Robotics & Machine Learning Daily News2024,Issue(Jun.5) :79-79.

New Findings from University of Hamburg in the Area of Machine Learning Publishe d (Refining fast simulation using machine learning)

汉堡大学在机器学习领域的新发现发表D(使用机器学习精炼快速模拟)

Robotics & Machine Learning Daily News2024,Issue(Jun.5) :79-79.

New Findings from University of Hamburg in the Area of Machine Learning Publishe d (Refining fast simulation using machine learning)

汉堡大学在机器学习领域的新发现发表D(使用机器学习精炼快速模拟)

扫码查看

摘要

由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于人工智能的新报告。根据NewsRx记者对汉布尔G大学的新闻报道,研究表明:"在CMS实验中,随着第二阶段预期的高亮度和探测器粒度,对快速蒙特卡洛应用程序(FastSim)的依赖性越来越大。"新闻编辑们从汉堡大学的研究中引用了一句话:“FastSim链比基于Geant4探测器模拟和全重建的应用程序快大约10倍。然而,这一优势是以一些最终分析观察到的准确度下降为代价的。在本文中,本文提出了一种基于机器学习的方法来精化这些可观测值。我们使用一个复杂的多个损失函数组合训练的回归神经网络来对FastSim链产生的样本进行随机校正。结果表明,FastSim链与FullSim输出的一致性得到了显著提高,输出可观测值与外部参数之间的相关性得到了改善。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting from the University of Hambur g by NewsRx journalists, research stated, “At the CMS experiment, a growing reli ance on the fast Monte Carlo application (FastSim) will accompany the high lumin osity and detector granularity expected in Phase 2.” The news editors obtained a quote from the research from University of Hamburg: “The FastSim chain is roughly 10 times faster than the application based on the Geant4 detector simulation and full reconstruction referred to as FullSim. Howev er, this advantage comes at the price of decreased accuracy in some of the final analysis observables. In this contribution, a machine learning-based technique to refine those observables is presented. We employ a regression neural network trained with a sophisticated combination of multiple loss functions to provide p ost-hoc corrections to samples produced by the FastSim chain. The results show c onsiderably improved agreement with the FullSim output and an improvement in cor relations among output observables and external parameters.”

Key words

University of Hamburg/Cyborgs/Emerging Technologies/Machine Learning

引用本文复制引用

出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
段落导航相关论文