Supernova explosions play a central role in many areas of astrophysics: shedding light on the late stages of stellar evolution, giving birth to compact objects, synthesizing chemical elements, and tracking cosmic expansion. Such studies are enabled by the classification of supernovae into different classes that track distinct progenitor systems, evolutionary paths, and explosion physics. Already at the present, only 10% of supernovae are classified spectroscopically, and starting with the Vera C. Rubin Observatory LSST survey in late 2023, only 0.1% will be classified. This simple fact necessitates a new approach of photometric classification. In this talk I will present our recent work on developing and using various machine learning approaches, trained on real rather than simulated data, to classify supernovae. I will describe both broad classification approaches (using multiple supernova classes) and targeted approaches tailored to specific rare types of supernovae. In each case I will also give specific examples of scientific results enabled by these approaches.
To view this talk via YouTube, please visit: https://www.youtube.com/playlist?list=PLb1880Rn0qkKzIavl-n_7RaMyDOiU9XHm