The Dark Energy Survey 5-year photometrically identified Type Ia Supernovae

TL;DR

This paper presents a highly accurate photometric classification of Type Ia Supernovae from the Dark Energy Survey, utilizing ensemble methods and Bayesian neural networks to improve sample robustness and assess classification confidence.

Contribution

It introduces an improved ensemble classification framework for photometric SN Ia identification, achieving over 98% accuracy and incorporating uncertainty estimates for out-of-distribution detection.

Findings

Identified 1,863 SNe Ia with high confidence from DES data.

Achieved over 98% classification accuracy on simulations.

Demonstrated the use of Bayesian neural networks for uncertainty estimation.

Abstract

As part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the Dark Energy Survey (DES), we present photometrically identified SN Ia samples using multi-band light-curves and host galaxy redshifts. For this analysis, we use the photometric classification framework SuperNNova (SNN; M\"oller et al. 2019) trained on realistic DES-like simulations. For reliable classification, we process the DES SN programme (DES-SN) data and introduce improvements to the classifier architecture, obtaining classification accuracies of more than 98 per cent on simulations. This is the first SN classification to make use of ensemble methods, resulting in more robust samples. Using photometry, host galaxy redshifts, and a classification probability requirement, we identify 1,863 SNe Ia from which we select 1,484 cosmology-grade SNe Ia spanning the redshift range of 0.07 < z < 1.14. We find good agreement between the light-curve properties of the photometrically-selected sample and simulations. Additionally, we create similar SN Ia samples using two types of Bayesian Neural Network classifiers that provide uncertainties on the classification probabilities. We test the feasibility of using these uncertainties as indicators for out-of-distribution candidates and model confidence. Finally, we discuss the implications of photometric samples and classification methods for future surveys such as Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST).