Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning

  title={Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning},
  author={Luiz F. G. dos Santos and Souvik Bose and Valentina Salvatelli and Brad Neuberg and Mark C. M. Cheung and Miho Janvier and Meng Jin and Yarin Gal and Paul Boerner and Atilim Gunecs Baydin},
Context. Solar activity plays a quintessential role in affecting the interplanetary medium and space weather around Earth. Remote-sensing instruments on board heliophysics space missions provide a pool of information about solar activity by measuring the solar magnetic field and the emission of light from the multilayered, multithermal, and dynamic solar atmosphere. Extreme-UV (EUV) wavelength observations from space help in understanding the subtleties of the outer layers of the Sun, that is… 


Deep learning (MIT
  • ApJ,
  • 2016
The handbook of brain theory and neural networks
A circular cribbage board having a circular base plate on which a circular counter disc, bearing a circular scale having 122 divisions numbered consecutively from 0, is mounted for rotation. A
Energy localization and excess fluctuations from long-range interactions in equilibrium molecular dynamics
SciPy 1.0: fundamental algorithms for scientific computing in Python
An overview of the capabilities and development practices of SciPy 1.0 is provided and some recent technical developments are highlighted.
The Solar Orbiter mission
Solar Orbiter, the first mission of ESA's Cosmic Vision 2015-2025 programme and a mission of international collaboration between ESA and NASA, will explore the Sun and heliosphere from close up and
A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission
A curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research is presented, anticipating this curated dataset will facilitate machine learningResearch in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission.
A deep learning virtual instrument for monitoring extreme UV solar spectral irradiance
This paper shows how to learn a mapping from EUV narrowband images to spectral irradiance measurements using data from NASA’s Solar Dynamics Observatory obtained between 2010 to 2014, and describes a protocol and baselines for measuring the performance of models.
Corrected for Degradation Over Time Fontenla
  • 2019
Outgassing Environment of Spacecraft: An Overview
Solar farside magnetograms from deep learning analysis of STEREO/EUVI data
Solar magnetograms are important for studying solar activity and predicting space weather disturbances1. Farside magnetograms can be constructed from local helioseismology without any farside