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Machine learning for the extragalactic astronomy educational manual

Published online by Cambridge University Press:  23 December 2021

Maksym Vasylenko Open the ORCID record for Maksym Vasylenko [Opens in a new window]  and
Daria Dobrycheva
Maksym Vasylenko
Affiliation:
Main Astronomical Observatory of the National Academy of Sciences of Ukraine 27 Akademik Zabolotnyi St., Kyiv, 03143 Ukraine emails: [email protected], [email protected]
Daria Dobrycheva
Affiliation:
Main Astronomical Observatory of the National Academy of Sciences of Ukraine 27 Akademik Zabolotnyi St., Kyiv, 03143 Ukraine emails: [email protected], [email protected]
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Abstract

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We evaluated a new approach to the automated morphological classification of large galaxy samples based on the supervised machine learning techniques (Naive Bayes, Random Forest, Support Vector Machine, Logistic Regression, and k-Nearest Neighbours) and Deep Learning using the Python programming language. A representative sample of ∼315000 SDSS DR9 galaxies at z < 0.1 and stellar magnitudes r < 17.7m was considered as a target sample of galaxies with indeterminate morphological types. Classical machine learning methods were used to binary morphologically classification of galaxies into early and late types (96.4% with Support Vector Machine). Deep machine learning methods were used to classify images of galaxies into five visual types (completely rounded, rounded in-between, smooth cigar-shaped, edge-on, and spiral) with the Xception architecture (94% accuracy for four classes and 88% for cigar-like galaxies). These results created a basis for educational manual on the processing of large data sets in the Python programming language, which is intended for students of the Ukrainian universities.

Keywords

machine learningmorphological galaxy classificationeducation
Type
Poster Paper
Information
Proceedings of the International Astronomical Union , Volume 15 , Symposium S367: Education and Heritage in the Era of Big Data in Astronomy , December 2019 , pp. 461 - 463
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of International Astronomical Union

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