# Deep-Learnt Classification of Light Curves

arXiv: https://arxiv.org/abs/1709.06257 · HTML: https://mlsanity.com/paper/1709.06257/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2017.

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

- [The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves](https://mlsanity.com/paper/2106.03736/) (2021)
- [Applications of AI in Astronomy](https://mlsanity.com/paper/2212.01493/) (2022)
- [Learning Representations of Event Time Series with Sparse Autoencoders for Anomaly Detection, Similarity Search, and Unsupervised Classification](https://mlsanity.com/paper/2507.11620/) (2025)
- [Image-Based Multi-Survey Classification of Light Curves with a Pre-Trained Vision Transformer](https://mlsanity.com/paper/2507.11711/) (2025)

## Abstract (arXiv)

Astronomy light curves are sparse, gappy, and heteroscedastic. As a result standard time series methods regularly used for financial and similar datasets are of little help and astronomers are usually left to their own instruments and techniques to classify light curves. A common approach is to derive statistical features from the time series and to use machine learning methods, generally supervised, to separate objects into a few of the standard classes. In this work, we transform the time series to two-dimensional light curve representations in order to classify them using modern deep learning techniques. In particular, we show that convolutional neural networks based classifiers work well for broad characterization and classification. We use labeled datasets of periodic variables from CRTS survey and show how this opens doors for a quick classification of diverse classes with several possible exciting extensions.