Collaborative Research: SI2:SSE: Extending the Physics Reach of LHCb in Run 3 Using Machine Learning in the Real-Time Data Ingestion and Reduction System

在过去的200年里，物理学家发现了普通物质的基本组成，并发展了一个非常成功的理论来描述它们之间的相互作用（力）。 所有的原子和构成原子的分子都可以用这些成分来描述。 原子核通过强的核相互作用结合在一起。 它们的衰变是由强和弱的核相互作用引起的。电磁力将原子结合在一起，并将原子结合成分子。电磁力、弱核力和强核力都用量子场论来描述。 这些理论的预测可以非常非常精确，并且它们已经被同样精确的实验测量所验证。 最近，位于瑞士欧洲核子研究中心实验室的大型强子对撞机（LHC）发现了一种新的基本粒子希格斯玻色子，它是统一弱相互作用和电磁相互作用所必需的。尽管在过去的世纪里人们获得了大量关于基本粒子和自然力的知识，但许多重要的问题仍然没有得到解答。例如，宇宙中大多数物质的引力相互作用不具有普通的电磁或核相互作用。 由于它只能通过引力相互作用被观察到，因此被称为暗物质。 什么事？ 同样有趣的是，当我们所知的基本相互作用将物质和反物质描述为几乎完美的镜像时，为什么宇宙中的反物质如此之少？大型强子对撞机的建立是为了发现和研究希格斯玻色子，并寻找这些问题的答案。LHC的第一次数据采集运行（运行1，2010-2012）取得了巨大的成功，产生了1000多篇期刊文章，其中希格斯玻色子的发现尤为突出。目前的LHC运行（运行2，2015年至今）已经产生了许多世界领先的结果;然而，最有趣的问题仍然没有答案。位于CERN LHC上的LHCb实验具有回答其中一些问题的独特潜力。LHCb正在寻找在LHC的高能粒子碰撞中产生的暗物质信号，并对罕见的过程进行高精度研究，这些过程可以揭示导致我们宇宙中观察到的物质/反物质不平衡的未知力量的存在。该项目的主要目标-由计算机和信息科学与工程局高级网络基础设施办公室以及数学和物理科学局物理部和数学科学部支持-是利用机器学习（ML）开发和部署软件，使LHCb实验能够显着提高其在运行3中的发现潜力（2021-2023年）。具体来说，开发的ML将大大提高对许多拟议类型的暗物质和新力量的敏感性，使其能够更有效地识别和研究潜在的信号-使用有限的计算资源可用。 LHC实验收集的数据集是世界上最大的数据集之一。例如，LHCb实验的传感器阵列（两个PI都在其上工作）每秒产生约100 TB的数据，接近每年1 ZB的数据。即使在定制的读出电子设备进行了大幅数据减少之后，数据量仍然约为每年10艾字节，与最大规模的工业数据集相当。如此大的数据集不能被无限期地存储;因此，所有的高能物理（HEP）实验都采用由数据摄取系统（在HEP中被称为触发系统）在真实的时间中执行的数据缩减方案，以决定每个事件是被持久化以用于未来的分析还是被永久地丢弃。触发系统的设计取决于传感器的读出速率、数据摄取系统的计算能力以及数据的可用存储空间。LHCb探测器正在升级运行3（2021-2023），届时触发系统每年需要处理25艾字节。目前，触发器每年处理的10艾字节中只有0.3艾字节使用高级计算算法进行分析;其余部分在此阶段之前使用FPGA上执行的简单算法丢弃。为了处理CPU群上的所有数据，ML将用于开发和部署新的触发算法。该提案的具体目标是使用ML更全面地表征LHCb数据，并使用这些表征构建算法：替换事件模式识别中计算最昂贵的部分;提高事件分类算法的性能;并在不降低物理性能的情况下减少每个事件持久化的字节数。由于量子系统的限制，目前还无法对暗物质和我们宇宙的物质/反物质不对称性做出许多潜在的解释。由于HEP计算预算预计将大致持平，因此必须重新设计LHCb触发系统以实现其全部潜力。这种重新设计必须超越可扩展的技术升级;需要激进的新战略。

In the past 200 years, physicists have discovered the basic constituents of ordinary matter and the developed a very successful theory to describe the interactions (forces) between them. All atoms, and the molecules from which they are built, can be described in terms of these constituents. The nuclei of atoms are bound together by strong nuclear interactions. Their decays result from strong and weak nuclear interactions. Electromagnetic forces bind atoms together, and bind atoms into molecules. The electromagnetic, weak nuclear, and strong nuclear forces are described in terms of quantum field theories. The predictions of these theories can be very, very precise, and they have been validated with equally precise experimental measurements. Most recently, a new fundamental particle required to unify the weak and electromagnetic interactions, the Higgs boson, was discovered at the Large Hadron Collider (LHC), located at the CERN laboratory in Switzerland. Despite the vast amount of knowledge acquired over the past century about the fundamental particles and forces of nature, many important questions still remain unanswered. For example, most of the matter in the universe that interacts gravitationally does not have ordinary electromagnetic or nuclear interactions. As it has only been observed via its gravitation interactions, it is called dark matter. What is it? Equally interesting, why is there so little anti-matter in the universe when the fundamental interactions we know describe matter and anti-matter as almost perfect mirror images of each other? The LHC was built to discover and study the Higgs boson and to search for answers to these questions. The first data-taking run (Run 1, 2010-2012) of the LHC was a huge success, producing over 1000 journal