Predicting Stellar Metallicity: A Comparative Analysis of Regression Models for Solar Twin Stars

TL;DR

This study compares various regression models to predict stellar metallicity in solar twin stars, finding that ensemble methods like Random Forest perform best on high-dimensional astronomical data.

Contribution

It provides a comprehensive comparison of regression techniques for stellar metallicity prediction using high-accuracy survey data, highlighting the superior performance of ensemble models.

Findings

Random Forest achieved the highest accuracy with MSE of 0.001628 and R-squared of 0.9266.

Ensemble methods effectively handle complex, high-dimensional astronomical datasets.

Proper model selection is crucial for accurate stellar property predictions.

Abstract

The research focuses on determining the metallicity ([Fe/H]) predicted in the solar twin stars by using various regression modeling techniques which are, Random Forest, Linear Regression, Decision Tree, Support Vector, and Gradient Boosting. The data set that is taken into account here includes Stellar parameters and chemical abundances derived from a high-accuracy abundance catalog of solar twins from the GALAH survey. To overcome the missing values, intensive preprocessing techniques involving, imputation are done. Each model will subjected to training using different critical observables, which include, Mean Squared Error(MSE), Mean Absolute Error(MAE), Root Mean Squared Error(RMSE), and R-squared. Modeling is done by using, different feature sets like temperature: effective temperature(Teff), surface gravity: log g of 14-chemical-abundances namely, (([Na/Fe], [Mg/Fe], [Al/Fe], [Si/Fe], [Ca/Fe], [Sc/Fe], [Ti/Fe], [Cr/Fe], [Mn/Fe], [Ni/Fe], [Cu/Fe], [Zn/Fe], [Y/Fe], [Ba/Fe])). The target variable considered is the metallicity ([Fe/H]). The findings indicate that the Random Forest model achieved the highest accuracy, with an MSE of 0.001628 and an R-squared value of 0.9266. The results highlight the efficacy of ensemble methods in handling complex datasets with high dimensionality. Additionally, this study underscores the importance of selecting appropriate regression models for astronomical data analysis, providing a foundation for future research in predicting stellar properties with machine learning techniques.