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AnonTechie links to a story at esciencenews.com:

Fully automated "deep learning" by computers greatly improves the odds of discovering particles such as the Higgs boson, beating even veteran physicists' abilities, according to findings by UC Irvine researchers published today in the journal Nature Communications. "We are thrilled with the publication of our work," said co-author Pierre Baldi, Chancellor's Professor of computer science, "and even more so with the hope that deep learning may help solve fundamental open questions about the nature of matter, gravity and the origin of the universe."

Baldi, along with computer science Ph.D. student Peter Sadowski and associate professor of physics & astronomy Daniel Whiteson, found quicker, more efficient ways to analyze data obtained from particle accelerators/colliders to better detect rare particles. The Higgs boson first theorized in 1964 and whose existence was finally confirmed in 2012 at the massive, underground Large Hadron Collider near Geneva, Switzerland could help explain why some particles have mass, among other primary questions of physics. Finding these particles requires sorting out relevant data from huge amounts of background noise; machine learning techniques are already used in analyzing these sets of "big data."