Multiband neural network classification of ZTF light curves as LSST proxies
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| Format: | Preprint |
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2025
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| author | Szklenár, Tamás Bódi, Attila Szabó, Róbert |
| author_facet | Szklenár, Tamás Bódi, Attila Szabó, Róbert |
| contents | In this project we use data obtained by Zwicky Transient Facility to develop and test a neural-network-based, multiband classification algorithm to classify periodic variable stars (i.e. pulsating variable stars and eclipsing binaries). The aim is to utilize the algorithm on LSST data once they become available. Phase-folded light curve images and period information were used from five different variable star types: Classical and Type II Cepheids, δ Scuti stars, eclipsing binaries, and RR Lyrae stars. The data is taken from the 17th data release of ZTF, from which we used two passbands, g and r in this project. The periods were calculated from the raw data and this information was used as an additional numerical input in the neural network. For the training and testing process a supervised machine learning method was created, the neural network contains Convolutional Neural Networks concatenated with Fully Connected Layers.
During the training-validation process the training accuracy reached 99% and the validation accuracy peaked at 95.6%. At the test classification phase three variable star types out of the 5 classes were classified with around 99% of accuracy, the other two also had very high accuracy, 89.6% and 93.6%. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_13395 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multiband neural network classification of ZTF light curves as LSST proxies Szklenár, Tamás Bódi, Attila Szabó, Róbert Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics In this project we use data obtained by Zwicky Transient Facility to develop and test a neural-network-based, multiband classification algorithm to classify periodic variable stars (i.e. pulsating variable stars and eclipsing binaries). The aim is to utilize the algorithm on LSST data once they become available. Phase-folded light curve images and period information were used from five different variable star types: Classical and Type II Cepheids, δ Scuti stars, eclipsing binaries, and RR Lyrae stars. The data is taken from the 17th data release of ZTF, from which we used two passbands, g and r in this project. The periods were calculated from the raw data and this information was used as an additional numerical input in the neural network. For the training and testing process a supervised machine learning method was created, the neural network contains Convolutional Neural Networks concatenated with Fully Connected Layers. During the training-validation process the training accuracy reached 99% and the validation accuracy peaked at 95.6%. At the test classification phase three variable star types out of the 5 classes were classified with around 99% of accuracy, the other two also had very high accuracy, 89.6% and 93.6%. |
| title | Multiband neural network classification of ZTF light curves as LSST proxies |
| topic | Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics |
| url | https://arxiv.org/abs/2512.13395 |