Orateur
Description
With millions of detections per night the Vera C. Rubin Observatory (Rubin) will detect millions of supernovae (SNe) over the next ten years. This dataset presents a great opportunity to characterize core-collapse supernovae (SNCC) at redshifts > 0.2. Especially rates and properties of SNCC at these distances have been challenging to measure statistically until now.
I present a machine learning approach for the photometric classification of SNCC into their broad subtypes (SNII, SNIb/c). In preparation for Rubin, I use the Dark Energy Survey (DES) as a benchmark. The classifier is trained on simulations and tested on DES observations. My classifier achieves an accuracy of over 90% on the simulations with contamination below 5%. Validation of the method using low redshift SNCC samples from DES yields contamination below 20%. In this talk, I will present SNCC population properties at redshifts spanning 0.1 < z < 0.7 from DES, effectively extending the SNCC luminosity function and rate measurements to higher redshifts with a more diverse sample compared to the literature. I will conclude by discussing the implications of photometric SNCC classification and property estimations in the era of Rubin.