Source-linked AI summary
Searching for New Physics with Reinforcement Learning
Jacky Kumar, Marianne Bouchard, David London
TL;DR
Identifying SMEFT operators that explain anomalies is difficult because operators are numerous, correlated, and constrained by multiple observables. The paper applies reinforcement learning to search the full operator space and finds viable solutions where random search finds none.
Problem
Finding SMEFT operator combinations that explain anomalies is difficult because many operators are correlated through matching and constrained by other observables.
Method
The paper uses reinforcement learning to autonomously search the full space of 912 SMEFT operators using experimental data, theoretical predictions, and a reward controlled by Δχ2.
Results
For the full search space, reinforcement learning found 560 operator sets with Δχ2 ≥120, whereas random search found none; its best solution used 10 operators with Δχ2 = 156.4.
Takeaways & Limitations
The results demonstrate that machine learning can help tackle the search for physics beyond the Standard Model.
Takeaways & Limitations
The evaluation includes a case with 10 fictitious measurements assigned values deviating from Standard Model predictions by 3–5σ.
Abstract
from arXiv · showhide
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 operators. Analyses by humans typically rely on phenomenological intuition to decide which operators are relevant. This is often biased and does not explore the complete SMEFT operator space. Interestingly, reinforcement learning (RL) techniques excel at tasks that require decision making to achieve their goals. In this paper, we introduce an RL method that can be used to find the SMEFT operators that explain any anomalies. We test it on the CDF $W$-mass anomaly, and show that it reproduces (and improves upon) known results. We then consider a far more complicated situation with multiple anomalies and show that, even here, this method is able to find the SMEFT operators that explain the data. Our RL method can therefore be used to efficiently search for NP at the level of SMEFT.