simulated LISA data and quantify the search depth, signal-to-noise ratio (SNR), and breadth, within the ranges of the EMRI parameters, that PSO can handle. Our results demonstrate that a PSO-based EMRI search is successful for a search region ranging over ≳10σ for the majority of parameters and ≳200σ for one, with

σ

being the SNR-dependent Cramer–Rao lower bound on the parameter estimation error and

30 \leq SNR \leq 50

. This is in the vicinity of the search ranges that the current hierarchical schemes can identify. Directions for future improvement, including computational bottlenecks to be overcome, are identified. Full article

(This article belongs to the Special Issue Newest Results in Gravitational Waves and Machine Learning)

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64 pages, 3716 KiB

Open AccessReview

Stochastic Gravitational-Wave Backgrounds: Current Detection Efforts and Future Prospects

by Arianna I. Renzini, Boris Goncharov, Alexander C. Jenkins and Patrick M. Meyers

Galaxies 2022, 10(1), 34; https://doi.org/10.3390/galaxies10010034 - 14 Feb 2022

Cited by 85 | Viewed by 9449

Abstract

The collection of individually resolvable gravitational wave (GW) events makes up a tiny fraction of all GW signals that reach our detectors, while most lie below the confusion limit and are undetected. Similarly to voices in a crowded room, the collection of unresolved signals gives rise to a background that is well-described via stochastic variables and, hence, referred to as the stochastic GW background (SGWB). In this review, we provide an overview of stochastic GW signals and characterise them based on features of interest such as generation processes and observational properties. We then review the current detection strategies for stochastic backgrounds, offering a ready-to-use manual for stochastic GW searches in real data. In the process, we distinguish between interferometric measurements of GWs, either by ground-based or space-based laser interferometers, and timing-residuals analyses with pulsar timing arrays (PTAs). These detection methods have been applied to real data both by large GW collaborations and smaller research groups, and the most recent and instructive results are reported here. We close this review with an outlook on future observations with third generation detectors, space-based interferometers, and potential noninterferometric detection methods proposed in the literature. Full article

(This article belongs to the Special Issue Present and Future of Gravitational Wave Astronomy)

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