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arXiv · 2209.02310

Variable star classification with a Multiple-Input Neural Network

T. Szklenár·A. Bódi·D. Tarczay-Nehéz·K. Vida·Gy. Mező·R. Szabó
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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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T. Szklenár, A. Bódi, D. Tarczay-Nehéz, K. Vida, Gy. Mező, R. Szabó. 2022-09-06. Variable star classification with a Multiple-Input Neural Network. https://doi.org/10.3847/1538-4357/ac8df3

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