Detectability and Characterisation of Strongly Lensed Supernova Lightcurves in the Zwicky Transient Facility

TL;DR

This study investigates the detection biases of strongly lensed supernovae in ZTF, simulates realistic lightcurves, and develops selection criteria to improve identification of these rare events.

Contribution

It provides a detailed simulation-based analysis of detection biases and proposes optimized selection cuts for identifying strongly lensed supernovae in ZTF data.

Findings

Detection rates are biased towards highly magnified events.

Applying magnitude cuts significantly reduces detection rates.

Proposed selection criteria can distinguish lensed from unlensed supernovae.

Abstract

The Zwicky Transient Facility (ZTF) was expected to detect more than one strong gravitationally-lensed supernova (glSN) per year, but only one event was identified in the first four years of the survey. This work investigates selection biases in the search strategy that could explain the discrepancy and revise discovery predictions. We present simulations of realistic lightcurves for lensed thermonuclear (glSNIa) and core-collapse supernova (glCCSN) explosions over a span of 5.33 years of the survey, utilizing the actual observation logs of ZTF. We find that the magnitude limit in spectroscopic screening significantly biases the selection towards highly magnified glSNe, for which the detection rates are consistent with the identification of a single object by ZTF. To reach the higher predicted rate of detections requires an optimization of the identification criteria for fainter objects. We find that around 1.36 (3.08) Type Ia SNe (CCSNe) are identifiable with the magnification method per year in ZTF, but when applying the magnitude cut of m < 19 mag, the detection rates decrease to 0.17 (0.32) per year. We compare our simulations with the previously found lensed Type Ia SNe, iPTF16geu and SN Zwicky, and conclude that considering the bias towards highly magnified events, the findings are within expectations in terms of detection rates and lensing properties of the systems. In addition, we provide a set of selection cuts based on simple observables to distinguish glSNe from regular, unlensed, supernovae to select potential candidates for spectroscopic and high-spatial resolution follow-up campaigns. We find optimal cuts in observed colours $g-r$, $g-i$, and $r-i$ as well as in the colour SALT2 fit parameter. The developed pipeline and the simulated lightcurves employed in this analysis can be found in the $LENSIT$ github repository.