基于QCD感知图神经网络的重离子碰撞喷注表征
Jet characterization in Heavy Ion Collisions by QCD-Aware Graph Neural Networks
摘要：
在RHIC和LHC等重离子实验中，喷流及其成分的识别是关键问题和挑战性任务之一。软粒子背景的存在给射流探测技术带来了很大的困难。过滤掉背景的有效技术的无能或缺乏导致可能具有错误解释的假的或组合的射流形成。在这篇文章中，我们提出了图形约简技术（GraphRed），一类新的物理感知和基于拓扑的注意力图神经网络建立在重离子碰撞射流物理。这种方法直接与可变长度的终态粒子集的物理观测值在事件的基础上，找到最有可能的射流诱导粒子的事件。这种技术证明了这种方法的鲁棒性和适用性，寻找射流诱导粒子，并显示图形架构比以前的框架更有效。该技术首次展示了在LHC产生的每个重离子事件中的粒子级分类器。我们提出了适用性和集成的模型与当前的射流发现算法，如FastJet。
英文摘要：
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.
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。
