Stellar Spectra Models Classification and Parameter Estimation Using Machine Learning Algorithms

TL;DR

This paper compares various machine learning algorithms for classifying stellar spectra and estimating key stellar parameters, using synthetic data with noise to simulate real observations.

Contribution

It introduces a structured dataset and evaluates multiple supervised learning models for stellar spectra classification and parameter estimation.

Findings

Machine learning algorithms can effectively classify synthetic stellar spectra.

Parameter estimation accuracy varies with signal-to-noise ratio.

The proposed dataset facilitates training and benchmarking of models.

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 atmospheric parameters, a fundamental task on stellar research. In this work we present a comparison of different machine learning algorithms, using for the classification of stellar synthetic spectra and the estimation of fundamental stellar parameters included T_eff(K), log(L/Lo), log g, M/Mo, and Vrot. For both tasks, we established a group of supervised learning models, and propose a database of measures with the same structure to train the algorithms. This data includes equivalent-width types 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.