Using Swarm Intelligence To Accelerate Pulsar Timing Analysis

2012/10/12 by Stephen R. Taylor, Jonathan R. Gair, Taylor, Stephen R. +4 · 1 citation
Computer Science · Earth and Planetary Sciences · Physics and Astronomy · #Computational Physics and Python Applications #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #General Relativity and Quantum Cosmology (gr-qc) #Geophysics and Gravity Measurements #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Pulsars and Gravitational Waves Research #astro-ph.CO #astro-ph.IM #gr-qc

paper · pdf · doi:10.48550/arxiv.1210.3489

6 pages, 1 figure, 1 table

arxiv created 2012/10/12 · openalex publication_date 2012/10/12 · arxiv updated 2012/10/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We provide brief notes on a particle swarm-optimisation approach to constraining the properties of a stochastic gravitational-wave background in the first International Pulsar Timing Array data-challenge. The technique employs many computational-agents which explore parameter space, remembering their most optimal positions and also sharing this information with all other agents. It is this sharing of information which accelerates the convergence of all agents to the global best-fit location in a very short number of iterations. Error estimates can also be provided by fitting a multivariate Gaussian to the recorded fitness of all visited points.

Cited by

• Results for the International Pulsar Timing Array Second Mock Data Challenge: New Techniques and Challenges for the Detection of Low-Frequency Gravitational-Wave Signals

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