Machine learning classification of RR Lyrae stars observed by TESS

Lukas Steinwender*, Paul G. Beck, Kelly Hambleton, Ceca Kraisnikovic (Editor), Manuela Stadlober-Temmer, Arnold Hanslmeier

*Corresponding author for this work

Research output: Contribution to conferencePosterpeer-review


In this study we present first results of the investigation of different ML-classifiers and their ability to distinguish between different subclasses of RR Lyrae stars (RRab and RRc) from the morphology of TESS-lightcurves. From TESS full-frame images we extracted over 3000 lightcurves of stars listed as RRab and RRc in the General Catalogue of Variable Stars (GCVS). The extracted lightcurves were preprocessed and analyzed with a dedicated python-package. For more than 100 RR Lyrae stars, we determined pulsation periods that are not yet listed in the GCVS and verified the given periods of the remaining ones. On the extracted lightcurves, we test and compare the performance of three unsupervised clustering algorithms (DBSCAN, HDBSCAN, KMeans) in combination with different projection techniques (t-SNE, UMAP) against the classes provided in the GCVS. Independent observations from other surveys such as GAIA will further improve the accuracy of this automated classification procedure, which in the future we plan to apply to different pulsator-classes.
Original languageEnglish
Publication statusPublished - Jul 2022
EventTASC6/KASC13 Workshop - Leuven, Belgium
Duration: 11 Jul 202215 Jul 2022


WorkshopTASC6/KASC13 Workshop
Internet address


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