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| Paper: |
Stellar Spectra Models Classification and Parameter Estimation Using Machine Learning Algorithms |
| Volume: |
535, Astronomical Data Analysis Software and Systems XXXI |
| Page: |
83 |
| Authors: |
Flores R., M..; Corral, L. J.; Fierro-Santillan, C. R. |
| Abstract: |
The growth of sky surveys and the large amount of stellar spectra in the current databases, has generated the necessity of developing new methods to estimate stellar parameters, a fundamental task on stellar research. In this work, we present a comparison of different machine learning algorithms, used for the classification of stellar synthetic spectra and the estimation of fundamental stellar parameters including temperature, and luminosity. For both tasks, we established a group of supervised learning models, and propose a database of measurements with the same structure to train the algorithms. These data include equivalent-width measurements over noisy synthetic spectra in order to replicate the natural noise on a real observed spectrum. Different levels of signal to noise ratio are considered for this analysis. |
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