Using overlap of sky localization probability maps for filtering potentially lensed pairs of gravitational-wave signals
Creators
Abstract
Strong gravitational lensing creates multiple images of a gravitational wave transient. The current state-of-the-art method for identifying such lensing events is a computationally expensive full Bayesian analysis. In this paper, we investigate the feasibility and efficiency of using the overlap of sky localization probability maps (skymaps) to quickly filter potentially lensed gravitational wave signal pairs. We introduce three overlap statistics and test their performance using 200 simulated lensed pairs of gravitational-wave signals across five sets of signal-to-noise ratios. By setting a threshold with a false positive rate of FPR =10⁻² for the three overlap statistics, we find that we can filter out over 99% of nonlensed events while retaining all lensed events. The statistics for each event pair can be computed instantly, and can be used in practice to quickly analyze existing events using the skymaps from the low-latency localization pipelines when results from the full parameter estimation are not available.
Copyright and License
© 2025 American Physical Society.
Acknowledgement
We thank Otto Akseli Hannuksela for fruitful discussion. I. C. F. W. and T. G. F. L. are partially supported by grants from the Research Grants Council of the Hong Kong (Projects No. 24304317 and No. 14306419) and Research Committee of the Chinese University of Hong Kong. R. K. L. L. and T. G. F. L. would also like to gratefully acknowledge the support from the Croucher Foundation in Hong Kong. The authors acknowledge the use of the IUCAA LDG cluster Sarathi for the computational/numerical work. The authors are also grateful for the computational resources provided by the Chinese University of Hong Kong. The Center of Gravity is a Center of Excellence funded by the Danish National Research Foundation under Grant No. 184. This work is also partially supported by the Research Foundation - Flanders (FWO) through Grants No. I002123N, No. I000725N, and No. G086722N.
Data Availability
The data that support the findings of this manuscript are openly available [39].
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Additional details
Related works
- Is new version of
- Discussion Paper: arXiv:2112.05932 (arXiv)
- Is supplemented by
- Dataset: 10.5281/zenodo.17177201 (DOI)
Funding
- University Grants Committee
- 24304317
- University Grants Committee
- 14306419
- Chinese University of Hong Kong
- Croucher Foundation
- Danish National Research Foundation
- 184
- Research Foundation - Flanders
- I002123N
- Research Foundation - Flanders
- I000725N
- Research Foundation - Flanders
- G086722N
Dates
- Submitted
-
2025-09-24
- Accepted
-
2025-11-10