Machine learning with anti-hydrogen

Lead Research Organisation: Swansea University
Department Name: College of Science

Abstract

The ALPHA Antihydrogen experiment makes use of several particle detector technologies, including a Silicon Vertex Detector, Time Projection Chamber, and a barrel of scintillating bars. One of the key challenges for these detector systems is to distinguish between antihydrogen annihilations and cosmic rays, a classification problem machine learning can do excellently. Presently this task is done by the use of cuts based on two high-level variables from the detectors for online analysis, and boosted decision trees with high level variables in offline analysis. This project would take a student into the future of machine learning. High level variables are a powerful tool for discrimination, however they are slow to pre-process. The challenge of this PhD project would be to build both online and offline analyses that have different processing budgets. Initially the plan is to investigate the application of modern machine learning techniques, such as deep learning, to attempt to beat the current cutting edge decision tree analysis used by the collaboration. Subsequently the project will expand to look at replacing the high level variables with lower level variables to reduce pre-processing time. Ultimately, a small enough model that can interpret raw detector output can make a real-time online analysis, with the final goal of programming an FPGA or micro-controller to perform accurate, real-time classification of detector events. The combination of these projects would build a robust and comprehensive thesis that investigates machine learning applied to particle detectors.

It will clearly illustrate that good data preparation is the key to accurate classification models, as well as demonstrate the speed that can be achieved using simple models to handle low level data. Demonstration of a micro-controller and FPGA level classification would have a large impact for the particle detector community contributing to detector trigger systems and live diagnostics beyond the scope of the ALPHA experiment.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023992/1 01/04/2019 30/09/2027
2430895 Studentship EP/S023992/1 01/10/2020 30/09/2024 Lukas Golino