Variable Star Classification with a Multiple-input Neural Network
Abstract
In this experiment, we created a Multiple-Input Neural Network, consisting of convolutional and multilayer 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 Optical Gravitational Lensing Experiment-III (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, δ 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.
- Publication:
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The Astrophysical Journal
- Pub Date:
- October 2022
- DOI:
- 10.3847/1538-4357/ac8df3
- arXiv:
- arXiv:2209.02310
- Bibcode:
- 2022ApJ...938...37S
- Keywords:
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- Astronomy data analysis;
- Delta Scuti variable stars;
- RR Lyrae variable stars;
- Eclipsing binary stars;
- Neural networks;
- Cepheid variable stars;
- Classification;
- 1858;
- 370;
- 1410;
- 444;
- 1933;
- 218;
- 1907;
- Astrophysics - Solar and Stellar Astrophysics;
- Astrophysics - Instrumentation and Methods for Astrophysics
- E-Print:
- doi:10.3847/1538-4357/ac8df3