Vectorised Neutrino Reconstruction by Computing Graphs

Journal of Physics Conference Series, vol. 2438, pp. 012133

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.

Authors 4

1. D. Noll corresponding Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Physics Insitute III A

2. Martin Erdmann Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Physics Insitute III A

3. P. Fackeldey Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Physics Insitute III A

4. Benjamin Fischer Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Physics Insitute III A