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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.

Information

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

References

Barrow, J.D. and Saich, P. 1993, MNRAS, 3, 717 CrossRefGoogle Scholar
Dobrycheva, D.V. 2013, Odessa Astron. Publ., 26, 187 Google Scholar
Dobrycheva, D.V., Vavilova, I.B., Melnyk, O.V. et al. 2018, Kinemat. Phys. Cel. Bodies, 34, 290 Google Scholar
Elyiv, A.A., Melnyk, O.V., Vavilova, I.B. et al. 2020, AA, 635, A124 Google Scholar
Khramtsov, V., Dobrycheva, D.V., Vasylenko, M.Y. et al. 2019, Odessa Astron. Publ., 32, 21 CrossRefGoogle Scholar
Peng, Y., Lilly, S.J., Kovac, K. et al. 2010, ApJ, 721, 193 Google Scholar
Reid, B.A., Samushia, L., White, M. et al. 2010, MNRAS, 426, 2719 CrossRefGoogle Scholar
Vasylenko, M. Y., Dobrycheva, D. V., Vavilova, I. B. et al. 2019, Odessa Astron. Publ., 32, 46 Google Scholar
Vasylenko, M., Dobrycheva, D., Khramtsov, V., et al. 2020, Communications of BAO, 67, 354 Google Scholar
Vavilova, I., Dobrycheva, D., Vasylenko, M. et al. 2020, in: P. Skoda & F. Adam, Knowledge Discovery in Big Data from Astronomy and Earth Observation (Elsevier), p. 307–323CrossRefGoogle Scholar
Vavilova, I., Pakuliak, L., Babyk, I. et al. 2020, in: P. Skoda & F. Adam, Knowledge Discovery in Big Data from Astronomy and Earth Observation (Elsevier), p. 57–102CrossRefGoogle Scholar
Vavilova, I., Elyiv, A., Dobrycheva, D., Melnyk, O. 2021, in: I. Zelinka, M. Brescia, & D. Baron Intelligent Astrophysics (Springer), p. 57–79Google Scholar
Vavilova, I.B., Dobrycheva, D.V., Vasylenko, M.Y. et al. 2021, AA, 648, A122 CrossRefGoogle Scholar
Vavilova, I.B., Dobrycheva, D.V., Vasylenko, M. Yu. et al. 2018, VizieR Online Data Catalog, J/A+A/648/A122CrossRefGoogle Scholar
Willett, K.W., Lintott, C.J., Bamford, S.P. et al. 2013, MNRAS, 435, 2835 CrossRefGoogle Scholar