FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
Particle Physics Model Building with Reinforcement Learning
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abstract
In this paper, we apply reinforcement learning to particle physics model building. As an example environment, we use the space of Froggatt-Nielsen type models for quark masses. Using a basic policy-based algorithm we show that neural networks can be successfully trained to construct Froggatt-Nielsen models which are consistent with the observed quark masses and mixing. The trained policy networks lead from random to phenomenologically acceptable models for over 90% of episodes and after an average episode length of about 20 steps. We also show that the networks are capable of finding models proposed in the literature when starting at nearby configurations.
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hep-ph 3representative citing papers
With CMB+BAO mass-sum bounds only A1/A2 two-zero textures survive; viable one-zero textures (via flow matching) predict distinct Σmi, ⟨mee⟩, and δCP patterns, realizable by non-invertible selection rules.
Machine learning optimization of a generalized SU(5) parameter y finds y ≈ 0.8 produces the closest match to the original model while resolving the fermion mass discrepancy.
citing papers explorer
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Local Conformal Predictions for Calibrated Surrogates
FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
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Revisiting One-Zero and Two-Zero Neutrino Mass Textures in Light of Recent Oscillation and Cosmological Data
With CMB+BAO mass-sum bounds only A1/A2 two-zero textures survive; viable one-zero textures (via flow matching) predict distinct Σmi, ⟨mee⟩, and δCP patterns, realizable by non-invertible selection rules.
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Good flavor search in SU(5): a machine learning approach
Machine learning optimization of a generalized SU(5) parameter y finds y ≈ 0.8 produces the closest match to the original model while resolving the fermion mass discrepancy.