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The CNN classification of galaxies by their image morphological peculiarities

Published online by Cambridge University Press:  20 January 2023

D. Dobrycheva Open the ORCID record for D. Dobrycheva [Opens in a new window] ,
V. Khramtsov Open the ORCID record for V. Khramtsov [Opens in a new window] ,
M. Vasylenko Open the ORCID record for M. Vasylenko [Opens in a new window]  and
I. Vavilova Open the ORCID record for I. Vavilova [Opens in a new window]
D. Dobrycheva
Affiliation:
Main Astronomical Observatory of the NAS of Ukraine, 27 Akademik Zabolotny Str., Kyiv, 03143, Ukraine
V. Khramtsov
Affiliation:
Institute of Astronomy, V.N. Karazin Kharkiv National University, 35 Sumska St., Kharkiv, 61022, Ukraine
M. Vasylenko
Affiliation:
Main Astronomical Observatory of the NAS of Ukraine, 27 Akademik Zabolotny Str., Kyiv, 03143, Ukraine Institute of Physics of the National Academy of Sciences of Ukraine, 46 avenue Nauka, Kyiv, 03028, Ukraine
I. Vavilova
Affiliation:
Main Astronomical Observatory of the NAS of Ukraine, 27 Akademik Zabolotny Str., Kyiv, 03143, Ukraine
Rights & Permissions [Opens in a new window]

Abstract

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Multidimensional mathematical analysis, like Machine Learning techniques, determines the different features of objects, which is difficult for the human mind. We create a machine learning model to predict galaxies’ detailed morphology (∼ 300000 SDSS-galaxies with z < 0.1) and train it on a labeled dataset defined within the Galaxy Zoo 2 (GZ2). We use convolutional neural networks (CNNs) to classify the galaxies into five visual types (completely rounded, rounded in-between, smooth cigar-shaped, edge-on, and spiral) and 34 morphological classes attaining >94% of accuracy for five-class morphology prediction except for the cigar-shaped (∼ 87%) galaxies.

Keywords

methods: data analysisgalaxies: generalsurveysmethods: convolutional neural networks, etc.
Type
Contributed Paper
Information
Proceedings of the International Astronomical Union , Volume 16 , Symposium S362: The Predictive Power of Computational Astrophysics as a Discovery Tool , June 2020 , pp. 111 - 115
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of International Astronomical Union

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