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

@article{Santos2021MultiChannelAF,
  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},
  journal={ArXiv},
  year={2021},
  volume={abs/2012.14023}
}
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… 

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