Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 024053 |
| Seitenumfang | 16 |
| Fachzeitschrift | Physical Review D |
| Jahrgang | 110 |
| Ausgabenummer | 2 |
| Publikationsstatus | Veröffentlicht - 22 Juli 2024 |
Abstract
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.
ASJC Scopus Sachgebiete
- Physik und Astronomie (insg.)
- Kern- und Hochenergiephysik
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in: Physical Review D, Jahrgang 110, Nr. 2, 024053, 22.07.2024.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - New framework to follow up candidates from continuous gravitational-wave searches
AU - Covas, P. B.
AU - Prix, R.
AU - Martins, J.
N1 - Publisher Copyright: © 2024 authors.
PY - 2024/7/22
Y1 - 2024/7/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85199533479&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2404.18608
DO - 10.48550/arXiv.2404.18608
M3 - Article
AN - SCOPUS:85199533479
VL - 110
JO - Physical Review D
JF - Physical Review D
SN - 2470-0010
IS - 2
M1 - 024053
ER -