Document Type
Article
Publication Date
2-17-2024
Abstract
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≤SNR≤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.
Recommended Citation
Zou, Xiao-Bo, Soumya D. Mohanty, Hong-Gang Luo, and Yu-Xiao Liu. "Swarm Intelligence Methods for Extreme Mass Ratio Inspiral Search: First Application of Particle Swarm Optimization." Universe 10, no. 2 (2024): 96. https://doi.org/10.3390/universe10020096
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
Universe
DOI
10.3390/universe10020096
Comments
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).