Finding Strongly Lensed Supernovae from Blended Light Curves
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2026
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| author | Park, Sangwoo Shafieloo, Arman Kim, Alex G. Linder, Eric V. Huang, Xiaosheng |
| author_facet | Park, Sangwoo Shafieloo, Arman Kim, Alex G. Linder, Eric V. Huang, Xiaosheng |
| contents | We present a model-independent, photometry-only framework for identifying strongly lensed supernovae when multiple images are unresolved and blended into a single point source. Building on the simulation-based methodology of Bag et al. (2021), we apply this approach to real Zwicky Transient Facility (ZTF) data using a validation sample of spectroscopically confirmed Type Ia supernovae. The method models the observed flux as a superposition of two time-shifted components, and Bayesian inference is used to estimate the relative scaling and time delay. Applying this framework to 445 well-converged supernovae, we find that only a single object satisfies the selection criteria when adopting a conservative threshold of $Δt \ge 12$ days, corresponding to a false positive fraction of $1/445 \approx 0.22\%$. A laxer threshold of $Δt \ge 10$ days yields fourteen objects, for a false positive fraction of $3.15\%$. The method provides a scalable and model-independent first-stage filter for identifying lens-like candidates in large time-domain surveys such as the Rubin Observatory's Legacy Survey of Space and Time (LSST). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_27511 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Finding Strongly Lensed Supernovae from Blended Light Curves Park, Sangwoo Shafieloo, Arman Kim, Alex G. Linder, Eric V. Huang, Xiaosheng Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics We present a model-independent, photometry-only framework for identifying strongly lensed supernovae when multiple images are unresolved and blended into a single point source. Building on the simulation-based methodology of Bag et al. (2021), we apply this approach to real Zwicky Transient Facility (ZTF) data using a validation sample of spectroscopically confirmed Type Ia supernovae. The method models the observed flux as a superposition of two time-shifted components, and Bayesian inference is used to estimate the relative scaling and time delay. Applying this framework to 445 well-converged supernovae, we find that only a single object satisfies the selection criteria when adopting a conservative threshold of $Δt \ge 12$ days, corresponding to a false positive fraction of $1/445 \approx 0.22\%$. A laxer threshold of $Δt \ge 10$ days yields fourteen objects, for a false positive fraction of $3.15\%$. The method provides a scalable and model-independent first-stage filter for identifying lens-like candidates in large time-domain surveys such as the Rubin Observatory's Legacy Survey of Space and Time (LSST). |
| title | Finding Strongly Lensed Supernovae from Blended Light Curves |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2604.27511 |