# A CNN adapted to time series for the classification of Supernovae

arXiv: https://arxiv.org/abs/1901.00461 · HTML: https://mlsanity.com/paper/1901.00461/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://mlsanity.com/paper/2302.02515/) (2023)
- [Stratified Learning: A General-Purpose Statistical Method for Improved Learning under Covariate Shift](https://mlsanity.com/paper/2106.11211/) (2021)
- [A Critical Review of the state-of-the-art on Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes](https://mlsanity.com/paper/2109.02178/) (2021)
- [IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications](https://mlsanity.com/paper/2109.13732/) (2021)

## Abstract (arXiv)

Cosmologists are facing the problem of the analysis of a huge quantity of data when observing the sky. The methods used in cosmology are, for the most of them, relying on astrophysical models, and thus, for the classification, they usually use a machine learning approach in two-steps, which consists in, first, extracting features, and second, using a classifier. In this paper, we are specifically studying the supernovae phenomenon and especially the binary classification "I.a supernovae versus not-I.a supernovae". We present two Convolutional Neural Networks (CNNs) defeating the current state-of-the-art. The first one is adapted to time series and thus to the treatment of supernovae light-curves. The second one is based on a Siamese CNN and is suited to the nature of data, i.e. their sparsity and their weak quantity (small learning database).