Estimation of Physical Stellar Parameters from Spectral Models Using Deep Learning Techniques
This article presents a new algorithm that uses techniques from the field of artificial intelligence to automatically estimate the physical parameters of massive stars from a grid of stellar spectral models. This is the first grid to consider hydrodynamic solutions for stellar winds and radiative tr...
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MDPI AG
2024-10-01
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| author | Esteban Olivares Michel Curé Ignacio Araya Ernesto Fabregas Catalina Arcos Natalia Machuca Gonzalo Farias |
| author_facet | Esteban Olivares Michel Curé Ignacio Araya Ernesto Fabregas Catalina Arcos Natalia Machuca Gonzalo Farias |
| author_sort | Esteban Olivares |
| collection | DOAJ |
| description | This article presents a new algorithm that uses techniques from the field of artificial intelligence to automatically estimate the physical parameters of massive stars from a grid of stellar spectral models. This is the first grid to consider hydrodynamic solutions for stellar winds and radiative transport, containing more than 573 thousand synthetic spectra. The methodology involves grouping spectral models using deep learning and clustering techniques. The goal is to delineate the search regions and differentiate the “species” of spectra based on the shapes of the spectral line profiles. Synthetic spectra close to an observed stellar spectrum are selected using deep learning and unsupervised clustering algorithms. As a result, for each spectrum, we found the effective temperature, surface gravity, micro-turbulence velocity, and abundance of elements, such as helium and silicon. In addition, the values of the line force parameters were obtained. The developed algorithm was tested with 40 observed spectra, achieving <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>85</mn><mo>%</mo></mrow></semantics></math></inline-formula> of the expected results according to the scientific literature. The execution time ranged from 6 to 13 min per spectrum, which represents less than <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5</mn><mo>%</mo></mrow></semantics></math></inline-formula> of the total time required for a one-to-one comparison search under the same conditions. |
| format | Article |
| id | doaj-art-4fa9f4085dba467faeee1e15fa171b3e |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj-art-4fa9f4085dba467faeee1e15fa171b3e2025-08-20T02:10:56ZengMDPI AGMathematics2227-73902024-10-011220316910.3390/math12203169Estimation of Physical Stellar Parameters from Spectral Models Using Deep Learning TechniquesEsteban Olivares0Michel Curé1Ignacio Araya2Ernesto Fabregas3Catalina Arcos4Natalia Machuca5Gonzalo Farias6Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Av. Brasil 2147, Valparaíso 2362804, ChileInstituto de Física y Astronomía, Universidad de Valparaíso, Av. Gran Bretaña 1111, Valparaíso 2362804, ChileCentro Multidisciplinario de Física, Universidad Mayor, Santiago 8580745, ChileDepartamento de Informática y Automática, Universidad Nacional de Educación a Distancia, Juan del Rosal 16, 28040 Madrid, SpainInstituto de Física y Astronomía, Universidad de Valparaíso, Av. Gran Bretaña 1111, Valparaíso 2362804, ChileInstituto de Física y Astronomía, Universidad de Valparaíso, Av. Gran Bretaña 1111, Valparaíso 2362804, ChileEscuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Av. Brasil 2147, Valparaíso 2362804, ChileThis article presents a new algorithm that uses techniques from the field of artificial intelligence to automatically estimate the physical parameters of massive stars from a grid of stellar spectral models. This is the first grid to consider hydrodynamic solutions for stellar winds and radiative transport, containing more than 573 thousand synthetic spectra. The methodology involves grouping spectral models using deep learning and clustering techniques. The goal is to delineate the search regions and differentiate the “species” of spectra based on the shapes of the spectral line profiles. Synthetic spectra close to an observed stellar spectrum are selected using deep learning and unsupervised clustering algorithms. As a result, for each spectrum, we found the effective temperature, surface gravity, micro-turbulence velocity, and abundance of elements, such as helium and silicon. In addition, the values of the line force parameters were obtained. The developed algorithm was tested with 40 observed spectra, achieving <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>85</mn><mo>%</mo></mrow></semantics></math></inline-formula> of the expected results according to the scientific literature. The execution time ranged from 6 to 13 min per spectrum, which represents less than <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5</mn><mo>%</mo></mrow></semantics></math></inline-formula> of the total time required for a one-to-one comparison search under the same conditions.https://www.mdpi.com/2227-7390/12/20/3169data analysisdeep learningmassive starsfundamental parametersastronomical databases miscellaneous |
| spellingShingle | Esteban Olivares Michel Curé Ignacio Araya Ernesto Fabregas Catalina Arcos Natalia Machuca Gonzalo Farias Estimation of Physical Stellar Parameters from Spectral Models Using Deep Learning Techniques Mathematics data analysis deep learning massive stars fundamental parameters astronomical databases miscellaneous |
| title | Estimation of Physical Stellar Parameters from Spectral Models Using Deep Learning Techniques |
| title_full | Estimation of Physical Stellar Parameters from Spectral Models Using Deep Learning Techniques |
| title_fullStr | Estimation of Physical Stellar Parameters from Spectral Models Using Deep Learning Techniques |
| title_full_unstemmed | Estimation of Physical Stellar Parameters from Spectral Models Using Deep Learning Techniques |
| title_short | Estimation of Physical Stellar Parameters from Spectral Models Using Deep Learning Techniques |
| title_sort | estimation of physical stellar parameters from spectral models using deep learning techniques |
| topic | data analysis deep learning massive stars fundamental parameters astronomical databases miscellaneous |
| url | https://www.mdpi.com/2227-7390/12/20/3169 |
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