Recovering thermodynamics from spectral profiles observed by IRIS using machine and deep learning techniques

mardi 19 mars 2019, par Alberto Sainz Dalda (Lockheed-Martin Solar and Astrophysics Laboratory / BAERI, USA)

Mercredi 20 mars 2019 à 11h00 , Lieu : Salle de conférence du bâtiment 17

We present three novel methods to recover the physical information from spectral profiles suitable to be inverted from an iterative solution of the radiative transfer equation. We combine the meaningful results provided by these traditional methods with machine and deep learning techniques to obtain similar-quality results in a easy-to-use, faster way. We have applied these new methods to Mg II h&k lines observed by IRIS. As a result, we are able to obtain the thermodynamics in the chromosphere and high photosphere in a few CPU-minutes, speeding up the process in a factor of 105-106. The open-source code developed to this aim will allow the community to use IRIS observations to open a new window to a host of solar phenomena.


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