Machine learning initialization to accelerate Stokes profile inversions

@article{Gafeira2021MachineLI,
  title={Machine learning initialization to accelerate Stokes profile inversions},
  author={Ricardo Gafeira and David Orozco Su'arez and Ivan Mili{\'c} and Carlos Quintero Noda and Basilio Ruiz Cobo and Han Uitenbroek},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.09651}
}
Context. At present, an exponential growth in scientific data from current and upcoming solar observatories is expected. Most of the data consist of high spatial and temporal resolution cubes of Stokes profiles taken in both local thermodynamic equilibrium (LTE) and non-LTE spectral lines. The analysis of such solar observations requires complex inversion codes. Hence, it is necessary to develop new tools to boost the speed and efficiency of inversions and reduce computation times and costs… 

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