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We explore and demonstrate the capabilities of LSST to study Type Isuperluminous supernovae (SLSNe). We first fit the light curves of 58 knownSLSNe at z~0.1-1.6, using an analytical magnetar spin-down model implemented inMOSFiT. We then use the posterior distributions of the magnetar and ejectaparameters to generate thousands of synthetic SLSN light curves, and we injectthose into the OpSim to generate realistic ugrizy light curves. We definesimple, measurable metrics to quantify the detectability and utility of thelight curve, and to measure the efficiency of LSST in returning SLSN lightcurves satisfying these metrics. We combine the metric efficiencies with thevolumetric rate of SLSNe to estimate the overall discovery rate of LSST, and wefind that ~10^4 SLSNe per year with >10 data points will be discovered in theWFD survey at z<3.0, while only ~15 SLSNe per year will be discovered in eachDDF at z<4.0. To evaluate the information content in the LSST data, we refitrepresentative output light curves with the same model that was used togenerate them. We correlate our ability to recover magnetar and ejectaparameters with the simple light curve metrics to evaluate the most importantmetrics. We find that we can recover physical parameters to within 30% of theirtrue values from ~18% of WFD light curves. Light curves with measurements ofboth the rise and decline in gri-bands, and those with at least fiftyobservations in all bands combined, are most information rich, with ~30% ofthese light curves having recoverable physical parameters to ~30% accuracy. WFDsurvey strategies which increase cadence in these bands and minimize seasonalgaps will maximize the number of scientifically useful SLSN light curves.Finally, although the DDFs will provide more densely sampled light curves, weexpect only ~50 SLSNe with recoverable parameters in each field in thedecade-long survey.

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