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

arXiv: https://arxiv.org/abs/2006.10489 · HTML: https://mlsanity.com/paper/2006.10489/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [Supernova Light Curves Approximation based on Neural Network Models](https://mlsanity.com/paper/2206.13306/) (2022)
- [Understanding of the properties of neural network approaches for transient light curve approximations](https://mlsanity.com/paper/2209.07542/) (2022)

## Abstract (arXiv)

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.