arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2306.04415hep-ex
收藏

使用图神经网络在 ATLAS 探测器中进行味道标记

Flavour tagging with graph neural networks with the ATLAS detector

Arnaud Duperrin

AI总结:

本文介绍 ATLAS 基于图神经网络的最新 $b$-tagging 算法,并报告其在 13.6 TeV Run 3 质子-质子碰撞数据中的初步性能及 HL-LHC 预期表现。

摘要:

识别含有 $b$-强子的喷注(称为 $b$-tagging,即 $b$ 标记),对 CERN 大型强子对撞机(LHC)上 ATLAS 实验开展的各类物理测量和搜寻具有重要作用。本文介绍了基于图神经网络架构的最新 $b$-tagging 算法进展。展示了在质心系能量 $\sqrt s=13.6$ TeV 的 $pp$ 碰撞 Run 3 数据上的初步性能,并讨论了在高亮度 LHC(HL-LHC)上的预期性能。

英文摘要:

The identification of jets containing a $b$-hadron, referred to as $b$-tagging, plays an important role for various physics measurements and searches carried out by the ATLAS experiment at the CERN Large Hadron Collider (LHC). The most recent $b$-tagging algorithm developments based on graph neural network architectures are presented. Preliminary performance on Run 3 data in $pp$ collisions at $\sqrt s = 13.6$ TeV is shown and expected performance at the High-Luminosity LHC (HL-LHC) discussed.

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。

↑