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arXiv 2203.07460hep-phhep-ex
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机器学习与LHC事例生成

Machine Learning and LHC Event Generation

Anja Butter, Tilman Plehn, Steffen Schumann, Simon Badger, Sascha Caron, Kyle Cranmer, Francesco Armando Di Bello, Etienne Dreyer, Stefano Forte, Sanmay Ganguly… 展开作者

Anja Butter, Tilman Plehn, Steffen Schumann, Simon Badger, Sascha Caron, Kyle Cranmer, Francesco Armando Di Bello, Etienne Dreyer, Stefano Forte, Sanmay Ganguly, Dorival Gonçalves, Eilam Gross, Theo Heimel, Gudrun Heinrich, Lukas Heinrich, Alexander Held, Stefan Höche, Jessica N. Howard, Philip Ilten, Joshua Isaacson, Timo Janßen, Stephen Jones, Marumi Kado, Michael Kagan, Gregor Kasieczka, Felix Kling, Sabine Kraml, Claudius Krause, Frank Krauss, Kevin Kröninger, Rahool Kumar Barman, Michel Luchmann, Vitaly Magerya, Daniel Maitre, Bogdan Malaescu, Fabio Maltoni, Till Martini, Olivier Mattelaer, Benjamin Nachman, Sebastian Pitz, Juan Rojo, Matthew Schwartz, David Shih, Frank Siegert, Roy Stegeman, Bob Stienen, Jesse Thaler, Rob Verheyen, Daniel Whiteson, Ramon Winterhalder, Jure Zupan

AI总结:

本文综述机器学习在LHC事例生成与基于模拟的推断中的应用,指出二者交叉产生的新方法可提升模拟速度、精度及碰撞数据处理能力。

摘要:

第一性原理模拟处于高能物理研究计划的核心。它们将多用途探测器产生的海量数据与基础理论预测及物理解释联系起来。本综述阐述了现代机器学习在事例生成和基于模拟的推断中的广泛应用,包括由粒子物理特定需求推动的概念性发展。在粒子物理与机器学习交叉领域开发的新思想和新工具,将提升正向模拟的速度与精度,处理碰撞数据的复杂性,并把作为逆模拟问题的推断加以增强。

英文摘要:

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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