AI Revolutionizes Solar Data Analysis: Bridging Past and Present Observations

From past to future: AI brings new light to solar observations

A deep learning framework transforms decades of solar data into a unified, high-resolution view -- adjusting instruments, overcoming limitations, and helping us better understand our star. As solar telescopes get more sophisticated, they offer increasingly detailed views of our closest star. But with each new generation of instruments, we face the growing challenge of differences in observations. Older datasets, which sometimes span decades, can't easily be compared with the most recent imagery. The ability to study long-term solar changes or rare events is limited by inconsistencies in resolution, calibration, and data quality. Scientists from the University of Graz, Austria, in collaboration with colleagues from the Skolkovo Institute of Science and Technology (Skoltech), Russia, and the High Altitude Observatory of the U.S. National Center for Atmospheric Research developed a new deep learning framework (Instrument-to-Instrument translation; ITI) that helps bridge the gap between old and new observations. The research results were published in the journal Nature Communications. "Using a type of artificial intelligence called generative adversarial networks (GANs), we've developed a method that can translate solar observations from one instrument to another -- even if those instruments never operated at the same time," says the lead author of the study, Robert Jarolim, a NASA postdoctoral fellow at the High Altitude Observatory in Colorado (U.S.). This technique enables the AI system to learn the characteristics of the most recent observing capabilities and transfer this information to legacy observations. The model works by training one neural network to simulate degraded images from high-quality ones, and a second network to reverse the synthetic degradation. Specifically, the method uses real-world solar data, capturing the complexity of the instrumental differences. The second network can then be applied to real low-quality observations to translate them to the quality and resolution of the high-quality reference data. This approach can transform noisy, low-resolution images into clearer ones, which are comparable to observations obtained from recent solar missions, while also preserving the physical features in the images. This framework was applied to a range of solar datasets: combining 24 years of space-based observations, enhancing the resolution of full-disk solar imagery, reducing atmospheric noise in ground-based solar observations, and even estimating magnetic fields on the far side of the sun using only data from extreme ultraviolet observations. "AI can't replace observations, but it can help us get the most out of the data that we've already collected," says Jarolim. "That's the real power of this approach." By improving legacy solar data with information from recent observing capabilities, the full potential of the combined datasets can be used. This creates a more consistent picture of the long-term evolution of our dynamic star. "This project demonstrates how modern computing can breathe new life into historical data," adds Skoltech Associate Professor Tatiana Podladchikova, a co-author of the paper. "Our work goes beyond enhancing old images -- it's about creating a universal language to study the sun's evolution across time. Thanks to Skoltech's high-performance computing resources, we've trained AI models that uncover hidden connections in decades' worth of solar data, revealing patterns across multiple solar cycles. "Ultimately, we're building a future where every observation, past or future, can speak the same scientific language."

How AI is helping scientists unlock some of the sun's deepest secrets

A mass of plasma at the very top of the sun rises up from the surface in November 2012, as seen by NASA's Solar Dynamics Observatory. (Image credit: NASA/SDO/GSFC) Our sun cycles through its pattern of energetic activity every 11 years, but the technology scientists use to observe is advancing at a faster pace. That's one of the take-home messages of a new study, which argues that artificial intelligence (AI) can bridge the growing gap between newer and older solar data and help scientists uncover overlooked aspects of our star's long-term evolution. New generations of solar telescopes and instruments are constantly delivering unprecedented views of the sun. These advances, which allow scientists to capture intricate details of solar flares and map the sun's magnetic fields with increasing precision, are crucial for understanding its complex processes and driving new discoveries. Yet, data collected by each new instrument, while offering superior quality, is often incompatible with data from older ones due to variations in resolution, calibration and quality, making it tricky to study how the sun evolves over decades, the new study argues. The new AI-based approach overcomes these limitations by identifying patterns and relationships within datasets from different solar instruments and data types, translating them into a common, standardized format. This provides scientists with a richer and more consistent archive of solar observations for their research, particularly for long-term analyses of historic sunspots, rare events and studies that require combining data from multiple instruments, the study authors say. Related: Earth's sun: Facts about the sun's age, size and history "AI can't replace observations, but it can help us get the most out of the data that we've already collected," Robert Jarolim, who develops advanced algorithms to process solar images at the University of Graz in Austria and led the new study, said in a statement. "That's the real power of this approach." The AI method developed by Jarolim and his team can essentially translate observations from one instrument to another, even if those instruments never operated at the same time. That makes their data-driven approach applicable to many astrophysical imaging datasets, the new study suggests. The team achieved this through a two-step process involving neural networks, a type of machine learning algorithm loosely modeled on the human brain. First, one neural network takes high-quality images from one instrument and simulates degraded images as if they were taken by a different, lower-quality instrument. This allows the AI to learn the "damage" or systematic differences introduced by instruments. A second neural network is then trained to take these artificially degraded images and "undo" the degradation, making them look like the original high-quality images again. In doing so, it learns how to correct for the differences between the two instruments, according to the new study. Once the AI has learned how to "fix" the artificially degraded images, the second neural network can be used to improve the resolution and reduce noise in real, low-quality images collected by older instruments in a way that does not distort or remove the actual physical features of the sun that were present in the original data. This AI framework allows older data to effectively benefit from the capabilities of newer instruments, enabling scientists to bring less detailed historical observations up to the quality of modern data, according to the statement. "This project demonstrates how modern computing can breathe new life into historical data," study co-author Tatiana Podladchikova, of the Skolkovo Institute of Science and Technology in Russia, said in the same statement. "Our work goes beyond enhancing old images -- it's about creating a universal language to study the sun's evolution across time." The researchers applied this technique to data collected by different space telescopes over two solar cycles, spanning a little over two decades. According to the statement, the approach improved the detail in full-disk solar images, reduced blurring and distortion in ground-based observations caused by atmospheric noise, and even estimated magnetic fields on the far side of the sun. The team also applied the method to a sunspot (NOAA 11106) that was tracked for about a week in September 2010. According to the findings, the AI produced sharper and more detailed "magnetic pictures" of the sunspot that allowed scientists to see its magnetic structure more effectively than with the original data collected by the Solar and Heliospheric Observatory, a joint effort of NASA and the European Space Agency. "Ultimately, we're building a future where every observation, past or future, can speak the same scientific language," Podladchikova said in the statement.