arXiv subjects

Pere Mato

Publications and source records attributed to Pere Mato.

3 recordsLinked to original sources

arXiv2025

Julia in HEP

Julia is a mature general-purpose programming language, with a large ecosystem of libraries and more than 12000 third-party packages, which specifically targets scientific computing. 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, via UnROOT.jl, read HEP's major file formats, including TTree and RNTuple. Interfaces to some of HEP's major software packages, such as through Geant4.jl, 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.

hep-ex↗

arXiv2020

HL-LHC Computing Review: Common Tools and Community Software

Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will be critical in the future. The challenges are driven by an ambitious physics programme, notably the LHC accelerator upgrade to high-luminosity, HL-LHC, and the corresponding detector upgrades of ATLAS and CMS. In this document we address the issues for software that is used in multiple experiments (usually even more widely than ATLAS and CMS) and maintained by teams of developers who are either not linked to a particular experiment or who contribute to common software within the context of their experiment activity. We also give space to general considerations for future software and projects that tackle upcoming challenges, no matter who writes it, which is an area where community convergence on best practice is extremely useful.

physics.comp-ph↗

arXiv2019

Machine Learning in High Energy Physics Community White Paper

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

physics.comp-ph↗