1 INTRODUCTION
2 DES-SN 5-YEAR
2.1 DES-SN Candidate Sample
2.2 Filtering Multi-Season and Other Transients
2.3 Selection Requirements (Cuts)
3 PHOTOMETRIC CLASSIFICATION
3.1 DES-SN Data Pre-Processing
3.2 Simulations of the DES-SN Survey
3.3 SuperNNova (SNN)
3.4 SNN Configuration for Performance and Robustness
3.5 SNN Trained Models for DES-SN 5-Year Analysis
3.6 Bayesian Neural Networks (BNNs)
4 DES-SN 5-YEAR PHOTOMETRICALLY CLASSIFIED SNE IA
4.1 Photometric Classification
4.2 Cuts Towards a Cosmology Sample (JLA-like)
4.3 Contamination
4.4 Classification Efficiency
4.5 Colour and Stretch Evolution
5 PHOTOMETRICALLY CLASSIFIED SNE IA WITH BAYESIAN NEURAL NETWORKS
5.1 BNN Photometric Sample
5.2 BNN Uncertainties
5.3 BNN Photometric Sample Contribution
6 FROM DES TO RUBIN OBSERVATORY LSST
6.1 DES-SNIa Photometric Samples
6.2 Bayesian Neural Networks as a Proxy
7 CONCLUSIONS