Finding Strongly Lensed Supernovae from Blended Light Curves

TL;DR

This paper introduces a model-independent, photometry-only method to identify strongly lensed supernovae from unresolved blended light curves, validated on real ZTF data, with promising false positive rates.

Contribution

It extends a simulation-based approach to real data, providing a scalable, model-independent filter for lens candidate identification in large surveys.

Findings

Only 1 of 445 supernovae met the conservative threshold for lensing.

Laxer threshold identified 14 candidates with a 3.15% false positive rate.

The method is suitable for large upcoming surveys like LSST.

Abstract

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 $\Delta t \ge 12$ days, corresponding to a false positive fraction of $1/445 \approx 0.22\%$. A laxer threshold of $\Delta t \ge 10$ 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).