A targeted search for strongly lensed supernovae and expectations for targeted searches in the Rubin era
Authors: Peter Craig, Kyle O'Connor, Sukanya Chakrabarti, Steven A. Rodney, Justin R. Pierel, Curtis McCully, Ismael Perez-Fournon
Abstract: Gravitationally lensed supernovae (glSNe) are of interest for time delay cosmology and SN physics. However, glSNe detections are rare, owing to the intrinsic rarity of SN explosions, the necessity of alignment with a foreground lens, and the relatively short window of detectability. We present the Las Cumbres Observatory Lensed Supernova Search, LCOLSS, a targeted survey designed for detecting glSNe in known strong-lensing systems. Using cadenced $r^\prime$-band imaging, LCOLSS targeted 112 galaxy-galaxy lensing systems with high expected SN rates, based on estimated star formation rates. No plausible glSN was detected by LCOLSS over two years of observing. The analysis performed here measures a detection efficiency for these observations and runs a Monte Carlo simulation using the predicted supernova rates to determine the expected number of glSN detections. The results of the simulation suggest an expected number of detections and $68\%$ Poisson confidence intervals, $N_{SN} = 0.20, [0,2.1] $, $N_{Ia} = 0.08, [0,2.0]$, $N_{CC} = 0.12, [0,2.0]$, for all SN, Type Ia, and core-collapse (CC) SNe respectively. These results are broadly consistent with the absence of a detection in our survey. Analysis of the survey strategy can provide insights for future efforts to develop targeted glSN discovery programs. We thereby forecast expected detection rates for the Rubin observatory for such a targeted survey, finding that a single visit depth of 24.7 mag with the Rubin observatory will detect $0.63 \pm 0.38$ SNe per year, with $0.47 \pm 0.28$ core collapse SNe per year and $0.16 \pm 0.10$ Type Ia SNe per year.
Explore the paper tree
Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant
Look for similar papers (in beta version)
By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.