| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01067 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701067 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701067
Fast Jet Finding in Julia
1 CERN, Esplanade des Particules 1, Geneva, Switzerland
2 Indian Institute of Technology, Kanpur, India
3 Indian Institute of Science Education and Research, Kolkata, India
4 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
5 Julius-Maximilians-Universität Würzburg, Würzburg, Germany
* e-mail: graeme.andrew.stewart@cern.ch
Published online: 7 October 2025
Jet reconstruction remains a critical task in the analysis of data from HEP colliders. We describe in this paper a new, highly performant, Julia package for jet reconstruction, JetReconstruction.jl, which integrates into the growing ecosystem of Julia packages for HEP. With this package users can run sequential reconstruction algorithms for jets. In particular, for LHC events, the Anti-kT, Cambridge/Aachen and Inclusive-kT algorithms can be used. For FC-Cee studies the use of alternative algorithms such as the Generalised kT for e+e− and Durham are also supported.
The performance of the core algorithms is better than Fastjet’s C++ implementation, for typical LHC and FCCee events, thanks to the Julia compiler’s exploitation of single-instruction-multiple-data (SIMD), as well as ergonomic compact data layouts.
The full reconstruction history is made available, allowing inclusive and exclusive jets to be retrieved. The package also provides the means to visualise the reconstruction. Substructure algorithms have been added that allow advanced analysis techniques to be employed. The package can read event data from EDM4hep files and reconstruct jets from these directly, opening the door to FCCee and other future collider studies in Julia.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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