SuperRAENN: A Semi-supervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium Deep Survey Supernovae

Understand

Automated classification of supernovae (SNe) based on optical photometric light curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin Observatory.

• Photometric classification can enable real-time identification of interesting events for extended multi-wavelength follow-up, as well as archival population studies.
• Here we present the complete sample of 5,243 "SN-like" light curves (in griz) from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS).
• The PS1-MDS is similar to the planned LSST Wide-Fast-Deep survey in terms of cadence, filters and depth, making this a useful training set for the community.