Given the widespread availability of grids of models for stellar atmospheres, it is necessary to recover intermediate atmospheric models by means of accurate techniques that go beyond simple linear interpolation and capture the intricacies of the data. Our goal is to establish a reliable, precise, lightweight, and fast method for recovering stellar model atmospheres, that is to say the stratification of mass column, temperature, gas pressure, and electronic density with optical depth given any combination of the defining atmospheric specific parameters: metallicity, effective temperature, and surface gravity, as well as the abundances of other key chemical elements. We employed a fully connected deep neural network which in turn uses a 1D convolutional auto-encoder to extract the nonlinearities of a grid using the ATLAS9 and MARCS model atmospheres. This new method we call iNNterpol effectively takes into account the nonlinearities in the relationships of the data as opposed to traditional machine-learning methods, such as the light gradient boosting method (LightGBM), that are repeatedly used for their speed in well-known competitions with reduced datasets. We show a higher precision with a convolutional auto-encoder than using principal component analysis as a feature extractor.We believe it constitutes a useful tool for generating fast and precise stellar model atmospheres, mitigating convergence issues, as well as a framework for future developments. The code and data for both training and direct interpolation are available online at https://github.com/cwestend/iNNterpol for full reproducibility and to serve as a practical starting point for other continuous 1D data in the field and elsewhere.

翻译：鉴于恒星大气模型网格的广泛可用性，需要采用超越简单线性插值并能捕捉数据复杂性的精确技术来恢复中间大气模型。我们的目标是建立一种可靠、精确、轻量且快速的恒星模型大气恢复方法，即根据给定的大气特征参数组合（金属丰度、有效温度、表面重力及其他关键化学元素丰度），恢复质量柱密度、温度、气体压强和电子密度随光学深度的分层结构。我们采用全连接深度神经网络，该网络利用一维卷积自编码器提取ATLAS9和MARCS模型大气网格的非线性特征。这种新方法称为iNNterpol，有效考虑了数据关系中的非线性特性，而传统机器学习方法如轻量梯度提升法（LightGBM）虽因处理小规模数据集的快速性能而常用于知名竞赛，却难以应对此类非线性关系。相比于主成分分析特征提取方法，我们展示了一维卷积自编码器具有更高的精度。我们认为该方法能够生成快速精确的恒星模型大气，缓解收敛问题，并为未来发展提供框架框架。训练与直接插值的代码及数据已在https://github.com/cwestend/iNNterpol开源，以实现完全可复现性，并为该领域及其他领域的连续一维数据提供实用起点。