Event Generators for High-Energy Physics Experiments

TL;DR

This paper reviews the current state and development of Monte-Carlo event generators in high-energy physics, emphasizing their role in understanding physics at high energies, reducing uncertainties, and supporting future experiments.

Contribution

It provides a comprehensive overview of physics models, algorithms, and development strategies for event generators, highlighting opportunities for improvement and standardization.

Findings

Event generators are crucial for interpreting high-energy physics data.

Unified development approaches improve model accuracy and reduce systematic uncertainties.

Open science practices enhance data accessibility and model consistency.

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.