ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
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DOI：
10.1140/epjc/s10052-023-11699-1
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发表时间：
2022-11
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味道标记算法开发的ATLAS协作和用于分析其数据集= 13 TeV pp碰撞运行2的大型强子对撞机。这些新的标记算法基于递归和深度神经网络，并在模拟碰撞事件中评估了它们的性能。这些发展产生了相当大的改进，以前的喷气式飞机的味道识别策略。在77% b-喷流识别效率工作点，在模拟标准模型事件的样本中获得170（5）的光喷流（魅力喷流）排斥因子;类似地，在30% c-喷流识别效率下，获得70（9）的光喷流（b-喷流）排斥因子。
The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of= 13 TeV pp collisions from Run 2 of the Large Hadron Collider are presented. These new tagging algorithms are based on recurrent and deep neural networks, and their performance is evaluated in simulated collision events. These developments yield considerable improvements over previous jet-flavour identification strategies. At the 77% b-jet identification efficiency operating point, light-jet (charm-jet) rejection factors of 170 (5) are achieved in a sample of simulated Standard Model events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained.