Multiband neural network classification of ZTF light curves as LSST proxies

TL;DR

This paper develops a neural network classifier using ZTF data to identify periodic variable stars, aiming to apply it to future LSST data, achieving high accuracy across multiple star types.

Contribution

The study introduces a multiband neural network approach combining CNNs and fully connected layers for variable star classification using ZTF data, with potential application to LSST.

Findings

Training accuracy reached 99%.

Validation accuracy peaked at 95.6%.

Three star types classified with ~99% accuracy.

Abstract

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, {\delta} 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%.