Graph-based deep learning for representing events at the Large Hadron Collider

找到合适的特定领域的数据表示是解决科学问题的关键。当代机器学习已经开发出了一些架构，这些架构在某些领域被证明是成功的，它们提出了自适应隐藏变量，可以捕获大型数据集中的相关性。欧洲核子研究中心的大型强子对撞机（LHC）是非常大的数据集的来源，这些数据集提供了学习新的基础物理的可能性，需要开发新的模式识别任务。从已知物理到大型强子对撞机探测器观测的映射构成了寻求新颖性的背景。这种映射需要将物理约束转化为机器学习框架，以校准数据流并提出优化目标，从而使现代深度学习方法能够提供潜在的解决方案。在这个项目中探索的有前途的方法之一是使用图卷积网络来嵌入作为点云处理的检测到的粒子簇射的事件。这将提供对撞机事件的表示，可用于处理任务，如从背景中检测新物理的适当信号。该项目将由来自南安普顿大学和卢瑟福阿普尔顿实验室的粒子物理学家共同监督，他们与LHC有联系。粒子物理学的适当培训将作为项目开始时通过NExT博士学校和RAL研究生讲座提供的学生的一部分。

Finding appropriate domain specific representations of data is key to solving problems in science. Contemporary machine learning has developed architectures that have proved to be successful in some domains in proposing adaptive hidden variables that capture correlations in large datasets. The Large Hadron Collider (LHC) in CERN is a source of very large datasets that offer the possibility to learn new fundamental physics for which novel pattern recognition tasks need to be developed. The mapping from the known physics to the observations in the detectors of the LHC constitutes the background against which novelties are sought. This mapping requires the necessary translation of physical constraints into machine learning frameworks in order to calibrate data streams and propose optimisation objectives in order to allow modern deep learning methods to offer potential solutions. One of the promising approaches to be explored in this project is the use of graph convolution networks to embed events of detected particle showers treated as point clouds. This will provide the representations of collider events that can be used for processing tasks such as detecting appropriate signals of new physics from background. This project will be jointly supervised with particle physicists from the University of Southampton and Rutherford Appleton Laboratory who have existing links with LHC. Appropriate training in particle physics will be provided as part of the studentship at the start of the project via the NExT PhD School and the RAL Graduate Lectures.