Exploring the flavor structure of quarks and leptons with reinforcement learning

We propose a method to explore the flavor structure of quarks and leptons with reinforcement learning. As a concrete model, we utilize a basic value-based algorithm for models with \(U(1)\) flavor symmetry. By training neural networks on the \(U(1)\) charges of quarks and leptons, the agent finds 21...

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Veröffentlicht in:	arXiv.org 2024-01
Hauptverfasser:	Nishimura, Satsuki, Miyao, Coh, Otsuka, Hajime
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Beta decay Computer Science - Learning CP violation Flavor (particle physics) Leptons Machine learning Neural networks Physics - High Energy Physics - Phenomenology Physics - High Energy Physics - Theory Quarks
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