End-to-end simulation of particle physics events with Flow Matching and generator Oversampling

Abstract

The simulation of high-energy physics collision events is a key element for data analysis at present and future particle accelerators. The comparison of simulation predictions to data allows looking for rare deviations that can be due to new phenomena not previously observed. We show that novel machine learning algorithms, specifically Normalizing Flows and Flow Matching, can be used to replicate accurate simulations from traditional approaches with several orders of magnitude of speed-up. The classical simulation chain starts from a physics process of interest, computes energy deposits of particles and electronics response, and finally employs the same reconstruction algorithms used for data. Eventually, the data are reduced to some high-level analysis format. Instead, we propose an end-to-end approach, simulating the final data format directly from physical generator inputs, skipping any intermediate steps. We use particle jets simulation as a benchmark for comparing both discrete and continuous Normalizing Flows models. The models are validated across a variety of metrics to identify the most accurate. We discuss the scaling of performance with the increase in training data, as well as the generalization power of these models on physical processes different from the training one. We investigate sampling multiple times from the same physical generator inputs, a procedure we name oversampling, and we show that it can effectively reduce the statistical uncertainties of a dataset. This class of ML algorithms is found to be capable of learning the expected detector response independently of the physical input process. Their speed and accuracy, coupled with the stability of the training procedure, make them a compelling tool for the needs of current and future experiments.

35 pages, 19 figures, submitted to Machine Learning: Science and Technology. Added references

References in corpus (8)

• Machine Learning and LHC Event Generation
• CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
• Generative Networks for Precision Enthusiasts
• Fast Point Cloud Generation with Diffusion Models in High Energy Physics
• Evaluating generative models in high energy physics
• Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows
• End-to-end simulation of particle physics events with Flow Matching and generator Oversampling
• Systematic Evaluation of Generative Machine Learning Capability to Simulate Distributions of Observables at the Large Hadron Collider

Cited by in corpus (5)

• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• End-to-end simulation of particle physics events with Flow Matching and generator Oversampling
• Advancing Set-Conditional Set Generation: Diffusion Models for Fast Simulation of Reconstructed Particles
• Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction
• Implicit Quantile Networks For Emulation in Jet Physics