| Title: |
Bayesian framework to follow-up continuous gravitational wave candidates from deep surveys |
| Authors: |
Martins, J.; Papa, M. A.; Steltner, B.; Prix, R.; Covas, P. B. |
| Contributors: |
Horizon 2020 Framework Programme; H2020 Marie Skłodowska-Curie Actions |
| Source: |
Physical Review D ; volume 113, issue 2 ; ISSN 2470-0010 2470-0029 |
| Publisher Information: |
American Physical Society (APS) |
| Publication Year: |
2026 |
| Description: |
Broad all-sky searches for continuous gravitational waves have high computational costs and require hierarchical pipelines. The sensitivity of these approaches is set by the initial search and by the number of candidates from that stage that can be followed up. The current follow-up schemes for the deepest surveys require careful tuning and setup, and have a significant human-labor cost and this impacts the number of follow-ups that can be afforded. Here we present and demonstrate a new follow-up framework based on Bayesian parameter estimation for the rapid, highly automated follow-up of candidates produced by the early stages of deep, wide-parameter space searches for continuous waves. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1103/rbyp-157m |
| DOI: |
10.1103/rbyp-157m/fulltext |
| Availability: |
https://doi.org/10.1103/rbyp-157m; https://link.aps.org/article/10.1103/rbyp-157m; http://harvest.aps.org/v2/journals/articles/10.1103/rbyp-157m/fulltext |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.5CC56734 |
| Database: |
BASE |