[Paper Review] Event Generators for High-Energy Physics Experiments

J. M. Campbell, M. Diefenthaler+210 more|arXiv (Cornell University)|Jan 1, 2022

Advanced Data Storage Technologies1,243 references55 citations

TL;DR

This paper provides a comprehensive overview of Monte Carlo event generators for high-energy physics, emphasizing their role in simulating particle collisions across experiments. It outlines current developments, shared physics models, and the need for coordinated tuning and data preservation to reduce systematic uncertainties and enhance future experimental accuracy.

ABSTRACT

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments.

Motivation & Objective

• To assess the current state of Monte Carlo event generators used in high-energy physics experiments.
• To identify common physics models and algorithms that span multiple experiments and detector systems.
• To highlight opportunities for improving event generator development through coordinated tuning and open science practices.
• To advocate for the preservation of experimental data and analysis tools to ensure consistency and reproducibility across future studies.
• To reduce systematic uncertainties in physics predictions by enhancing model fidelity and data-driven tuning

Proposed method

• The paper synthesizes input from a broad community of theorists and experimentalists across major high-energy physics collaborations.
• It evaluates event generators based on their implementation of quantum field theory calculations, parton showering, and hadronization models.
• The authors analyze the role of tuning parameters in matching simulations to experimental data, emphasizing the need for systematic and transparent procedures.
• The study promotes the use of shared software frameworks and standardized interfaces to improve interoperability and reproducibility.
• It advocates for the long-term preservation of both experimental data and the analysis workflows used to tune event generators.
• The paper emphasizes the integration of next-to-leading-order (NLO) and fixed-order QCD calculations with parton shower resummation techniques

Experimental results

Research questions

• RQ1 How can event generators be improved to better reflect the physics of high-energy collisions across diverse experimental setups?
• RQ2 What common models and algorithms are used across multiple experiments, and how can they be unified to reduce redundancy and improve consistency?
• RQ3 What are the key challenges in tuning event generators to experimental data while preserving underlying theoretical principles?
• RQ4 How can open science practices ensure the long-term accessibility and reproducibility of event generator tuning and analysis workflows?
• RQ5 What role do systematic uncertainties in event generators play in limiting the precision of experimental measurements?

Key findings

• Event generators are essential tools that bridge theoretical predictions and experimental data in high-energy physics.
• Common models such as parton showers, matrix element matching, and hadronization schemes are widely used across experiments and form a shared foundation for simulation.
• Systematic uncertainties in event generators are a major source of experimental error and can be reduced through coordinated tuning and improved model fidelity.
• The preservation of both experimental data and the analysis procedures used to tune event generators is critical for long-term consistency and reproducibility.
• A cohesive, community-driven approach to event generator development enables more accurate physics interpretation and reduces reliance on ad hoc tuning.
• The integration of fixed-order QCD calculations with parton shower resummation is now standard practice, significantly improving simulation accuracy