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This research demonstrates a critical advancement in applying machine learning to high-energy physics by enabling real-time data filtering at CERN's Large Hadron Collider. The system successfully transfers from simulation to live operation without fine-tuning, ensuring experimental integrity while managing overwhelming data volumes.
UChicago team wins award for AI filter at CERN’s collider
UChicago-led team builds an AI data filter for CERN’s Large Hadron Collider, addressing the challenge of saving relevant collision data from the machine’s extraordinary speeds. Led by Zixin Ding, the award-winning research merges computer science and physics to help experiments make faster decisions, staying within strict limits.
Assistant student Zixin Ding led a UChicago team to a best paper award at the ICML 2026 Workshop on AI for Physics by developing an artificial intelligence filter for data collected at CERN’s Large Hadron Collider. The team, collaborating with researchers from the University of Michigan and Fermilab, tackled the challenge of preserving collision data amidst an overwhelming volume of information. David Miller of UChicago’s Department of Physics said, “Thankfully, we can…
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