Machine learning based stellar classification with highly sparse photometry data [version 2; peer review: 2 approved, 1 approved with reservations]

Background Identifying stars belonging to different classes is vital in order to build up statistical samples of different phases and pathways of stellar evolution. In the era of surveys covering billions of stars, an automated method of identifying these classes becomes necessary. Methods Many clas...

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Bibliographic Details

Published in:	Open Research Europe
Main Authors:	Sebastian Scher, Seán Enis Cody, Albert Zijlstra, Iain McDonald, Nick Cox, Emma Alexander
Format:	Article
Language:	English
Published:	F1000 Research Ltd 2024-08-01
Subjects:	stellar classification photometry astrophysics machine learning sparsity XGBoost eng
Online Access:	https://open-research-europe.ec.europa.eu/articles/4-29/v2