Papers › Photometric Data-driven Classification of Type Ia Supernovae in the Open Supernova Catalog
Photometric Data-driven Classification of Type Ia Supernovae in the Open Supernova Catalog
Stanislav Dobryakov, Konstantin Malanchev, Denis Derkach, Mikhail Hushchyn
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.
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