Classification of evolved stars with (unsupervised) machine learning

Jiacheng Tan, Mel Krokos, Jamie Welsh, Cristobal Bordiu, Eva Sciacca, Filomena Bufano

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper documents our analysis on the effectiveness of unsupervised machine learning algorithms for classification of evolved stars based on multi-wavelength photometric measurements. The foundation is a custom made reference dataset compiled from available stellar catalogues for target sources—AGB, Wolf Rayet, luminous blue variable and red supergiant stars. Our results indicate that applying HDBSCAN to UMAP’s feature representation seems to be the most effective approach for this usecase.
Original languageEnglish
Title of host publicationMachine Learning for Astrophysics
Subtitle of host publicationProceedings of the ML4Astro International Conference 30 May - 1 Jun 2022
EditorsSimone Riggi, Eva Sciacca, Francesco Schilliro
Number of pages4
ISBN (Electronic)9783031341670
ISBN (Print)9783031341663
Publication statusPublished - 15 Oct 2023

Publication series

NameAstrophysics and Space Science Proceedings
PublisherSpringer Nature
ISSN (Print)1570-6591
ISSN (Electronic)1570-6605


  • Machine Learning
  • Astrophyics

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