Efficient phase-space generation for hadron collider event simulation

Bothmann, Enrico; Childers, Taylor; Giele, Walter; Herren, Florian; Höche, Stefan; Isaacson, Joshua; Knobbe, Max; Wang, Rui

doi:10.21468/scipostphys.15.4.169

Cited by 6 publications

(1 citation statement)

References 57 publications

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“…The opportunity to use heterogeneous computing architectures at leadership class computing facilities has facilitated these investigations and promises to provide solutions for future computing platforms. Currently, there is no full-fledged, portable event generator, but several concepts have been presented that may lead to solutions in the near term, and various alpha release versions are currently in preparation [448][449][450][451][452][453]. The emergence of cross-platform abstraction layers is expected to simplify the construction of architecture-agnostic event generators.…”

Section: Computing Performance and Portabilitymentioning

confidence: 99%

Event generators for high-energy physics experiments

Campbell,

Diefenthaler,

Hobbs

et al. 2024

SciPost Phys.

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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.

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Section: Computing Performance and Portabilitymentioning

confidence: 99%

Event generators for high-energy physics experiments

Campbell,

Diefenthaler,

Hobbs

et al. 2024

SciPost Phys.

Self Cite

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Accelerating HEP simulations with Neural Importance Sampling

Deutschmann,

Götz

2024

J. High Energ. Phys.

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Many high-energy-physics (HEP) simulations for the LHC rely on Monte Carlo using importance sampling by means of the VEGAS algorithm. However, complex high-precision calculations have become a challenge for the standard toolbox, as this approach suffers from poor performance in complex cases. As a result, there has been keen interest in HEP for modern machine learning to power adaptive sampling. While previous studies have shown the potential of normalizing-flow-powered neural importance sampling (NIS) over VEGAS, there remains a gap in accessible tools tailored for non-experts. In response, we introduce ZüNIS, a fully automated NIS library designed to bridge this divide, while at the same time providing the infrastructure to customise the algorithm for dealing with challenging tasks. After a general introduction on NIS, we first show how to extend the original formulation of NIS to reuse samples over multiple gradient steps while guaranteeing a stable training, yielding a significant improvement for slow functions. Next, we introduce the structure of the library, which can be used by non-experts with minimal effort and is extensivly documented, which is crucial to become a mature tool for the wider HEP public. We present systematic benchmark results on both toy and physics examples, and stress the benefit of providing different survey strategies, which allows higher performance in challenging cases. We show that ZüNIS shows high performance on a range of problems with limited fine-tuning.

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Efficient precision simulation of processes with many-jet final states at the LHC

Bothmann,

Childers,

Gütschow

et al. 2024

Phys. Rev. D

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We present a scalable technique for the simulation of collider events with multijet final states, based on an improved parton-level event file format and scalable I/O. The method is implemented for both leading- and next-to-leading-order QCD calculations. We perform a comprehensive analysis of the computing performance and validate our new framework using Higgs-boson plus multijet production with up to seven jets. We make the resulting code base available for public use. Published by the American Physical Society 2024

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Efficient phase-space generation for hadron collider event simulation

Cited by 6 publications

References 57 publications

Event generators for high-energy physics experiments

Event generators for high-energy physics experiments

Accelerating HEP simulations with Neural Importance Sampling

Efficient precision simulation of processes with many-jet final states at the LHC

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