Austrian Early Career Conference 2024

Contribution:
Poster

Authors:
L. Steinwender [1], P. G. Beck [2,3], K. Hambleton [4], A. Hanslmeier [5]

Affiliations:
1: Graz University of Technology; 2: Instituto de Astrofísica de Canarias; 3: Universidad de La Laguna; 4: Villanova University; 5: University of Graz

Title:
Unsupervised Classification of RR Lyrae Stars

Abstract:
Big Datasets are becoming increasingly important in the field of astrophysics due to new powerful telescopes and surveys continuously observing our night sky, producing giant datasets. A prominent example of such large data streams is Vera C. Rubin LSST, designed to simultaneously monitor the variable southern night sky in 6 colors down to a limiting magnitude of r~24. Unsupervised machine learning (ML), a method of learning from patterns within the data, is key for gaining insight into this expected excess of photometric data.
We present our prototype classifier dedicated to classifying RR Lyrae variable stars into their relevant subclasses (RRab, RRc, and RRd), optimized for the expected needs of Rubin LSST. Using the classification from the RR Lyrae Catalog of the ESA Gaia mission, we constructed training sets containing ~30000 lightcurves using data from the Zwicky Transient Facility Catalog of Periodic Variable Stars (ZTF CPVS) and lightcurves extracted from Full-Frame-Images of the NASA TESS space telescope. We further preprocessed and analyzed these lightcurves using a custom-built Python package. Subsequently, using a Variational Autoencoder (VAE), we created a deep generative model to project the lightcurves into a low-dimensional representation, which quantitatively describes the characteristic shape elements of the lightcurves. In preparation for the Rubin LSST data, we apply our pipeline to the data of the Zwicky Transient Factory (ZTF), a precursor facility for Rubin LSST. We show how the results are improved with the inclusion of physical features such as period and variational amplitude. Through unsupervised clustering of this data representation, we identify the RR Lyrae subclasses in the ZTF data, which we anticipate to be easily adaptable to the Rubin LSST data.