Vectorised Neutrino Reconstruction by Computing Graphs. Issue 1 (1st February 2023)

• Record Type: Journal Article
• Authors: Noll, Dennis; Erdmann, Martin; Fackeldey, Peter; Fischer, Benjamin
• Is Part Of: Journal of physics. Volume 2438:Issue 1(2023)
• Publication Date: 2023-02-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2438/1/012133
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Many particle physics analyses are adopting the concept of vectorised computing, often making them increasingly performant and resource-efficient. While a variety of computing steps can be vectorised directly, some calculations are challenging to implement. One of these is the analytical neutrino reconstruction which involves fitting that naturally varies between events. We show a vectorised implementation of the analytical neutrino reconstruction using a graph computing model. It uses established deep learning software libraries and is natively portable to local and external hardware accelerators such as GPUs. Using the example of ttH events with a semi-leptonic final state, we present performance studies for our implementation.