JoseEliel/lagrangian_generation

Dataset Description

The Particle Physics Lagrangian Dataset was created to train a BART model for generating Lagrangians from particle fields and their symmetries. This task supports research in field theories within particle physics. Check arXiv:2501.09729 for more information about the model.

Data Generation

The dataset is generated through a pipeline utilizing AutoEFT, which helps automate the creation of effective field theories (EFTs). This tool is crucial for creating invariant terms based on specified fields and symmetries.

Dataset Sampling

Due to the vast space of possible Lagrangians, careful sampling is essential:

1. 1.Uniform Dataset: Provides evenly distributed Lagrangians for validation.
2. 2.Sampled Dataset: Focuses on extreme cases to optimize learning, based on insights from natural language processing.

Key Features

• —Field Count: Skews towards simpler Lagrangians with fewer fields.
• —Spin Types: Includes a balanced mix of scalars and fermions.
• —Gauge Groups: Uses SU(3), SU(2), and U(1) representations.
• —Trilinear Interaction Enrichment: Includes crucial interaction terms fundamental to particle physics.

Data Fields

• —fields: List of input fields identified by their quantum numbers.
• —Lagrangian: The corresponding Lagrangian for the input fields.
• —train/eval: A flag describing whether the datapoint was used for training or evaluation.

Encoding scheme

To facilitate understanding by the transformer model, the dataset undergoes a custom tokenization process that preserves the essential information of fields and Lagrangians:

• —Fields and derivatives are tokenized to encapsulate quantum numbers, spins, and gauge symmetries.
• —Key interactions are represented through positional tokens.
• —Tokenization ensures all necessary contraction and symmetry details are conveyed.

For further details on the methods and theoretical underpinnings of this work, please refer to the paper "Generating Particle Physics Lagrangians with Transformers" arXiv:xxxx.xxxxx.

Usage

Ideal for sequence-to-sequence tasks, this dataset is optimized for training transformer models to derive particle physics Lagrangians efficiently.