README.md

metadata

language:
  - en
license: gpl-3.0
tags:
  - physics
  - particle-physics
  - lagrangian
annotations_creators:
  - machine-generated
pretty_name: Particle Physics Lagrangian Dataset
source_datasets:
  - none
task_categories:
  - text2text-generation
configs:
  - config_name: sampled
    data_files:
      - split: train
        path: ../datasets/huggingface_dataset_sampled.csv
      - split: eval
        path: ../datasets/huggingface_dataset_sampled.csv
  - config_name: uniform
    data_files:
      - split: train
        path: ../datasets/huggingface_dataset_uniform.csv
      - split: eval
        path: ../datasets/huggingface_dataset_uniform.csv
dataset_info:
  - config_name: sampled
    features:
      - name: fields
        dtype: string
      - name: Lagrangian
        dtype: string
      - name: train/eval
        dtype: string
    splits:
      - name: train
        num_examples: 228865
      - name: eval
        num_examples: 57217
  - config_name: uniform
    features:
      - name: fields
        dtype: string
      - name: Lagrangian
        dtype: string
      - name: train/eval
        dtype: string
    splits:
      - name: train
        num_examples: 220552
      - name: eval
        num_examples: 55138
train-eval-index:
  - config: sampled
    task: text2text-generation
    task_id: seq2seq
    splits:
      train_split: train
      eval_split: eval
    col_mapping:
      fields: fields
      Lagrangian: Lagrangian
      train/eval: train/eval
  - config: uniform
    task: text2text-generation
    task_id: seq2seq
    splits:
      train_split: train
      eval_split: eval
    col_mapping:
      fields: fields
      Lagrangian: Lagrangian
      train/eval: train/eval
size_categories:
  - 100K<n<1M

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. Uniform Dataset: Provides evenly distributed Lagrangians for validation.
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.