Bayesian statistical framework for identifying strongly lensed gravitational-wave signals
Abstract
It is expected that gravitational waves, similar to electromagnetic waves, can be gravitationally lensed by intervening matters, producing multiple instances of the same signal arriving at different times from different apparent luminosity distances with different phase shifts compared to the unlensed signal due to lensing. If unaccounted for, these lensed signals will masquerade as separate systems with higher mass and lower redshift. Here we present a Bayesian statistical framework for identifying strongly lensed gravitational-wave signals that incorporates astrophysical information and accounts for selection effects. We also propose a two-step hierarchical analysis for more efficient computations of the probabilities and inferences of source parameters free from shifts introduced by lensing. We show with examples on how changing the astrophysical models could shift one’s interpretation on the origin of the observed gravitational waves, and possibly lead to indisputable evidence of strong lensing of the observed waves. In addition, we demonstrate the improvement in the sky localization of the source of the lensed signals, and in some cases the identification of the Morse indices of the lensed signals. If confirmed, lensed gravitational waves will allow us to probe the Universe at higher redshift, and to constrain the polarization contents of the waves with fewer detectors.
Copyright and License
© 2023 American Physical Society.
Acknowledgement
The authors would like to thank Tjonnie G. F. Li, Will Farr, Masamune Oguri, Alan Weinstein, Yanbei Chen, Katerina Chatziioannou, Colm Talbot, and Ken K. Y. Ng for the discussion and the help when preparing this paper. R. K. L. L. acknowledges support from the Croucher Foundation. R. K. L. L. also acknowledges support from NSF Awards No. PHY-1912594, No. PHY-2207758. I. M. H. is supported by the NSF Graduate Research Fellowship Program under Grant No. DGE-17247915. The computations presented here were conducted on the Caltech High Performance Cluster partially supported by a grant from the Gordon and Betty Moore Foundation. I. M. H. also acknowledges support from NSF Awards No. PHY-1607585, No. PHY-1912649, No. PHY-1806990, and No. PHY-2207728. The authors are also grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants No. PHY-0757058 and No. PHY-0823459. Figs. 1, 2, 4, and 5 were generated using BayesNet. Figures 6, 7, and 10 were generated using corner.py [54]. This is LIGO Document No. P1900058.
Files
Name | Size | Download all |
---|---|---|
md5:7677c5217daacc20496fd8bbbcd9b796
|
2.2 MB | Preview Download |
Additional details
- ISSN
- 2470-0029
- Croucher Foundation
- National Science Foundation
- PHY-1912594
- National Science Foundation
- PHY-2207758
- National Science Foundation
- NSF Graduate Research Fellowship DGE-17247915
- Gordon and Betty Moore Foundation
- National Science Foundation
- PHY-1607585
- National Science Foundation
- PHY-1912649
- National Science Foundation
- PHY-1806990
- National Science Foundation
- PHY-0823459
- National Science Foundation
- PHY-0757058
- National Science Foundation
- PHY-2207728
- Caltech groups
- LIGO
- Other Numbering System Name
- LIGO Document
- Other Numbering System Identifier
- P1900058