Science

AI helps researchers calibrate NASA’s sun image

Researchers are using artificial intelligence (AI) techniques to calibrate some of NASA’s images of the Sun, helping to improve the data scientists use for solar research. Launched in 2010, NASA’s Solar Dynamics Observatory (SDO) has been providing high-definition images of the Sun for more than a decade. The Atmospheric Imaging Assembly, or AIA, is one of two imaging instruments on SDO and is constantly staring at the Sun, taking images at 10 wavelengths of ultraviolet light every 12 seconds. This creates a wealth of information about the Sun unlike any other, but – like all instruments for observing the Sun – AIA degrades over time and the data must be frequently calibrated, NASA said in a statement. .

To overcome this challenge, the scientists decided to look at other options for calibrating the instrument, keeping an eye on constant calibration.

Machine learning, a technique used in artificial intelligence, seemed like a perfect fit.

For starters, the team would teach the algorithm what a solar flare looked like by showing it solar flares on all AIA wavelengths until it recognized solar flares in all of the different types of. light.

Once the program can recognize a solar flare without any degradation, the algorithm can then determine the extent of degradation affecting the current AIA images and the amount of calibration needed for each.

“That was the big deal. Instead of just identifying it on the same wavelength, we are identifying structures on all wavelengths, ”said Dr. Luiz Dos Santos, solar physicist at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. , and main author of the article published in the journal. Astronomy and astrophysics.

“It’s also important for deep space missions, which won’t have the ability to calibrate sounding rockets. We are tackling two problems at the same time. “

Since AIA looks at the Sun in multiple wavelengths of light, researchers can also use the algorithm to compare specific structures across wavelengths and strengthen its assessments.

As machine learning advances, its scientific applications will expand into more and more missions.

“Going forward, this may mean that deep space missions – which go to places where calibration rocket flights are not possible – can still be calibrated and continue to provide accurate data, even when they move further and further away from Earth or any star. “NASA said.

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