机器学习在分析与单顶夸克相关的希格斯玻色子产生中的应用

IF 0.4 Q4 PHYSICS, PARTICLES & FIELDS

Physics of Particles and Nuclei Letters Pub Date : 2023-10-06 DOI:10.1134/S1547477123050229

A. R. Didenko

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引用次数: 0

摘要

这项工作描述了应用神经网络对希格斯玻色子进行分类的结果，该分类与ATLAS实验中LHC产生的单个顶夸克信号（pp\tH\）和主要背景过程（pp\tt，ttH，ttW，ttZ\）有关。与ttH不同，tH通道对tH耦合的符号敏感。此外，对希格斯顶部耦合的精确测量对超越标准模型物理学是敏感的[1，2]。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Application of Machine Learning for the Analysis of Higgs Boson Production in Association with Single Top-Quark

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Application of Machine Learning for the Analysis of Higgs Boson Production in Association with Single Top-Quark

This work describes results of application of a neural network for the classification of the Higgs boson in association with a single top quark signal production \(pp \to tH\) and the main background processes \(pp \to tt,ttH,ttW,ttZ\) production at the LHC in the ATLAS experiment. The tH channel is sensitive to the sign of the tH-coupling unlike the ttH. Also, an accurate measurement of the Higgs-top coupling is sensitive to the Beyond the Standard Model physics [1, 2].

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来源期刊

Physics of Particles and Nuclei Letters PHYSICS, PARTICLES & FIELDS-

CiteScore

0.80

自引率

20.00%

发文量

108

期刊介绍： The journal Physics of Particles and Nuclei Letters, brief name Particles and Nuclei Letters, publishes the articles with results of the original theoretical, experimental, scientific-technical, methodological and applied research. Subject matter of articles covers: theoretical physics, elementary particle physics, relativistic nuclear physics, nuclear physics and related problems in other branches of physics, neutron physics, condensed matter physics, physics and engineering at low temperatures, physics and engineering of accelerators, physical experimental instruments and methods, physical computation experiments, applied research in these branches of physics and radiology, ecology and nuclear medicine.