Journal article 132 views 10 downloads
Bayesian reconstruction of primordial perturbations from induced gravitational waves
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Gianmassimo Tasinato ,
Ivonne Zavala Carrasco
Physical Review D, Volume: 112, Issue: 12, Start page: 123538
Swansea University Authors:
Aya Ghaleb, Ameek Malhotra , Gianmassimo Tasinato , Ivonne Zavala Carrasco
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DOI (Published version): 10.1103/n23j-5bfc
Abstract
The formation of primordial black holes or other dark matter relics from amplified density fluctuations in the early Universe may also generate scalar-induced gravitational waves (GW), carrying vital information about the primordial power spectrum and the early expansion history of our Universe. We...
| Published in: | Physical Review D |
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| ISSN: | 2470-0010 2470-0029 |
| Published: |
American Physical Society (APS)
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71107 |
| Abstract: |
The formation of primordial black holes or other dark matter relics from amplified density fluctuations in the early Universe may also generate scalar-induced gravitational waves (GW), carrying vital information about the primordial power spectrum and the early expansion history of our Universe. We present a Bayesian approach aimed at reconstructing both the shape of the scalar power spectrum and the Universe’s equation of state from GW observations, using interpolating splines to flexibly capture features in the GW data. The optimal number of spline nodes is chosen via Bayesian evidence, aiming at balancing complexity of the model and the fidelity of the reconstruction. We test our method using both representative mock data and recent pulsar timing array measurements, demonstrating that it can accurately reconstruct the curvature power spectrum as well as the underlying equation of state, if different from radiation. |
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| College: |
Faculty of Science and Engineering |
| Funders: |
STFC Grants No. ST/T000813/1 and No. ST/X000648/1; UKRI AIMLAC CDT (Artificial Intelligence, Machine Learning and Advanced Computing)-(Center for Doctoral Training), funded by Grant No. EP/S023992/1; We also acknowledge the support of the Supercomputing Wales project, which is part-funded by the European Regional Development Fund (ERDF) via Welsh Government. |
| Issue: |
12 |
| Start Page: |
123538 |