U.S. National Aeronautics and Space Administration (NASA) scientists are calibrating images of the sun with artificial intelligence to enhance data for solar research.
The Atmospheric Imagery Assembly (AIA) on NASA's Solar Dynamics Observatory captures this data, and requires regular calibration via sounding rockets to correct for periodic degradation.
The researchers are pursuing constant virtual calibration between sounding rocket flights by first training a machine learning algorithm on AIA data to identify and compare solar structures, then feeding it similar images to determine whether it identifies the correct necessary calibration.
The scientists also can employ the algorithm to compare specific structures across wavelengths and improve evaluations.
Once the program can identify a solar flare without degradation, it can then calculate how much degradation is affecting AIA's current images, and how much calibration each needs.
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA
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