# Scalable End-to-end Recurrent Neural Network for Variable star classification

arXiv: https://arxiv.org/abs/2002.00994 · HTML: https://mlsanity.com/paper/2002.00994/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 9 of 9)

- [Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves](https://mlsanity.com/paper/2201.08482/) (2022)
- [nbi: the Astronomer's Package for Neural Posterior Estimation](https://mlsanity.com/paper/2312.03824/) (2023)
- [Informative Bayesian model selection for RR Lyrae star classifiers](https://mlsanity.com/paper/2105.11531/) (2021)
- [The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves](https://mlsanity.com/paper/2106.03736/) (2021)
- [StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars](https://mlsanity.com/paper/2510.06200/) (2025)
- [Understanding of the properties of neural network approaches for transient light curve approximations](https://mlsanity.com/paper/2209.07542/) (2022)
- [Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shift](https://mlsanity.com/paper/2303.06544/) (2023)
- [Intelligence of Astronomical Optical Telescope: Present Status and Future Perspectives](https://mlsanity.com/paper/2306.16834/) (2023)
- [Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification Framework](https://mlsanity.com/paper/2311.08080/) (2023)

## Abstract (arXiv)

During the last decade, considerable effort has been made to perform automatic classification of variable stars using machine learning techniques. Traditionally, light curves are represented as a vector of descriptors or features used as input for many algorithms. Some features are computationally expensive, cannot be updated quickly and hence for large datasets such as the LSST cannot be applied. Previous work has been done to develop alternative unsupervised feature extraction algorithms for light curves, but the cost of doing so still remains high. In this work, we propose an end-to-end algorithm that automatically learns the representation of light curves that allows an accurate automatic classification. We study a series of deep learning architectures based on Recurrent Neural Networks and test them in automated classification scenarios. Our method uses minimal data preprocessing, can be updated with a low computational cost for new observations and light curves, and can scale up to massive datasets. We transform each light curve into an input matrix representation whose elements are the differences in time and magnitude, and the outputs are classification probabilities. We test our method in three surveys: OGLE-III, Gaia and WISE. We obtain accuracies of about 95% in the main classes and 75% in the majority of subclasses. We compare our results with the Random Forest classifier and obtain competitive accuracies while being faster and scalable. The analysis shows that the computational complexity of our approach grows up linearly with the light curve size, while the traditional approach cost grows as Nlog(N).