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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,两束质子或离子的反向循环束在27公里长的真空管中反复通过。它们的运动由超导磁体和众多射频(RF)加速结构引导,在实验中碰撞之前获得地球上无法比拟的粒子能量。射频腔需要将光束结构成束,这样每束在环的每一圈都能看到加速电压。由于光束以99.9999991%的光速行进,每束束之间的最小间隔时间为25纳秒,因此为了使注入和提取磁体一次只影响一束,每10个可能的束中有9个是空的。在操作过程中,空桶可能充满“幽灵”和“卫星”束,这可能成为机器保护和绝对光度校准的问题。为此,纵向密度监测器(LDM)被开发出来,并利用光束的同步辐射对光束进行非侵入性和连续监测。LDM具有足够的动态范围来监测名义束和“幽灵”束,时间分辨率约为50 ps,允许在多次转弯后,在大型强子对撞机中建立完整的束轮廓。该项目将利用现代机器学习和数据科学技术分析大量数据,开发一种用于精确实时光度测量的新型工具,包括纠正与不同监测器类型、光束分布和光束效应相关的偏差的技术,以及对堆积效应影响的调查和制定缓解策略的研究。
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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