  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.
