A neural network approach to determining photometric metallicities of M-type dwarf stars

TL;DR

This paper presents a neural network-based method to accurately estimate the metallicities of M dwarf stars using photometric data, outperforming previous empirical approaches and enabling large-scale stellar analysis.

Contribution

It introduces a novel neural network framework for photometric metallicity estimation of M dwarfs, utilizing multi-band photometry and advanced regularization techniques.

Findings

Achieved uncertainties as low as 0.08dex in metallicity estimates.

Validated the method with binary systems, surpassing 0.1dex accuracy.

Demonstrated the model's effectiveness for spectral types up to M5.0V.

Abstract

M dwarfs are the most abundant stars in the Galaxy and serve as key targets for stellar and exoplanetary studies. It is particularly challenging to determine their metallicities because their spectra are complex. For this reason, several authors have focused on photometric estimates of the M-dwarf metallicity. Although artificial neural networks have been used in the framework of modern astrophysics, their application to a photometric metallicity estimate for M dwarfs remains unexplored. We develop an accurate method for estimating the photometric metallicities of M dwarfs using artificial neural networks to address the limitations of traditional empirical approaches. We trained a neural network on a dataset of M dwarfs with spectroscopically derived metallicities. We used eight absolute magnitudes in the visible and infrared from Gaia, 2MASS, and WISE as input features. Batch normalization and dropout regularization stabilized the training and prevented overfitting. We applied the Monte Carlo dropout technique to obtain more robust predictions. The neural network demonstrated a strong performance in estimating photometric metallicities for M dwarfs in the range of -0.45<[Fe/H]<0.45dex and for spectral types as late as M5.0V. On the test sample, the predictions showed uncertainties down to 0.08dex. This surpasses the accuracy of previous methods. We further validated our results using an additional sample of 46 M dwarfs in wide binary systems with FGK-type primary stars with well-defined metallicities and achieved an excellent predictive performance that surpassed the 0.1dex error threshold. This study introduces a ML-based framework for estimating the photometric metallicities of M dwarfs and provides a scalable data-driven solution for analyzing large photometric surveys. The results outline the potential of artificial neural networks to enhance the determination of stellar parameters.