Detalles de publicación
PP 025096
Neural translation for Stokes inversion and synthesis
(1) IAC, (2) ULL, (3) Institute for Solar Physics, Stockholm University
The physical conditions in stellar atmospheres, such as temperature, velocity, and magnetic fields, can be obtained from the interpretation of solar spectro-polarimetric observations. However, traditional inversion codes, while successful, are computationally demanding, especially for lines whose formation is complex and dictated by non-local thermodynamical equilibrium effects. The
necessity of faster alternatives, particularly with the the increasing volume of data coming from large solar telescopes, 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 Vector Quantized Variational Autoencoders (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, akin to those used in natural language processing,
to perform the translation between these tokenized representations. The model is trained on a comprehensive 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 and crucial advantage, the method described in this paper provides the generative nature of our model, which naturally yields an estimate of the uncertainty in the solution.
necessity of faster alternatives, particularly with the the increasing volume of data coming from large solar telescopes, 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 Vector Quantized Variational Autoencoders (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, akin to those used in natural language processing,
to perform the translation between these tokenized representations. The model is trained on a comprehensive 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 and crucial advantage, the method described in this paper provides the generative nature of our model, which naturally yields an estimate of the uncertainty in the solution.