Searching for New Physics with Reinforcement Learning
Finding new physics (NP) is the most important problem in particle physics today. Studying ``anomalies'', i.e., measurements of low-energy observables whose values disagree with the predictions of the Standard Model (SM), is a powerful search strategy. The SM Effective Field Theory (SMEFT) provides a general model-independent framework for parameterizing NP; it is natural to try to find the SMEFT operator(s) that can explain such anomalies. This is a challenging task because (i) the number of SMEFT operators is enormous, and (ii) at loop level there are very complicated correlations among the
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- PossiblePossibly related (embedding) · 52%Path Integral Quantum Mechanics in the Era of Machine Learning - AIP Publishing LLC →
- PossiblePossibly related (embedding) · 51%MIT researchers develop AI models that learn physics more efficently - Scientific Computing World →
- FuzzySimilar title/name (fuzzy) · 84%amitness/learning →
“Fuzzy title match (0.92): “Searching for New Physics with Reinforcement Learning” ≈ “amitness/learning””
- FuzzySimilar title/name (fuzzy) · 59%MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning →
“Fuzzy title match (0.73): “Searching for New Physics with Reinforcement Learning” ≈ “MathFoundationRL/Book-Mathematical-Foundation-of-Reinforceme””
- LinkedLinked via arxiv author · 85%Jacky Kumar →
“Searching for New Physics with Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Marianne Bouchard →
“Searching for New Physics with Reinforcement Learning”
- LinkedLinked via arxiv author · 85%David London →
“Searching for New Physics with Reinforcement Learning”