Generating particle physics Lagrangians with transformers Article Swipe

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Yong Sheng Koay , Rikard Enberg , Stefano Moretti , Eliel Camargo-Molina ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2501.09729

In physics, Lagrangians provide a systematic way to describe laws governing physical systems. In the context of particle physics, they encode the interactions and behavior of the fundamental building blocks of our universe. By treating Lagrangians as complex, rule-based constructs similar to linguistic expressions, we trained a transformer model -- proven to be effective in natural language tasks -- to predict the Lagrangian corresponding to a given list of particles. We report on the transformer's performance in constructing Lagrangians respecting the Standard Model $\mathrm{SU}(3)\times \mathrm{SU}(2)\times \mathrm{U}(1)$ gauge symmetries. The resulting model is shown to achieve high accuracies (over 90\%) with Lagrangians up to six matter fields, with the capacity to generalize beyond the training distribution, albeit within architectural constraints. We show through an analysis of input embeddings that the model has internalized concepts such as group representations and conjugation operations as it learned to generate Lagrangians. We make the model and training datasets available to the community. An interactive demonstration can be found at: \url{https://huggingface.co/spaces/JoseEliel/generate-lagrangians}.

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Transformer Physics Particle physics Statistical physics Quantum mechanics Voltage

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2501.09729
PDF: https://arxiv.org/pdf/2501.09729
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4406549437

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DOI: https://doi.org/10.48550/arxiv.2501.09729

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Title: Generating particle physics Lagrangians with transformers

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-01-16

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Authors: Yong Sheng Koay, Rikard Enberg, Stefano Moretti, Eliel Camargo-Molina

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Landing page: https://arxiv.org/abs/2501.09729

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PDF URL: https://arxiv.org/pdf/2501.09729

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Open access: Yes

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OA URL: https://arxiv.org/pdf/2501.09729

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Concepts: Transformer, Physics, Particle physics, Statistical physics, Quantum mechanics, Voltage

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Cited by: 0

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Related works (count): 10

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