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| Paper: |
Fundamental Physical Parameters Estimation of O-type Stars Using Artificial Neural Networks |
| Volume: |
538, ADASS XXXII |
| Page: |
269 |
| Authors: |
Miguel Flores R.; Luis J. Corral; Celia R. Fierro-Santillán; Silvana G. Navarro |
| DOI: |
10.26624/NGZN1317 |
| Abstract: |
We present an artificial neural network approach to estimating stellar
luminosity, effective temperature, and surface gravity. The project’s final objective is to
develop a system capable of automatically fitting stellar spectra models and determining
the physical parameters of the stars. In previous work, we tried to establish the best way
to fit a stellar model using different machine-learning models and two primary methods:
the classification of the stellar spectra models and estimates of physical parameters in
a regression-type task. Here, we present the results of implementing a set of recurrent
neural networks trained with a database of synthetic model spectra and the predictions
of O-type stars’ observed spectra. |
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