1 INTRODUCTION

2 OPEN SUPERNOVA CATALOG DATA

2.1 Standardizing magnitudes and filters

2.2 Light curve trimming

2.3 Selection cuts

3 GAUSSIAN PROCESSES FOR INTERPOLATION IN TIME AND WAVELENGTH

3.1 Gaussian Processes

3.2 Two-dimensional Gaussian process regression

3.3 Using two-dimensional Gaussian processes to infer spectra from light curves

4 CONVOLUTIONAL NEURAL NETWORKS

4.1 Model architecture

4.2 Model training

5 RESULTS ON CLASSIFYING OPEN SUPERNOVA CATALOG DATA

6 TRANSFER LEARNING ON PLASTICC SUPERNOVA LIGHT CURVES

6.1 Overview

6.2 The PLAsTiCC dataset

6.3 The new classification problem

6.4 Data selection

6.5 Creating heatmaps

6.6 Applying transfer learning to PLAsTiCC light curves

7 CLASSIFYING PLASTICC LIGHT CURVES WITH TRANSFER LEARNING

7.1 Models without transfer learning

7.2 Models with transfer learning

7.3 Limited training set performance

8 DISCUSSION & CONCLUSIONS

8.1 Classifying supernovae from multiple surveys

8.2 Transfer learning for future surveys