articles, highlighted by the discovery of the Higgs boson. The current LHC run (Run 2, 2015-present) has already produced many world-leading results; however, the most interesting questions remained unanswered. The LHCb experiment, located on the LHC at CERN, has unique potential to answer some of these questions. LHCb is searching for signals of dark matter produced in high-energy particle collisions at the LHC, and performing high-precision studies of rare processes that could reveal the existence of the as-yet-unknown forces that caused the matter/anti-matter imbalance observed in our universe. The primary goal of this project - supported by the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering and the Physics Division and the Division of Mathematical Sciences in the Directorate of Mathematical and Physical Sciences - is developing and deploying software utilizing Machine Learning (ML) that will enable the LHCb experiment to significantly improve its discovery potential in Run 3 (2021-2023). Specifically, the ML developed will greatly increase the sensitivity to many proposed types of dark matter and new forces by making it possible to much more efficiently identify and study potential signals -- using the finite computing resources available. The data sets collected by the LHC experiments are some of the largest in the world. For example, the sensor arrays of the LHCb experiment, on which both PIs work, produce about 100 terabytes of data per second, close to a zettabyte of data per year. Even after drastic data-reduction performed by custom-built read-out electronics, the data volume is still about 10 exabytes per year, comparable to the largest-scale industrial data sets. Such large data sets cannot be stored indefinitely; therefore, all high energy physics (HEP) experiments employ a data-reduction scheme executed in real time by a data-ingestion system - referred to as a trigger system in HEP - to decide whether each event is to be persisted for future analysis or permanently discarded. Trigger-system designs are dictated by the rate at which the sensors can be read out, the computational power of the data-ingestion system, and the available storage space for the data. The LHCb detector is being upgraded for Run 3 (2021-2023), when the trigger system will need to process 25 exabytes per year. Currently, only 0.3 of the 10 exabytes per year processed by the trigger are analyzed using high-level computing algorithms; the rest is discarded prior to this stage using simple algorithms executed on FPGAs. To process all the data on CPU farms, ML will be used to develop and deploy new trigger algorithms. The specific objectives of this proposal are to more fully characterize LHCb data using ML and build algorithms using these characterizations: to replace the most computationally expensive parts of the event pattern recognition; to increase the performance of the event-classification algorithms; and to reduce the number of bytes persisted per event without degrading physics performance. Many potential explanations for dark matter and the matter/anti-matter asymmetry of our universe are currently inaccessible due to trigger-system limitations. As HEP computing budgets are projected to be approximately flat moving forward, the LHCb trigger system must be redesigned for the experiment to realize its full potential. This redesign must go beyond scalable technical upgrades; radical new strategies are needed.