Variable star classification with a Multiple-Input Neural Network

TL;DR

This paper introduces a Multiple-Input Neural Network that combines convolutional and multi-layer neural networks to classify variable stars using light curves and additional data, achieving high accuracy and improving classification of challenging cases.

Contribution

The study presents a novel neural network architecture that integrates visual light curve features with numerical data for improved variable star classification.

Findings

Achieved 89-99% accuracy for main star classes.

Improved classification of Anomalous Cepheids to nearly 80%.

Demonstrated the effectiveness of combining visual and numerical data.

Abstract

In this experiment, we created a Multiple-Input Neural Network, consisting of Convolutional and Multi-layer Neural Networks. With this setup the selected highest-performing neural network was able to distinguish variable stars based on the visual characteristics of their light curves, while taking also into account additional numerical information (e.g. period, reddening-free brightness) to differentiate visually similar light curves. The network was trained and tested on OGLE-III data using all OGLE-III observation fields, phase-folded light curves and period data. The neural network yielded accuracies of 89--99\% for most of the main classes (Cepheids, $\delta$ Scutis, eclipsing binaries, RR Lyrae stars, Type-II Cepheids), only the first-overtone Anomalous Cepheids had an accuracy of 45\%. To counteract the large confusion between the first-overtone Anomalous Cepheids and the RRab stars we added the reddening-free brightness as a new input and only stars from the LMC field were retained to have a fixed distance. With this change we improved the neural network's result for the first-overtone Anomalous Cepheids to almost 80\%. Overall, the Multiple-input Neural Network method developed by our team is a promising alternative to existing classification methods.