Abstract
AbstractThe 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Proceedings of the International Astronomical Union
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
