A neural network approach to determining photometric metallicities of M-type dwarf stars,Astronomy & Astrophysics

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A neural network approach to determining photometric metallicities of M-type dwarf stars
Astronomy & Astrophysics ( IF 5.8 ) Pub Date : 2025-06-03 , DOI: 10.1051/0004-6361/202554722
C. Duque-Arribas , H. M. Tabernero , D. Montes , J. A. Caballero , E. Galceran

Context. 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.Aims. 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.Methods. 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.Results. The neural network demonstrated a strong performance in estimating photometric metallicities for M dwarfs in the range of −0.45 ≤ [Fe/H] ≤ +0.45 dex and for spectral types as late as M5.0 V. On the test sample, the predictions showed uncertainties down to 0.08 dex. 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 0.1 dex error threshold.Conclusions. This study introduces a machine-learning-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, and they offer promising prospects for future applications.

中文翻译：

一种确定 M 型矮星光度金属性的神经网络方法

上下文。M 矮星是银河系中数量最多的恒星，是恒星和系外行星研究的关键目标。由于它们的光谱很复杂，因此确定它们的金属性特别具有挑战性。出于这个原因，几位作者专注于 M 矮星金属丰度的光度估计。尽管人工神经网络已在现代天体物理学的框架中使用，但它们在 M 矮星的光度金属丰度估计中的应用仍有待探索。目标。我们开发了一种使用人工神经网络估计 M 矮星光度金属丰度的准确方法，以解决传统经验方法的局限性。方法。我们在具有光谱学得出的金属丰度的 M 矮星数据集上训练了一个神经网络。我们使用了来自 Gaia、2MASS 和 WISE 的可见光和红外的八个绝对星等作为输入特征。批量归一化和 dropout 正则化稳定了训练并防止了过拟合。我们应用了 Monte Carlo dropout 技术来获得更稳健的预测。结果。神经网络在估计 -0.45 ≤ [Fe/H] ≤ +0.45 dex 范围内的 M 矮星的光度金属丰度以及晚至 M5.0 V 的光谱类型方面表现出强大的性能。在测试样本上，预测显示不确定性低至 0.08 dex。这超过了以前方法的准确性。我们进一步验证了我们的结果，在宽双星系统中使用了 46 M 矮星的额外样本，这些样本具有具有明确金属丰度的 FGK 型主星，并取得了超过 0.1 dex 误差阈值的出色预测性能。结论。本研究引入了一个基于机器学习的框架，用于估计 M 矮星的光度金属丰度，并为分析大型光度测量提供了可扩展的数据驱动解决方案。结果概述了人工神经网络在增强恒星参数确定方面的潜力，并为未来的应用提供了广阔的前景。