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AI algorithms blur the cosmos -Se

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A simulated image matching the parameters of the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST). To simulate the image, the researchers took high-resolution, atmosphere-free images from the Hubble Space Telescope (in the COSMOS dataset), simulated atmospheric effects using a software package called galsim, plugged in LSST parameters, and then sampled at lower resolution. Credit to LSST: Emma Alexander/Northwestern University

The universe would look a lot better if Earth’s atmosphere wasn’t photobombing it all the time.

Even images obtained by Earth’s best ground-based telescopes are blurred by shifting pockets of atmospheric air. Although seemingly harmless, this blur obscures the shapes of objects in astronomical images, sometimes leading to erroneous physical measurements that are essential to understanding the nature of our universe.

Now researchers at Northwestern University and Tsinghua University in Beijing have unveiled a new technique to solve this problem. The team adapted a well-known computer-vision algorithm used to sharpen photos and applied it to astronomical images from ground-based telescopes for the first time. The researchers also trained an artificial intelligence (AI) algorithm on the data to match the Vera C. Rubin Observatory’s imaging parameters, so, when the observatory opens next year, the tool will be instantly compatible.

While astrophysicists already use technology to remove blur, adaptive AI-powered algorithms work faster and produce more realistic images than current technology. The resulting images are blur-free and true to life. They are also beautiful – although this is not the purpose of technology

“The goal of photography is often to get a beautiful, beautiful-looking image,” said Northwestern’s Emma Alexander, senior author of the study. “But astronomical images are used for science. By cleaning the images in the right way, we can get more accurate information. The algorithm removes the atmosphere computationally, which enables physicists to get better scientific measurements. At the end of the day, the images do. It’s also better to look at.”

Watch the noise disappear as the images fade from “before” to “after” processing Credit: Northwestern University

The study will be published on March 30 Monthly Bulletin of the Royal Astronomical Society.

Alexander is an assistant professor of computer science at Northwestern’s McCormick School of Engineering, where he directs the Bio-Inspired Vision Lab. He co-led the new study with Tianao Li, an undergraduate in electrical engineering at Tsinghua University and a research intern in Alexander’s lab.

Light emitted from distant stars, planets, and galaxies travels through Earth’s atmosphere before reaching our eyes. Our atmosphere not only blocks certain wavelengths of light, it also distorts the light that reaches Earth. Even a clear night sky still has moving air that affects the light passing through it. This is why they flicker and why the best ground-based telescopes are located at high altitudes where the atmosphere is thinnest.

“It’s like looking up from the bottom of a swimming pool,” Alexander said. “Water pushes light around and distorts it. The atmosphere is definitely much less dense, but it’s a similar idea.”

The researchers used AI algorithms to remove simulated atmospheric blur, revealing true-to-life images. Credit: Emma Alexander/Northwestern University

Ambiguity becomes a problem when astrophysicists analyze the images to extract cosmic information. By studying the apparent shapes of galaxies, scientists can detect the gravitational effects of large-scale cosmic structures, which bend light on its way to our planet. It can make an elliptical galaxy spherical or more elongated than it is. But atmospheric blur stains the image in a way that distorts the shape of the galaxy. Removing ambiguity enables scientists to collect data of the right size.

“Slight differences in shape can tell us about the gravity of the universe,” Alexander said. “These differences are already hard to detect. If you look at an image from a ground-based telescope, a shape can be distorted. It’s hard to know if it’s due to a gravitational effect or the atmosphere.”

To address this challenge, Alexander and Lee combined an optimization algorithm with a deep-learning network trained on astronomical images. Among the training images, the team included simulated data that matched the expected imaging parameters of the Rubin Observatory. The resulting tool produced images with 38.6% fewer errors than classic blur removal methods and 7.4% fewer errors than modern methods.

When the Rubin Observatory officially opens next year, its telescopes will begin a decade-long deep survey of a vast swath of the night sky. As researchers train the new tool on data specifically designed to simulate upcoming images of Rubin, it will be able to help analyze the survey’s highly anticipated data.

For astronomers interested in using the tool, the open source, user-friendly code and accompanying tutorials are Available online.

“Now that we’ve shut down this instrument, we’ve put it in the hands of astrophysicists,” Alexander said. “We think it can be a valuable resource for sky surveys to obtain the most realistic data possible.”

More information:
Galaxy Image Deconvolution for Weak Gravitational Lensing with Unrolled Plug-and-Play ADMM, Monthly Bulletin of the Royal Astronomical Society (2023).

Journal Information:
Monthly Bulletin of the Royal Astronomical Society



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