# 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…

## 7 Citations

### Stokes inversion techniques with neural networks: analysis of uncertainty in parameter estimation

- Computer Science
- 2022

This paper provides end-to-end inversion codes based on the simple Milne-Eddington model of the stellar atmosphere and deep neural networks to both parameter estimation and their uncertainty intervals and demonstrates that the proposed architecture provides high accuracy of results, including a reliable uncertainty estimation, even in the multidimensional case.

### Convolutional Neural Networks and Stokes Response Functions

- Environmental ScienceThe Astrophysical Journal
- 2022

In this work, we study the information content learned by a convolutional neural network (CNN) when trained to carry out the inverse mapping between a database of synthetic Ca ii intensity spectra…

### Exploring the Sun’s upper atmosphere with neural networks: Reversed patterns and the hot wall effect

- Physics
- 2021

We have developed an inversion procedure designed for high-resolution solar spectro-polarimeters, such as those of Hinode and the DKIST. The procedure is based on artificial neural networks trained…

### Inferring Line-of-sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks

- Computer ScienceThe Astrophysical Journal
- 2022

A new deep learning method, named Stacked Deep Neural Networks (SDNN), for inferring line-of-sight (LOS) velocities and Doppler widths from Stokes profiles collected by the Near InfraRed Imaging Spectropolarimeter (NIRIS) on the 1.6 m Goode Solar Telescope (GST) at the Big Bear Solar Observatory (BBSO).

### Diagnostic capabilities of spectropolarimetric observations for understanding solar phenomena

- Physics, Environmental ScienceAstronomy & Astrophysics
- 2021

Future ground-based telescopes will expand our capabilities for simultaneous multi-line polarimetric observations in a wide range of wavelengths, from the near-ultraviolet to the near-infrared. This…

### Paradigm Shift Through the Integration of Physical Methodology and Data Science

- Computer ScienceArXiv
- 2021

Methods that integrate traditional physical and data science methodologies are new methods of mathematical analysis that complement both methodologies and are being studied in various scientific fields.

### Deep learning application for stellar parameters determination: I-constraining the hyperparameters

- Computer Science, PhysicsOpen Astronomy
- 2022

A step-by-step overview of how to select the optimal parameters for deriving the most accurate values for the stellar parameters of stars using the convolutional neural network architecture is given.

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