Estimation of Physical Stellar Parameters from Spectral Models Using Deep Learning Techniques

Esteban Olivares, Michel Curé, Ignacio Araya, Ernesto Fabregas, Catalina Arcos, Natalia Machuca and Gonzalo Farias ()
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Esteban Olivares: Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Av. Brasil 2147, Valparaíso 2362804, Chile
Michel Curé: Instituto de Física y Astronomía, Universidad de Valparaíso, Av. Gran Bretaña 1111, Valparaíso 2362804, Chile
Ignacio Araya: Centro Multidisciplinario de Física, Universidad Mayor, Santiago 8580745, Chile
Ernesto Fabregas: Departamento de Informática y Automática, Universidad Nacional de Educación a Distancia, Juan del Rosal 16, 28040 Madrid, Spain
Catalina Arcos: Instituto de Física y Astronomía, Universidad de Valparaíso, Av. Gran Bretaña 1111, Valparaíso 2362804, Chile
Natalia Machuca: Instituto de Física y Astronomía, Universidad de Valparaíso, Av. Gran Bretaña 1111, Valparaíso 2362804, Chile
Gonzalo Farias: Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Av. Brasil 2147, Valparaíso 2362804, Chile

Mathematics, 2024, vol. 12, issue 20, 1-19

Abstract: 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 85 % of the expected results according to the scientific literature. The execution time ranged from 6 to 13 min per spectrum, which represents less than 5 % of the total time required for a one-to-one comparison search under the same conditions.

Keywords: data analysis; deep learning; massive stars; fundamental parameters; astronomical databases miscellaneous (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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