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Longitudinal Density Monitor for the Large Hadron Collider

Longitudinal Density Monitor for the Large Hadron Collider
大型强子对撞机纵向密度监测仪
批准号:
2760510
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
大型强子对撞机(LHC)是世界上最大、最强大的粒子加速器,两束反向循环的质子或离子束通过27公里长的真空管重复传输。它们的运动是由超导磁铁和众多的射频加速结构引导的,以获得地球上无与伦比的粒子能量,然后在实验中发生碰撞。射频腔需要将光束结构成束,以便每个束在环周围的每一次通过都能看到加速电压。由于光束的传播速度是光速的99.9999991%,每个束之间的最小间隔时间为25 ns,因此,为了使注入和引出磁铁一次只能作用1个束,每10个可能的束中有9个是空的。在操作过程中,空的桶中可能会装满“幽灵”和“卫星”束,这可能会成为机器保护和绝对光度校准的问题。为此,开发了纵向密度监测器(LDM),它使用来自束流的同步辐射来非侵入性地、连续地监测束流。LDM的动态范围足以监测标称束团和“幽灵”束团,时间分辨率约为50ps,允许在许多转弯中建立完整的束团轮廓。该项目将使用现代机器学习和数据科学技术分析海量数据,开发一种新的精确实时光度测量工具,包括校正与不同监视器类型、束流分布和束流效应相关的偏差的技术,以及对堆积效应的影响和缓解策略的研究。
英文摘要
The Large Hadron Collider (LHC) is the world's largest and most powerful particle accelerator where two counter circulating beams of protons or ions are passed repeatedly though 27km of vacuum pipe. Their motion is guided by super conducting magnets and numerous radio frequency (RF) accelerating structures to achieve particle energies unmatched on Earth, before being brought into collision in the experiments.The RF cavities necessitate the beam to be structured into bunches so that each bunch sees an accelerating voltage on every pass around the ring . As the beams are travelling at 99.9999991% of the speed of light, each bunch would have a minimum separation in time of 25 ns between bunches and so to for the injection and extraction magnets to effect only 1 bunch at a time, 9 out of every 10 possible bunches are left empty.During operation, empty buckets can be filled with "ghost" and "satellite" bunches which can become a problem for machine protection and absolute luminosity calibration. To this end the Longitudinal Density Monitor (LDM) was developed and uses synchrotron radiation from the beam to non-invasively and continuously monitor the beam. The LDM has a dynamic range sufficient to monitor both the nominal and "ghost" bunches with a time resolution on the order of 50 ps allowing, over many turns, to build a complete bunch profile of the beams in the LHC.This project will analyse vast volumes of data using modern machine learning and data science techniques to develop a novel tool for precise real-time luminosity measurements, including techniques to correct for bias associated with different monitor types, beam distributions and beam-beam effects as well as an investigation in to the impact of pileup effects and the development of strategies for its mitigation.
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