Photometric Data-driven Classification of Type Ia Supernovae in the Open Supernova Catalog

Abstract

We propose a novel approach for a machine-learning-based detection of the type Ia supernovae using photometric information. Unlike other approaches, only real observation data is used during training. Despite being trained on a relatively small sample, the method shows good results on real data from the Open Supernovae Catalog. We also investigate model transfer from the PLAsTiCC simulations train dataset to real data application, and the reverse, and find the performance significantly decreases in both cases, highlighting the existing differences between simulated and real data.

27 pages, 10 figures, accepted for publication in Astronomy and Computing

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Cited by in corpus (4)

• Pan-chromatic photometric classification of supernovae from multiple surveys and transfer learning for future surveys
• Understanding of the properties of neural network approaches for transient light curve approximations
• Supernova Light Curves Approximation based on Neural Network Models
• Optimizing Supernova Classification with Interpretable Machine Learning Models