The Large Hadron Collider is one of the most complex experiments in science. It smashes particles together at high speeds and records what happens. But interpreting this data is not simple because of a strange quantum effect called interference. This means that different possible particle events can cancel each other out making it harder to tell what really happened.
Aishik Ghosh, a graduate student working with the ATLAS collaboration at CERN, took on this challenge. He noticed that the usual methods physicists used, which tried to sort data into categories like signal or background, struggled when interference caused some signals to vanish. Instead of sorting the data first, he introduced a new machine learning method called Neural Simulation Based Inference. This method skips classification and goes straight to figuring out the likelihood of different outcomes based on simulations.
This new approach lets physicists use all the data more effectively even when quantum interference is involved. The results were impressive. It improved the precision of measurements related to the Higgs boson so much that it forced scientists to update their plans for future experiments. What was once thought to take 15 years of data collection could now be done much faster.
This work shows how new ways of thinking combined with modern technology can push the boundaries of what we know about the quantum world. It is not about selling quantum physics as magic but about understanding its strange rules better and finding smarter ways to work with them.
For more details and full credit to the original reporting, see the article by Matt von Hippel at Ars Technica dated June 23, 2025.
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