Prediction of Supernova Rates in Known Galaxy-galaxy Strong-lens Systems
Yiping Shu, Adam S. Bolton, Shude Mao, Xi Kang, Guoliang Li, and, Monika Soraisam

TL;DR
This paper introduces a targeted approach to find strongly-lensed supernovae in known galaxy-galaxy lens systems, significantly improving detection efficiency and enabling better cosmological and astrophysical studies.
Contribution
It proposes a novel monitoring strategy focusing on known lens systems, providing rate estimates and detection forecasts for strongly-lensed supernovae.
Findings
Estimated 1.23 Type Ia and 10.4 core-collapse lensed SNe per year in the sample.
Forecasted detection of approximately 0.49 Type Ia and 2.1 core-collapse lensed SNe annually.
Detection is feasible with moderate telescope depth and cadence, enabling efficient follow-up.
Abstract
We propose a new strategy of finding strongly-lensed supernovae (SNe) by monitoring known galaxy-scale strong-lens systems. Strongly lensed SNe are potentially powerful tools for the study of cosmology, galaxy evolution, and stellar populations, but they are extremely rare. By targeting known strongly lensed starforming galaxies, our strategy significantly boosts the detection efficiency for lensed SNe compared to a blind search. As a reference sample, we compile the 128 galaxy-galaxy strong-lens systems from the Sloan Lens ACS Survey (SLACS), the SLACS for the Masses Survey, and the Baryon Oscillation Spectroscopic Survey Emission-Line Lens Survey. Within this sample, we estimate the rates of strongly-lensed Type Ia SN (SNIa) and core-collapse SN (CCSN) to be and events per year, respectively. The lensed SN images are expected to be widely separated with…
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