Title: Jet characterization in Heavy Ion Collisions by QCD-Aware Graph Neural Networks Show Chinese title

Title: 通过QCD感知图神经网络在重离子碰撞中对喷注的表征

Abstract: The identification of jets and their constituents is one of the key problems and challenging task in heavy ion experiments such as experiments at RHIC and LHC. The presence of huge background of soft particles pose a curse for jet finding techniques. The inabilities or lack of efficient techniques to filter out the background lead to a fake or combinatorial jet formation which may have an errorneous interpretation. In this article, we present Graph Reduction technique (GraphRed), a novel class of physics-aware and topology-based attention graph neural network built upon jet physics in heavy ion collisions. This approach directly works with the physical observables of variable-length set of final state particles on an event-by-event basis to find most likely jet-induced particles in an event. This technique demonstrate the robustness and applicability of this method for finding jet-induced particles and show that graph architectures are more efficient than previous frameworks. This technique exhibit foremost time a classifier working on particle-level in each heavy ion event produced at the LHC. We present the applicability and integration of the model with current jet finding algorithms such as FastJet. Show Chinese abstract

Abstract: 喷流及其组成部分的识别是重离子实验中的关键问题和具有挑战性的任务，例如在RHIC和LHC进行的实验。软粒子的巨大背景对喷流寻找技术构成了一个难题。无法有效过滤背景的技术不足导致了虚假或组合喷流的形成，这可能导致错误的解释。在本文中，我们提出了图简化技术（GraphRed），这是一种基于物理知识和拓扑结构的注意力图神经网络的新类别，建立在重离子碰撞的喷流物理基础上。这种方法直接在事件基础上处理最终状态粒子的可变长度集合的物理可观测量，以找到事件中最可能的喷流诱导粒子。该技术展示了该方法在寻找喷流诱导粒子方面的鲁棒性和适用性，并表明图架构比以前的框架更高效。该技术首次在每个在LHC产生的重离子事件中工作于粒子级别的分类器。我们介绍了该模型与当前喷流寻找算法（如FastJet）的适用性和集成。