Code repository for "Versatile Energy-Based Probabilistic Models for High Energy Physics", Taoli Cheng and Aaron Courville.
As a classical generative modeling approach, energy-based models have the natural advantage of flexibility in the form of the energy function. Recently, energy-based models have achieved great success in modeling high-dimensional data in computer vision and natural language processing. In line with these advancements, we build a multi-purpose energy-based probabilistic model for High Energy Physics events at the Large Hadron Collider. This framework builds on a powerful generative model and describes higher-order inter-particle interactions. It suits different encoding architectures and builds on implicit generation. As for applicational aspects, it can serve as a powerful parameterized event generator for physics simulation, a generic anomalous signal detector free from spurious correlations, and an augmented event classifier for particle identification.
Training sets and test sets for anomaly detection can be found here and here.
Input arguments
--input_dim: input dimension of each jet, i.e., 4*number of jet constituents
--input_scaler: scale the input features or not
--steps: MCMC steps
--step_size: MCMC step size
--epsilon: noise magnitude in Langevin Dynamics
--kl: add the KL divergence between the model distribution and MCMC estimation to the training objective
--hmc: use Hamiltonian Monte Carlo or not
--n_train: number of training samples
--batch_size: batch size
--epochs: number of training epochs
--lr: learning rate
--model_name: model name
Training
./ebm_jet_attn.py --input_dim 160 --steps 24 --step_size 0.1 --n_train 300000 --batch_size 128 --epochs 50 --lr 0.0001
@inproceedings{
cheng2023versatile,
title={Versatile Energy-Based Probabilistic Models for High Energy Physics},
author={Taoli Cheng and Aaron Courville},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=j0U6XJubbP}
}