Searching for New Physics with Reinforcement Learning
Researchers apply reinforcement learning to identify SMEFT operators explaining particle physics anomalies, reproducing and improving known CDF W-mass results.
The paper introduces a reinforcement learning method to search the large Standard Model Effective Field Theory (SMEFT) operator space for explanations of measurement anomalies. It was validated on the CDF W-mass anomaly, reproducing and improving known results, then applied to a harder multi-anomaly scenario. RL efficiently navigates complex loop-level operator correlations that bias human-driven phenomenological analysis.
- RL replaces biased human intuition in searching the huge SMEFT operator space
- Reproduces and improves known results on the CDF W-mass anomaly
- Handles multiple concurrent anomalies at loop level
- Organizations
- CDF
From the original article
1 min read at arxiv.orgFinding new physics (NP) is the most important problem in particle physics today. Studying ``anomalies'', i. e.
By Jacky Kumar · arxiv.org
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