论文标题
LHC运行2 $ pp $ collision DataSet的Atlas flavour-tagrating算法
ATLAS flavour-tagging algorithms for the LHC Run 2 $pp$ collision dataset
论文作者
论文摘要
ATLAS合作开发的命名算法，用于分析其$ \ sqrt s = 13 $ tev $ pp $ collisions的数据集，其中显示了大型强子对撞机的2次运行。这些新的标记算法基于经常性和深层神经网络，并且在模拟碰撞事件中评估它们的性能。这些事态发展比以前的喷气流动识别策略可取得很大改善。在77％$ b $ -JET识别效率工作点，在模拟标准模型$ t \ bar {t} $ events的样本中实现了170（5）的灯喷射（符号）的拒绝因子；同样，在30％的$ C $ -JET识别效率下，获得了70（9）的轻型（$ b $ -JET）拒绝因子。
The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of $\sqrt s = 13$ 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 $t\bar{t}$ events; similarly, at a $c$-jet identification efficiency of 30%, a light-jet ($b$-jet) rejection factor of 70 (9) is obtained.
