Vectorised Neutrino Reconstruction by Computing Graphs

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 re...

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Bibliographic Details
Published in:Journal of physics. Conference series 2023-02, Vol.2438 (1), p.12133
Main Authors: Noll, Dennis, Erdmann, Martin, Fackeldey, Peter, Fischer, Benjamin
Format: Article
Language:English
Subjects:
Computation
Neutrinos
Particle accelerators
Particle physics
Physics
Reconstruction
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Summary: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2438/1/012133