Using probabilistic neural network to analyze the binary stars Schulte 3, EY Cep, HD 101131, and Haro 1-14c
The use of artificial neural networks (ANNs) in physical sciences has increased recently. Determining the orbital elements of binary systems helps us to obtain fundamental information. In this paper, ANNs were used to find the corresponding orbital and spectroscopic elements of four double-lined s...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Prince of Songkla University
2018-06-01
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| Series: | Songklanakarin Journal of Science and Technology (SJST) |
| Subjects: | |
| Online Access: | https://rdo.psu.ac.th/sjstweb/journal/40-3/24.pdf |
| _version_ | 1828745407546523648 |
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| author | Ali Pirkhedri Kamal Ghaderi |
| author_facet | Ali Pirkhedri Kamal Ghaderi |
| author_sort | Ali Pirkhedri |
| collection | DOAJ |
| description | The use of artificial neural networks (ANNs) in physical sciences has increased recently. Determining the orbital
elements of binary systems helps us to obtain fundamental information. In this paper, ANNs were used to find the corresponding
orbital and spectroscopic elements of four double-lined spectroscopic binary stars: Schulte 3, EY Cep, HD 101131, and Haro 1-
14c. The orbital parameters of the radial velocity curve obtained from ANNs were compared with other traditional methods and
we show that the proposed method is of high accuracy. Our numerical results are in good agreement with those obtained by
others using nonlinear regression methods. We show the validity of our new method in a wide range of different types of binary.
In this method, the time consumed is considerably less than in the other traditional methods. The present method is applicable to
orbits of all eccentricities and inclination angles and enables one to vary all of the unknown parameters simultaneously. |
| first_indexed | 2024-04-14T04:02:36Z |
| format | Article |
| id | doaj.art-65178be46a38405ebfa8f4ca0877b119 |
| institution | Directory Open Access Journal |
| issn | 0125-3395 |
| language | English |
| last_indexed | 2024-04-14T04:02:36Z |
| publishDate | 2018-06-01 |
| publisher | Prince of Songkla University |
| record_format | Article |
| series | Songklanakarin Journal of Science and Technology (SJST) |
| spelling | doaj.art-65178be46a38405ebfa8f4ca0877b1192022-12-22T02:13:30ZengPrince of Songkla UniversitySongklanakarin Journal of Science and Technology (SJST)0125-33952018-06-0140367668110.14456/sjst-psu.2018.89Using probabilistic neural network to analyze the binary stars Schulte 3, EY Cep, HD 101131, and Haro 1-14cAli Pirkhedri0Kamal Ghaderi1Department of Science and Engineering, Islamic Azad University, Marivan Branch, Marivan, IranDepartment of Science and Engineering, Islamic Azad University, Marivan Branch, Marivan, IranThe use of artificial neural networks (ANNs) in physical sciences has increased recently. Determining the orbital elements of binary systems helps us to obtain fundamental information. In this paper, ANNs were used to find the corresponding orbital and spectroscopic elements of four double-lined spectroscopic binary stars: Schulte 3, EY Cep, HD 101131, and Haro 1- 14c. The orbital parameters of the radial velocity curve obtained from ANNs were compared with other traditional methods and we show that the proposed method is of high accuracy. Our numerical results are in good agreement with those obtained by others using nonlinear regression methods. We show the validity of our new method in a wide range of different types of binary. In this method, the time consumed is considerably less than in the other traditional methods. The present method is applicable to orbits of all eccentricities and inclination angles and enables one to vary all of the unknown parameters simultaneously.https://rdo.psu.ac.th/sjstweb/journal/40-3/24.pdfprobabilistic neural networkbinary systemseclipsingvelocity curve |
| spellingShingle | Ali Pirkhedri Kamal Ghaderi Using probabilistic neural network to analyze the binary stars Schulte 3, EY Cep, HD 101131, and Haro 1-14c Songklanakarin Journal of Science and Technology (SJST) probabilistic neural network binary systems eclipsing velocity curve |
| title | Using probabilistic neural network to analyze the binary stars Schulte 3, EY Cep, HD 101131, and Haro 1-14c |
| title_full | Using probabilistic neural network to analyze the binary stars Schulte 3, EY Cep, HD 101131, and Haro 1-14c |
| title_fullStr | Using probabilistic neural network to analyze the binary stars Schulte 3, EY Cep, HD 101131, and Haro 1-14c |
| title_full_unstemmed | Using probabilistic neural network to analyze the binary stars Schulte 3, EY Cep, HD 101131, and Haro 1-14c |
| title_short | Using probabilistic neural network to analyze the binary stars Schulte 3, EY Cep, HD 101131, and Haro 1-14c |
| title_sort | using probabilistic neural network to analyze the binary stars schulte 3 ey cep hd 101131 and haro 1 14c |
| topic | probabilistic neural network binary systems eclipsing velocity curve |
| url | https://rdo.psu.ac.th/sjstweb/journal/40-3/24.pdf |
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