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SICON: Stokes Inversion based on COnvolutional Neural networks

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Ada Coda
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SICON: Stokes Inversion based on COnvolutional Neural networks

Postby Ada Coda » Fri May 31, 2019 10:16 pm

SICON: Stokes Inversion based on COnvolutional Neural networks

Abstract: SICON (Stokes Inversion based on COnvolutional Neural networks) provides a three-dimensional cube of thermodynamical and magnetic properties from the interpretation of two-dimensional maps of Stokes profiles by use of a convolutional neural network. In addition to being much faster than parallelized inversion codes, SICON, when trained on synthetic Stokes profiles from two numerical simulations of different structures of the solar atmosphere, also provided a three-dimensional view of the physical properties of the region of interest in geometrical height, and pressure and Wilson depression properties that are decontaminated from the blurring effect of instrumental point spread functions.

Credit: Asensio Ramos, A.; Diaz Baso, C.

Site: https://github.com/aasensio/sicon
https://ui.adsabs.harvard.edu/abs/2019arXiv190403714A

Bibcode: 2019ascl.soft05024A

ID: ascl:1905.024

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