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Recovering Thermodynamics from Spectral Profiles observed by IRIS: A Machine and Deep Learning Approach
Stockholm University, Faculty of Science, Department of Astronomy.ORCID iD: 0000-0002-5879-4371
Number of Authors: 42019 (English)In: Astrophysical Journal Letters, ISSN 2041-8205, E-ISSN 2041-8213, Vol. 875, no 2, article id L18Article in journal (Refereed) Published
Abstract [en]

Inversion codes allow the reconstruction of a model atmosphere from observations. With the inclusion of optically thick lines that form in the solar chromosphere, such modeling is computationally very expensive because a non-LTE evaluation of the radiation field is required. In this study, we combine the results provided by these traditional methods with machine and deep learning techniques to obtain similar-quality results in an easy-to-use, much faster way. We have applied these new methods to Mg II h and k lines observed by the Interface Region Imaging Spectrograph (IRIS). As a result, we are able to reconstruct the thermodynamic state (temperature, line-of-sight velocity, nonthermal velocities, electron density, etc.) in the chromosphere and upper photosphere of an area equivalent to an active region in a few CPU minutes, speeding up the process by a factor of 10(5) - 10(6). The opensource code accompanying this Letter will allow the community to use IRIS observations to open a new window to a host of solar phenomena.

Place, publisher, year, edition, pages
2019. Vol. 875, no 2, article id L18
Keywords [en]
line: profiles, methods: data analysis, Sun: chromosphere, Sun: photosphere
National Category
Astronomy, Astrophysics and Cosmology
Identifiers
URN: urn:nbn:se:su:diva-169116DOI: 10.3847/2041-8213/ab15d9ISI: 000465196000001OAI: oai:DiVA.org:su-169116DiVA, id: diva2:1320260
Available from: 2019-06-04 Created: 2019-06-04 Last updated: 2019-06-04Bibliographically approved

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Dalda, Alberto Sainzde la Cruz Rodríguez, JaimeGošić, Milan
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