A New Classification Model for the ZTF Catalog of Periodic Variable Stars

TL;DR

This paper introduces a novel classification model for periodic variable stars in the ZTF catalog, utilizing a convolutional variational autoencoder and hierarchical random forest to improve classification accuracy and identify new non-stellar objects.

Contribution

The study develops a new hierarchical classification approach combining deep learning and random forests, enhancing the classification of ZTF periodic variable stars and identifying previously unclassified objects.

Findings

Achieved over 90% completeness for certain classes of PVSs

Identified new extragalactic objects such as quasars and supernovae

Provided an updated labeled dataset for future research

Abstract

Using the second data release from the Zwicky Transient Facility (ZTF, Bellm et al. 2019), Chen et al. (2020) created a ZTF Catalog of Periodic Variable Stars (ZTF CPVS) of 781, 602 periodic variables stars (PVSs) with 11 class labels. Here, we provide a new classification model of PVSs in the ZTF CPVS using a convolutional variational autoencoder and hierarchical random forest. We cross-match the sky-coordinate of PVSs in the ZTF CPVS with those presented in the SIMBAD catalog. We identify non-stellar objects that are not previously classified, including extragalactic objects such as Quasi-Stellar Objects, Active Galactic Nuclei, supernovae and planetary nebulae. We then create a new labelled training set with 13 classes in two levels. We obtain a reasonable level of completeness (> 90 %) for certain classes of PVSs, although we have poorer completeness in other classes (~ 40 % in some cases). Our new labels for the ZTF CPVS are available via Zenodo Cheung et al. (2021).