Astronomers may use deep learning to keep the sun in focus.
What’s new: Researchers at the U.S. National Aeronautics and Space Administration (NASA), Catholic University of America, University of Oslo, and elsewhere developed a model that helps recalibrate a space telescope focused on the sun.
Key insight: Although the sun is a writhing ball of fiery plasma, patterns across its surface correlate with its brightness. A neural network can learn to associate these patterns with their characteristic brightness, so its output can be used to recalibrate equipment that monitors Earth’s nearest star.
How it works: The Solar Dynamics Observatory is a satellite that watches activity in the sun’s outer layers from orbit. Over time, light and space-borne particles degrade its lenses and sensors, dimming its output. NASA typically recalibrates the equipment by comparing the observatory’s images with similar pictures captured by instruments aboard small rockets — an expensive method carried out only periodically. The new model generates a calibration curve that can be used to adjust the observatory on an ongoing basis.
- The authors artificially dimmed solar images captured at various wavelength of light.
- They trained a convolutional neural network to predict how much they had dimmed the images.
- The predicted degradation can be used to calibrate the observatory.
Results: In tests using images taken by uncalibrated equipment, the model outperformed a baseline method that didn’t involve machine learning. Defining success as a prediction within 10 percent of the actual degree of dimming, the authors obtained 77 percent mean success across all wavelengths. The baseline achieved 43 percent mean success.
Why it matters: Recalibrating the observatory based on data from the rockets results in downtime as the equipment degrades between launches. Automated recalibration could keep the equipment operating continuously. This approach could also be a boon to probes that monitor faraway bodies, which can’t rely on rocket-assisted correction.
We’re thinking: Mother always told us not to stare at the sun, but she didn’t say anything about making a neural network do it for us.