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Image refinement and estimations of radiation formation heights with the Deep Solar ALMA Neural Network Estimator
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Image refinement and estimations of radiation formation heights with the Deep Solar ALMA Neural Network Estimator
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Image refinement and estimations of radiation formation heights with the Deep Solar ALMA Neural Network Estimator
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Journal Article
Image refinement and estimations of radiation formation heights with the Deep Solar ALMA Neural Network Estimator
2022
Overview
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.
Publisher
Cambridge University Press
