The Dark Energy Survey Supernova Program: Cosmological biases from supernova photometric classification

Understand

Cosmological analyses of samples of photometrically-identified Type Ia supernovae (SNe Ia) depend on understanding the effects of 'contamination' from core-collapse and peculiar SN Ia events.

• We employ a rigorous analysis on state-of-the-art simulations of photometrically identified SN Ia samples and determine cosmological biases due to such 'non-Ia' contamination in the Dark Energy Survey (DES) 5-year SN sample.
• As part of the analysis, we test on our DES simulations the performance of SuperNNova, a photometric SN classifier based on recurrent neural networks.
• Depending on the choice of non-Ia SN models in both the simulated data sample and training sample, contamination ranges from 0.8-3.5 %, with the efficiency of the classification from 97.7-99.5 %.