Application of Neural Networks to the study of stellar model solutions

TL;DR

This paper demonstrates how artificial neural networks can effectively identify stellar model solutions and analyze degeneracies, using CG Cyg B as a case study, revealing correlations with initial composition and mixing-length parameters.

Contribution

The study introduces the application of ANNs to identify stellar models matching observational data, highlighting their ability to handle degeneracies in stellar parameter solutions.

Findings

ANN successfully identified models reproducing CG Cyg B's HR diagram position.

Revealed correlations between initial composition and age, mixing-length parameter.

All identified models have a mixing-length parameter below 1.3.

Abstract

Artificial neural networks (ANN) have different applications in Astronomy, including data reduction and data mining. In this work we propose the use ANNs in the identification of stellar model solutions. We illustrate this method, by applying an ANN to the 0.8M$_\odot$ star CG Cyg B. Our ANN was trained using 60,000 different 0.8M$_\odot$ stellar models. With this approach we identify the models which reproduce CG Cyg B's position in the HR diagram. We observe a correlation between the model's initial metal and helium abundance which, in most cases, does not agree with a helium to metal enrichment ratio $\Delta$Y/$\Delta$Z=2. Moreover, we identify a correlation between the model's initial helium/metal abundance and both its age and mixing-length parameter. Additionally, every model found has a mixing-length parameter below 1.3. This means that CG Cyg B's mixing-length parameter is clearly smaller than the solar one. From this study we conclude that ANNs are well suited to deal with the degeneracy of model solutions of solar type stars.