论文标题
使用QCD启发的可观察物识别B-JET
Identification of b-jets using QCD-inspired observables
论文作者
论文摘要
我们研究了仅包含底部夸克（$ b $ -jets）的HADRONIC喷射的问题，仅来自带有光part量的喷气式飞机。我们开发了一种新颖的方法，用于$ b $ tagry，以利用QCD启发的JET子结构可观察物的应用，例如一维射流角度和二维主隆德平面。我们证明，这些可观察物可以用作现代机器学习算法的输入，以有效地将$ b $ jets与轻质的算法分开。为了测试我们的标记程序，我们考虑了产生$ z $玻色子的模拟事件与喷气机的关联，并表明，使用喷气角度作为深神经网络的输入，以及使用从原发性隆德喷气平面获得的图像作为对卷积神经网络的输入的输入，可以与标记准确的准确性与常规轨迹标记的准确性相提并论。我们认为，基于赛道的标签器的互补用法以及基于QCD启发的可观察物的互补用法可以提高$ b $ to的准确性。
We study the issue of separating hadronic jets that contain bottom quarks ($b$-jets) from jets featuring light partons only. We develop a novel approach to $b$-tagging that exploits the application of QCD-inspired jet substructure observables such as one-dimensional jet angularities and the two-dimensional primary Lund plane. We demonstrate that these observables can be used as inputs to modern machine-learning algorithms to efficiently separate $b$-jets from light ones. In order to test our tagging procedure, we consider simulated events where a $Z$ boson is produced is association with jets and show that using jet angularities as an input for a deep neural network, as well as using images obtained from the primary Lund jet plane as input to a convolutional neural network, one can achieve tagging accuracy comparable with the accuracy of conventional track-based taggers. We argue that the complementary usage of the track-based taggers together with the ones based upon QCD-inspired observables could improve $b$-tagging accuracy.
