Reconstruction of Transverse Beam Distribution using Machine Learning

使用机器学习重建横向光束分布

• 批准号: 2889916
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889916
• 关键词: Reconstruction; Transverse; Beam; Distribution; using

The beam transverse distribution in CERN's high-radiation environment is measured by imaging the light generated by the particle beam hitting a scintillating screen, using cameras produced in-house based on radiation hard tubes. Due to the cessation of radiation hard tube production worldwide, CERN is investigating the transport of the beam image to low-radiation areas using radiation tolerant optical fibers coupled to normal CMOS cameras. In this framework, pioneering work to reconstruct the beam's transverse distribution using a single large-core multimode optical fiber began in 2020. It takes advantage of advances in generative modeling using deep learning methods, such as convolutional neural networks, and attempts to apply them to beam diagnostics. In this PhD project, the student will refine simulated data sets with more realistic optical modeling of the imaging fiber, taking into account environmental factors such as temperature and vibration effects. The student will then optimize the fiber's parameters, such as diameter and numerical aperture, and perform a market survey for available radiation-resistant fibers. In a next step, the student will screen available networks for image translation, in particular the convolutional U-Net, widely used for biomedical image segmentation and the already used generative adversarial networks. The student will then develop a machine learning-based model using simulated datasets and evaluate its performance. On the basis of these results, the student will develop an experimental setup to validate the simulation results and carry out a measurement campaign at CERN's CHARM irradiation facility to verify and study the accumulated dose related degradation effect to be included in the model. The student will have access to the Cockcroft Institute's comprehensive postgraduate training in accelerator science, as well as to LIV.INNO's structured training. The student will spend years 1 and 4 in the UK, and be based at CERN during years 2 and 3.

CERN高辐射环境中的光束横向分布是通过使用基于辐射硬管内部产生的摄像机来击中闪烁屏幕的粒子梁产生的光，来测量的。由于全世界的辐射硬管生产停止，CERN使用与正常CMOS摄像机耦合的辐射耐受光纤调查了光束图像向低辐射区域的运输。在此框架中，开创性的工作是使用单个大核多模光纤从2020年开始重建梁的横向分布。它利用了使用深度学习方法（例如卷积神经网络）的生成建模的进步，并尝试将它们应用于梁诊断。在此博士学位项目中，学生将考虑到温度和振动效应等环境因素，并以更真实的光学建模来完善模拟数据集。然后，学生将优化纤维的参数，例如直径和数值孔径，并对可用的抗辐射纤维进行市场调查。下一步，学生将筛选可用的网络以进行图像翻译，特别是卷积U-NET，广泛用于生物医学图像分割和已经使用的生成性对抗网络。然后，学生将使用模拟数据集开发基于机器学习的模型并评估其性能。根据这些结果，学生将开发一个实验设置，以验证模拟结果并在CERN的魅力辐射设施进行测量运动，以验证和研究将包括在模型中的累积剂量相关降解效应。该学生将可以进入Cockcroft Institute在加速器科学方面的全面研究生培训，以及Liv.inno的结构化培训。该学生将在英国度过1年和4年，并在第2年和3年内在CERN。