ATLAS Tracking Upgrade - High-Speed Data Acquisition

ATLAS追踪升级-高速数据采集

• 批准号: 2737003
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2737003
• 关键词: ATLAS; Tracking; Upgrade; Speed; Data

The Large Hadron Collider (LHC) at CERN will be upgraded to become the High-Luminosity LHC (HL-LHC), providing more proton-proton collisions per second than the current LHC. This will allow for greater precision in physics measurements, and further reach in searches for new physics. For this upgrade, the tracking detector which forms the innermost section of the ATLAS Detector closest to the collisions will be replaced with a new tracking detector, the ITk. The ITk will use Silicon sensors to detect and measure the paths and momenta of high-energy particles. It will comprise an inner section of Silicon pixel sensors, and an outer section of Silicon strip sensors. Custom high-speed radiation-hard electronics are being developed to read out these sensors. At the Rutherford Appleton Laboratory (RAL), 1500 pixel sensor modules, comprising 6000 front-end chips, must be pre-tested and qualified, mounted to carbon fibre support structures with high precision, wired into data readout, control, and powering circuitry, before the completed structures are tested for combined operation. A full ITk data readout chain will be used for these tests. Due to the large number of chips and modules to be tested, considerable automation will be required.As part of the ATLAS collaboration, a student will join RAL and University College London (UCL), to jointly develop ITk pixel data-readout and test infrastructure, building on the considerable ITk and data readout expertise and leadership at these institutes. The student will help to build and develop full pixel readout chains, and will identify and develop methods for quantifying and comparing pixel module performance, and with that rapid automated sensor qualification. The student will also develop and perform first operation, testing, and qualification of combined pixel structures at RAL. The student will gain and use skills in computer programming, electronics, FPGA firmware development, high-speed digital communication, and international collaboration on a large-scale scientific project.There will also be an opportunity to pursue physics analysis with the ATLAS Detector utilising the existing ATLAS tracking detector: Unconventional signatures of current great interest are long lived particles (LLPs), which decay in the detector itself. These LLPs have been predicted by a number of New Physics models, including dark matter candidates. For this task a dedicated track reconstruction algorithm was developed to find tracks starting in the tracking volume. A current analysis using this is the search for exotic decays of the Higgs boson into long-lived particles decaying into b-quarks and tau leptons. An ultimate goal will be placing more model independent limits on the cross-sections of highly displaced vertices and hence LLP production. The student will join an existing team already involved in this analysis, allowing a greater focus of person-power on this topic, increased progress, and better support.

欧洲核子研究中心（CERN）的大型强子对撞机（LHC）将升级为高光度大型强子对撞机（HL-LHC），每秒提供比现在的大型强子对撞机更多的质子-质子碰撞。这将使物理测量更加精确，并进一步探索新的物理。在这次升级中，ATLAS探测器最内部最靠近碰撞的跟踪探测器将被一个新的跟踪探测器ITk所取代。ITk将使用硅传感器来探测和测量高能粒子的路径和动量。它将包括硅像素传感器的内部部分和硅条传感器的外部部分。定制的高速抗辐射电子设备正在开发中，以读取这些传感器。在卢瑟福阿普尔顿实验室（RAL），由6000个前端芯片组成的1500个像素传感器模块必须经过预先测试和合格，并以高精度安装在碳纤维支撑结构上，连接到数据读出，控制和供电电路中，然后完成结构进行组合操作测试。完整的ITk数据读出链将用于这些测试。由于需要测试大量的芯片和模块，因此需要相当程度的自动化。作为ATLAS合作的一部分，一名学生将加入RAL和伦敦大学学院（UCL），共同开发ITk像素数据读取和测试基础设施，以这些研究所相当多的ITk和数据读取专业知识和领导能力为基础。学生将帮助建立和开发全像素读出链，并将确定和开发量化和比较像素模块性能的方法，并与快速自动化传感器鉴定。学生还将在RAL开发和执行组合像素结构的首次操作、测试和鉴定。学生将在计算机编程、电子学、FPGA固件开发、高速数字通信以及大型科学项目的国际合作方面获得和使用技能。也将有机会利用现有的ATLAS跟踪探测器与ATLAS探测器进行物理分析：目前非常感兴趣的非常规特征是长寿命粒子（llp），它们在探测器本身中衰变。这些llp已经被许多新物理模型所预测，包括暗物质候选者。针对这一任务，开发了一种专用的航迹重建算法来寻找从跟踪体中开始的航迹。目前的一项分析是寻找希格斯玻色子衰变为长寿命粒子衰变为b夸克和tau轻子的奇异衰变。最终目标将是在高度位移顶点的横截面上设置更多与模型无关的限制，从而提高LLP产量。学生将加入一个已经参与此分析的现有团队，从而使个人力量更集中于此主题，增加进展和更好的支持。