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Название: Observational and Theoretical Studies of 27 δ Scuti Stars with Investigation of the Period–luminosity Relation
Авторы: Poro, A.
Paki, E.
Mazhari, G.
Sarabi, S.
Alicavus, F. K.
Farahani, F. A.
Guilani, H.
Popov, A. A.
Zubareva, A. M.
Jalalabadi, B. Z.
Nourmohammad, M.
Davoudi, F.
Arani, Z. S.
Ghalee, A.
Дата публикации: 2021
Издатель: IOP Publishing Ltd
IOP Publishing
Библиографическое описание: Observational and Theoretical Studies of 27 δ Scuti Stars with Investigation of the Period–luminosity Relation / A. Poro, E. Paki, G. Mazhari et al. // Publications of the Astronomical Society of the Pacific. — 2021. — Vol. 133. — Iss. 1026. — 084201.
Аннотация: The multi-color CCD photometric study of 27 δ Scuti stars is presented. By using approximately three years of photometric observations, we obtained the times of maxima and magnitude changes during the observation time interval for each star. The ephemerides of our δ Scuti stars were calculated based on the Markov Chain Monte Carlo (MCMC) method using the observed times of maxima and the period of the stars’ oscillations. We used the Gaia EDR3 parallaxes to calculate the luminosities and also the absolute magnitudes of these δ Scuti stars. The fundamental physical parameters of all the stars in our sample such as masses and radii were estimated. We determined the pulsation modes of the stars based on the pulsation constants. Moreover, the period–luminosity (P–L) relation of δ Scuti stars was investigated and discussed. Then, by using a machine learning classification, new P–L relations for fundamental and overtone modes are presented. © 2021. The Astronomical Society of the Pacific. All rights reserved.
URI: http://elar.urfu.ru/handle/10995/111558
Условия доступа: info:eu-repo/semantics/openAccess
Идентификатор РИНЦ: 47018341
Идентификатор SCOPUS: 85114350148
Идентификатор WOS: 000686783100001
Идентификатор PURE: 23694325
ISSN: 0004-6280
DOI: 10.1088/1538-3873/ac12dc
Сведения о поддержке: This work was supported by the Ministry of Science and Education, FEUZ-2020-0030. Popov A.A. acknowledges support by the Ministry of Science and Higher Education of the Russian Federation under the grant 075-15-2020-780. The machine learning section of this study has been performed according to the scientific agreement with Raderon Lab Inc. (https:// raderonlab.ca) with contract number R\AST\2021\1001. The authors would like to appreciate Dr. Fahri Alicavus and Dr. Somayeh Khakpash for their contributions to the research. Also, great thanks to Paul D. Maley for editing the text. The authors would like to thank the reviewer for comments and suggestions that helped to improve the paper.
Располагается в коллекциях:Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC

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