Context. Current and near-future sky survey programmes, like the Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will produce vast amounts of data that will need new techniques to be developed to process them on reasonable timescales. Machine learning methods and properly trained neural networks proved to be efficient, fast, and reliable in performing a variety of tasks such as classification of variable star light curves. Since LSST survey full sky data is not yet available (only the Data Preview 1 from various sky segments), we need to test our methods to be used on real LSST data on proxy data sets for now.
Aims. 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.
Methods. 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.
Results. 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%.
Conclusions. Our results showed that utilizing phase-folded light-curves from multiple passbands and the periods as numerical data inputs we are able to train a neural network with outstanding accuracy.