AI helps NASA look at the Sun with new eyes

The top row of images shows the degradation of AIA’s channel over the years since SDO’s launch. The bottom row of images is corrected for this degradation using a machine learning algorithm. Credit: Luiz Dos Santos/NASA GSFC.

It’s not easy being a telescope — just look at Hubble’s recent woes (and Hubble is hardly an exception). But being a solar telescope, constantly being exposed to intense light and particle bombardment, is especially rough.

Solar telescopes have to be constantly recalibrated and checked, not to ensure that damage isn’t happening — because damage is always happening. Instead, they have to be recalibrated to understand just how the instrument is changing under the effect of the Sun.

But recalibrating a telescope like NASA’s Solar Dynamics Observatory, which is in Earth orbit, isn’t easy. Its Atmospheric Imagery Assembly, or AIA, created a trove of solar images enabling us to understand our star better than ever before. In order to recalibrate AIA, researchers have to use sounding rockets: smaller rockets that carry a few instruments and only fly for about 15 minutes or so into space.

The reason why the rockets are needed is that the wavelengths that AIA is analyzing can’t be observed from Earth. They’re filtered by the atmosphere. So you need the sounding rockets carrying a small telescope to look at the same wavelengths and map out how AIA’s lenses are changing.

The Sun seen by AIA in 304 Angstrom light in 2021 before degradation correction (left) and with corrections from a sounding rocket calibration (right). Credits: NASA GSFC

Obviously, the rocket procedure isn’t ideal. It costs a bit, and rockets can’t always be launched. So a group of NASA researchers looked for a more elegant solution.

“The current best calibration techniques rely on flights of sounding rockets to maintain absolute calibration. These flights are infrequent, complex, and limited to a single vantage point, however,” the new study reads. But that’s only part of the challenge.

“It’s also important for deep space missions, which won’t have the option of sounding rocket calibration,” said Dr. Luiz Dos Santos, a solar physicist  at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, and lead author on the paper. “We’re tackling two problems at once.” 

First, they set out to train a machine-learning algorithm to recognize solar structures and compare them with existing AIA data — they used images from the sounding rockets for that. The idea was that, by looking at enough images of a solar flare, the algorithm could identify a solar flare regardless of AIA lens degradation; and then, it could also figure out how much calibration was needed.

After enough examples, they gave the algorithm images to see if it would correctly identify just how much calibration was needed. The approach worked on multiple wavelengths.

“This was the big thing,” Dos Santos said. “Instead of just identifying it on the same wavelength, we’re identifying structures across the wavelengths.” 

This image shows seven of the ultraviolet wavelengths observed by the Atmospheric Imaging Assembly on board NASA’s Solar Dynamics Observatory. The top row is observations taken from May 2010 and the bottom row shows observations from 2019, without any corrections, showing how the instrument degraded over time.
Credits: Luiz Dos Santos/NASA GSFC.

When they compared the virtual calibration (algorithm calibration predictions) with the data from the sounding rockets, the results were very similar, indicating that the algorithm had done a good job at estimating what type of calibration was needed.

The approach can also be used for more space missions, even for deep space missions where calibration methods with rockets won’t be possible.

The study was published in the journal Astronomy and Astrophysics.

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