Variable star classification with a Multiple-Input Neural Network

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.

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Cited by in corpus (7)

• Periodic Variable Star Classification with Deep Learning: Handling Data Imbalance in an Ensemble Augmentation Way
• Application of Convolutional Neural Networks to time domain astrophysics. 2D image analysis of OGLE light curves
• Variable stars in the residual light curves of OGLE-IV eclipsing binaries towards the Galactic Bulge
• Leveraging pre-trained vision Transformers for multi-band photometric light curve classification
• Astrometric Binary Classification Via Artificial Neural Networks
• Sparse logistic regression for RR Lyrae vs binaries classification
• Multiband neural network classification of ZTF light curves as LSST proxies