2023/03/13 by Andrea Barontini, Alessandro Candido, Barontini, Andrea +7
Computer Science · Physics and Astronomy · #Advanced Data Storage Technologies #Computational Physics and Python Applications #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #Particle physics theoretical and experimental studies
paper · pdf · doi:10.48550/arxiv.2303.07119
openalex publication_date 2023/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Continuously comparing theory predictions to experimental data is a common task in analysis of particle physics such as fitting parton distribution functions (PDFs). However, typically, both the computation of scattering amplitudes and the evolution of candidate PDFs from the fitting scale to the process scale are non-trivial, computing intesive tasks. We develop a new stack of software tools that aim to facilitate the theory predictions by computing FastKernel (FK) tables that reduce the theory computation to a linear algebra operation. Specifically, I present PineAPPL, our workhorse for grid operations, EKO, a new DGLAP solver, and yadism, a new DIS library. Alongside, I review several projects that become available with the new tools.