| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01266 | |
| Number of page(s) | 13 | |
| DOI | https://doi.org/10.1051/epjconf/202533701266 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701266
Julia in HEP
1 EP-SFT, CERN, Geneva, Switzerland
2 Universidad Antonio Nariño, Ibagué, Colombia
3 IRFU-CEA, Université Paris-Saclay, Gif-sur-Yvette, France
4 Center for Advanced Systems Understanding, Görlitz, Germany
5 Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
6 Erlangen Centre for Astroparticle Physics, Friedrich-Alexander-Universität, Erlangen, Germany
7 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA, USA
8 Ruhr Universität Bochum, Bochum, Germany
9 Max-Planck-Institut für Physik, Munich, Germany
10 School of Physics & Astronomy, University of Glasgow, Glasgow, United Kingdom, G12 8QQ
* e-mail: graeme.andrew.stewart@cern.ch
Published online: 7 October 2025
Julia is a mature general-purpose programming language, with a large ecosystem of libraries and more than 12000 third-party packages, specifically targeting scientific computing. Julia runs on x86, aarch64 and PowerPC architectures, and on all major GPU platforms. As a language, Julia is as dynamic, interactive, and accessible as Python with NumPy, but achieves run-time performance on par with C/C++. In this paper, we describe the state of adoption of Julia in HEP, where momentum has been gathering over a number of years. HEP-oriented Julia packages can already read HEP’s major file formats, including TTree and RNTuple. Interfaces to some of HEP’s major software packages, such as Geant4, are available too. Jet reconstruction algorithms in Julia show excellent performance. A number of full HEP analyses have been performed in Julia.
We show how, as the support for HEP has matured, developments have benefited from Julia’s core design choices, which makes reuse from and integration with other packages easy. In particular, libraries developed outside HEP for plotting, statistics, fitting, and scientific machine learning are extremely useful.
We believe that the powerful combination of flexibility and speed, the wide selection of scientific programming tools, and support for all modern programming paradigms and tools, make Julia the ideal choice for a future language in HEP.
© 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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