Systematic Evaluation of Generative Machine Learning Capability to Simulate Distributions of Observables at the Large Hadron Collider

TL;DR

This paper systematically evaluates deep generative models for accurately simulating kinematic distributions of observables at the Large Hadron Collider, aiming to supplement traditional Monte Carlo simulations in physics analyses.

Contribution

It adapts and assesses various deep generative models, identifying the most effective approach for high-precision synthetic data generation in collider physics.

Findings

Generative models can produce synthetic data closely matching original samples.

The best model performs well in statistical tests and physics analysis scenarios.

Machine learning-based simulations can be integrated into physics data analysis workflows.

Abstract

Monte Carlo simulations are a crucial component when analysing the Standard Model and New physics processes at the Large Hadron Collider. This paper aims to explore the performance of generative models for complementing the statistics of classical Monte Carlo simulations in the final stage of data analysis by generating additional synthetic data that follows the same kinematic distributions for a limited set of analysis-specific observables to a high precision. Several deep generative models are adapted for this task and their performance is systematically evaluated using a well-known benchmark sample containing the Higgs boson production beyond the Standard Model and the corresponding irreducible background. The paper evaluates the autoregressive models and normalizing flows and the applicability of these models using different model configurations is investigated. The best performing model is chosen for a further evaluation using a set of statistical procedures and a simplified physics analysis. By implementing and performing a series of statistical tests and evaluations we show that a machine-learning-based generative procedure can be used to generate synthetic data that matches the original samples closely enough and that it can therefore be incorporated in the final stage of a physics analysis with some given systematic uncertainty.