Superluminous Supernovae in LSST: Rates, Detection Metrics, and Light Curve Modeling

TL;DR

This paper assesses LSST's ability to detect and analyze superluminous supernovae, estimating discovery rates, light curve quality, and parameter recovery, highlighting the importance of survey strategy for maximizing scientific return.

Contribution

It introduces a method to simulate and evaluate LSST's detection efficiency and parameter recovery for SLSNe, providing insights into optimal survey strategies.

Findings

Approximately 10,000 SLSNe per year will be discovered in the WFD survey at z<3.0.

Light curves with both rise and decline measurements and at least fifty observations enable ~30% of parameter recovery.

Increasing cadence and minimizing seasonal gaps improve the quality and scientific utility of SLSN light curves.

Abstract

We explore and demonstrate the capabilities of LSST to study Type I superluminous supernovae (SLSNe). We first fit the light curves of 58 known SLSNe at z~0.1-1.6, using an analytical magnetar spin-down model implemented in MOSFiT. We then use the posterior distributions of the magnetar and ejecta parameters to generate thousands of synthetic SLSN light curves, and we inject those into the OpSim to generate realistic ugrizy light curves. We define simple, measurable metrics to quantify the detectability and utility of the light curve, and to measure the efficiency of LSST in returning SLSN light curves satisfying these metrics. We combine the metric efficiencies with the volumetric rate of SLSNe to estimate the overall discovery rate of LSST, and we find that ~10^4 SLSNe per year with >10 data points will be discovered in the WFD survey at z<3.0, while only ~15 SLSNe per year will be discovered in each DDF at z<4.0. To evaluate the information content in the LSST data, we refit representative output light curves with the same model that was used to generate them. We correlate our ability to recover magnetar and ejecta parameters with the simple light curve metrics to evaluate the most important metrics. We find that we can recover physical parameters to within 30% of their true values from ~18% of WFD light curves. Light curves with measurements of both the rise and decline in gri-bands, and those with at least fifty observations in all bands combined, are most information rich, with ~30% of these light curves having recoverable physical parameters to ~30% accuracy. WFD survey strategies which increase cadence in these bands and minimize seasonal gaps will maximize the number of scientifically useful SLSN light curves. Finally, although the DDFs will provide more densely sampled light curves, we expect only ~50 SLSNe with recoverable parameters in each field in the decade-long survey.