AI Revolutionizes LHC Experiments: Uncovering New Particles in Particle Collision Data
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The Large Hadron Collider (LHC) is not just smashing particles; it’s smashing records in the quest to uncover the universe's secrets. A key goal of the LHC experiments is to search for new particles that could answer fundamental questions in physics. Traditionally, these searches focus on specific particles predicted by theory, but what about the unexpected ones?
AI to the Rescue
Given the monumental task of combing through billions of particle collisions, physicists from the ATLAS and CMS collaborations have turned to artificial intelligence (AI) for help. At the recent Rencontres de Moriond conference, the CMS team showcased how machine learning is revolutionizing the search for elusive particle "jets," which might hold clues to new physics.
Decoding Jets with AI
Jets—collimated sprays of particles from quarks and gluons—are notoriously complex to analyze. Using AI, physicists can now sift through data to identify jets from known particles and spot atypical jets that might indicate new phenomena. This method enhances the ability to detect subtle hints of new physics hidden within the data.
Broadening the Search
The researchers employ various AI-driven methods to broaden their hunt for new particles. Some approaches involve analyzing entire collision events for anomalies, while others compare real collision data to simulated scenarios that might reveal new particles. These strategies highlight the flexibility and power of AI in scientific discovery.
Future Frontiers in AI and Particle Physics
The latest results from CMS indicate that AI not only increases the sensitivity to a broader range of potential new particles but also opens up innovative ways to enhance future searches. According to physicist Oz Amram, the potential for AI in this field is just beginning to be tapped, with plans already underway to refine these algorithms for even more comprehensive searches.
Source: Phys.org
