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Image refinement and estimations of radiation formation heights with the Deep Solar ALMA Neural Network Estimator

Published online by Cambridge University Press:  28 September 2023

Henrik Eklund Open the ORCID record for Henrik Eklund [Opens in a new window]
Henrik Eklund*
Affiliation:
Institute for Solar Physics, Department of Astronomy, Stockholm University AlbaNova University Centre, SE-106 91 Stockholm, Sweden
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Abstract

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The signatures of small-scale features in the solar atmosphere are severely degraded by limited angular resolution of the observations. The Deep Solar ALMA Neural Network Estimator (Deep-SANNE) is trained towards synthetic observables from 3D magnetohydrodynamic simulations to recognize the small-scale dynamic features in data at limited observational resolution, and provide maps of correction factors across the field of view. The correction factors can be used to acquire deconvolved refined images with significantly improved brightness temperature contrasts, where the strength of brightening events are reproduced to an accuracy of 94.0% instead of the 43.7% at observational resolution. Deep-SANNE can also provide masks of the most probable locations with large accuracies, and estimations on the radiation formation heights in connection to the small-scale features. The Deep-SANNE refined images and estimations of radiation formation heights allow for larger accuracy and meaningful analysis of solar ALMA data.

Keywords

techniques: high angular resolutiontechniques: image processingtechniques: interferometricinstrumentation: high angular resolutioninstrumentation: interferometersSun: atmospheric motionsSun: radio radiationSun: chromosphereshock wavesstellar dynamics
Type
Contributed Paper
Information
Proceedings of the International Astronomical Union , Volume 18 , Symposium S372: The Era of Multi-Messenger Solar Physics , August 2022 , pp. 150 - 155
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Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of International Astronomical Union

References

Carlsson, M., Hansteen, V. H., Gudiksen, B. V., Leenaarts, J., & De Pontieu, B. 2016, A&A, 585, A4 Google Scholar
de la Cruz Rodríguez, J., Szydlarski, M., & Wedemeyer, S. 2021, ART: Advanced (and fast!) Radiative Transfer code for Solar Physics (https://github.com/SolarAlma/ART).Google Scholar
Eklund, H. 2022, A&A, Deep Solar ALMA Neural Network Estimator for image refinement and estimations of small scale dynamics, in review.10.1051/0004-6361/202244484CrossRefGoogle Scholar
Eklund, H., Szydlarski, M., & Wedemeyer, S. 2022, A&AGoogle Scholar
Eklund, H., Wedemeyer, S., Snow, B., et al. 2021a, Philosophical Transactions of the Royal Society of London Series A, 379, 20200185 Google Scholar
Eklund, H., Wedemeyer, S., Szydlarski, M., & Jafarzadeh, S. 2021b, A&A, 656, A68 Google Scholar
Eklund, H., Wedemeyer, S., Szydlarski, M., Jafarzadeh, S., & Guevara Gómez, J. C. 2020, A&A, 644, A152 Google Scholar
Gudiksen, B. V., Carlsson, M., Hansteen, V. H., et al. 2011, A&A, 531, A154 Google Scholar
Hochreiter, S. & Schmidhuber, J. 1997, Neural computation; MIT Press, 9, 1735 CrossRefGoogle Scholar
Privon, G., Nagai, H., Rebolledo, D., & Díaz Trigo, M. 2022, ALMA Cycle 9 Proposer’s Guide, ALMA Doc. 9.2 v1.4, Vol. 9.2Google Scholar
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. 1986, nature, 323, 533 CrossRefGoogle Scholar
Shi, X., Chen, Z., Wang, H., et al. 2015, arXiv e-prints, arXiv:1506.04214Google Scholar