New framework to follow up candidates from continuous gravitational-wave searches - INSPIRE

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New framework to follow up candidates from continuous gravitational-wave searches

P.B. Covas
(
- Hannover, Max Planck Inst. Grav. and
- Leibniz U., Hannover
)
,
R. Prix
(
- Hannover, Max Planck Inst. Grav. and
- Leibniz U., Hannover
)
,
J. Martins
(
- Hannover, Max Planck Inst. Grav. and
- Leibniz U., Hannover
)

Apr 29, 2024

16 pages

Published in:

Phys.Rev.D 110 (2024) 2, 024053

Published: Jul 15, 2024

e-Print:

2404.18608 [gr-qc]

DOI:

10.1103/PhysRevD.110.024053 (publication)

View in:

ADS Abstract Service

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Abstract: (APS)

Searches for continuous gravitational waves from unknown neutron stars are limited in sensitivity due to their high computational cost. For this reason, developing new methods or improving existing ones can increase the probability of making a detection. In this paper we present a new framework that uses Markov chain Monte Carlo (MCMC) or nested sampling methods to follow up candidates of continuous gravitational-wave searches. This framework aims to go beyond the capabilities of pyfstat (which is limited to the ptemcee sampler), by allowing a flexible choice of sampling algorithm (using bilby as a wrapper) and multidimensional correlated prior distributions. We show that MCMC and nested sampling methods can recover the maximum posterior point for much bigger parameter-space regions than previously thought (including for sources in binary systems), and we present tests that examine the capabilities of the new framework: a comparison between the dynesty, nessai, and ptemcee samplers, the usage of correlated priors, and its improved computational cost.

Note:

16 pages, 13 figures

Monte Carlo: Markov chain
gravitational radiation
costs
binary
sensitivity
neutron star
higher-dimensional
data analysis method: efficiency
correlation

References(51)

Figures(13)

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