标题： 神经翻译用于斯托克斯反演和合成 显示英文标题

标题： Neural translation for Stokes inversion and synthesis

摘要： [摘要] 恒星大气中的物理条件可以通过对太阳光谱偏振观测的解释获得。然而，传统的反演代码计算量大，特别是对于形成复杂的谱线。更快替代方案的必要性促使了机器学习解决方案的出现。本文介绍了一种受神经机器翻译启发的斯托克斯轮廓反演和合成方法。我们的目标是开发一个生成模型，将斯托克斯轮廓和大气模型视为两种不同的“语言”，编码相同的物理现实。我们构建了一个模型，学习如何在它们之间进行转换，同时提供不确定性估计。我们采用了一种针对斯托克斯参数和模型大气的分词策略，该策略使用VQ-VAE（一种用于将数据压缩成低维形式的神经模型）进行学习。我们反演代码的核心利用了变压器编码器-解码器架构，以在这些分词表示之间进行转换。该模型在一个由各种半经验太阳大气模型扰动产生的合成斯托克斯轮廓数据库上进行训练，确保涵盖了广泛的预期太阳物理条件。该方法有效地从观测到的斯托克斯轮廓重建大气模型，在所考虑光谱线的敏感区域显示出约束更好的模型。由VQ-VAE引起的潜在表示通过压缩斯托克斯轮廓和大气模型的长度来加速反演。此外，它通过减少获得非物理模型的可能性来规范解决方案。作为最终优势，该方法提供了我们模型的生成特性，自然地产生了解决方案的不确定性估计。 显示英文摘要

摘要： [Abridged] The physical conditions in stellar atmospheres can be obtained from the interpretation of solar spectro-polarimetric observations. However, traditional inversion codes are computationally demanding, especially for lines whose formation is complex. The necessity of faster alternatives has motivated the emergence of machine learning solutions. This paper introduces an approach to the inversion and synthesis of Stokes profiles inspired by neural machine translation. Our aim is to develop a generative model that treats Stokes profiles and atmospheric models as two distinct ``languages'' encoding the same physical reality. We build a model that learns how to translate between them, also providing estimates of the uncertainty. We employ a tokenization strategy for both Stokes parameters and model atmospheres, which is learned using a VQ-VAE, a neural model used to compress the data into a lower dimensionality form. The core of our inversion code utilizes a transformer encoder-decoder architecture to perform the translation between these tokenized representations. The model is trained on a database of synthetic Stokes profiles derived from perturbations to various semi-empirical solar atmospheric models, ensuring a wide range of expected solar physical conditions. The method effectively reconstructs atmospheric models from observed Stokes profiles, showing better constrained models within the region of sensitivity of the considered spectral lines. The latent representation induced by the VQ-VAE helps accelerate the inversion by compressing the length of the Stokes profiles and model atmospheres. Additionally, it helps regularize the solution by reducing the chances of obtaining unphysical models. As a final advantage, the method provides the generative nature of our model, which naturally yields an estimate of the uncertainty in the solution.