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Heavy-flavour and jet correlation measurements with the ALICE experiment at the LHC

Heavy-flavour and jet correlation measurements with the ALICE experiment at the LHC
在大型强子对撞机上通过 ALICE 实验进行重味和喷射相关性测量
批准号:
2751289
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2022
资助国家:
英国
项目状态:
未结题
起止时间:
2022 至 --

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中文摘要
翻译
ALICE(大型离子对撞机实验)是欧洲核子研究中心(瑞士日内瓦)大型强子对撞机的四个实验之一,主要用于测量超相对论性重离子碰撞。在过去的几年里,ALICE实验在大型强子对撞机的长期停工期间进行了广泛的升级,包括将其硅内部跟踪系统完全替换为世界上最大的全像素硅探测器,该探测器完全使用单片有源像素CMOS传感器构建。利物浦在该项目的建设、安装和调试阶段发挥了主导作用,并将在即将开始的物理开发阶段继续运作。大型强子对撞机(运行3)的数据采集将很快重启,包括计划在2022年底进行的重离子运行。再加上最近的探测器和加速器升级,这为进行新的测量来探测夸克-胶子等离子体的深层结构和约束定义热QCD物质的性质提供了前所未有的机会。在这个博士项目中,我们打算开发和执行第一次高精度测量,这些测量与超相对论能量下Pb-Pb碰撞中重味强子(带有魅力夸克或美丽夸克)和喷流的产生有关。这些碰撞在拓扑结构上极其复杂。因此,该项目还将涉及开发和使用机器学习技术,以应对这些重离子碰撞的具有挑战性的高密度环境。
英文摘要
ALICE (A Large Ion Collider Experiment) is one of the four experiments at the LHC at CERN (Geneva, Switzerland), which focuses on measurements of ultra-relativistic heavy ion collisions. The ALICE experiment has been extensively upgraded during the Long Shutdown 2 period of the LHC in the last few years, including the complete replacement of its Silicon Inner Tracking System with the largest all-pixel Silicon detector in the world built solely using monolithic active pixel CMOS sensors. Liverpool has played a leading role in this project during the construction, installation and commissioning phase and this will continue with its operation during the physics exploitation phase about to begin.Data taking at the LHC (Run 3) will indeed restart shortly, including a heavy-ion run scheduled at the end of 2022. Coupled to the recent detector and accelerator upgrades, this offers unprecedented opportunities to perform new measurements to probe the deep structure of the quark-gluon plasma and constrain the properties of deconfined hot QCD matter.With this PhD project we intend to develop and perform first high-precision measurements which correlate the production of heavy-flavour hadrons (with charm or beauty quarks) and jets in Pb-Pb collisions at ultra-relativistic energies. These collisions are topologically extremely complicated. The project will thus also involve the development and use of machine learning techniques to cope with the challenging high-density environment of these heavy-ion collisions.
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