Editor: @matthewfeickert (all papers)
Reviewers: @jpata (all reviews), @hqucms (all reviews)
Jackson Barr (0000-0002-9752-9204), Joschka Birk (0000-0002-1931-0127), Maxence Draguet (0000-0003-1530-0519), Stefano Franchellucci (0000-0003-0695-0798), Alexander Froch (0000-0002-8259-2622), Philipp Gadow (0000-0003-4475-6734), Daniel Hay Guest (0000-0002-4305-2295), Manuel Guth (0000-0002-6647-1433), Nicole Michelle Hartman (0000-0001-9111-4916), Michael Kagan (0000-0002-3386-6869), Osama Karkout (0000-0002-4907-9499), Dmitrii Kobylianskii (0009-0002-0070-5900), Ivan Oleksiyuk (0000-0002-4784-6340), Nikita Ivvan Pond (0000-0002-5966-0332), Frederic Renner (0000-0002-9475-3075), Sebastien Rettie (0000-0002-7092-3893), Victor Hugo Ruelas Rivera (0000-0002-2116-048X), Tomke Schröer (0000-0001-7967-6385), Martino Tanasini (0000-0002-6313-4175), Samuel Van Stroud (0000-0002-7969-0301), Janik Von Ahnen (0000-0003-4032-0079)
Barr et al., (2024). Umami: A Python toolkit for jet flavour tagging. Journal of Open Source Software, 9(102), 5833, https://doi.org/10.21105/joss.05833
Dockerfile high energy physics jet physics flavour tagging machine learning
Authors of JOSS papers retain copyright.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Journal of Open Source Software is an affiliate of the Open Source Initiative.
Journal of Open Source Software is part of Open Journals, which is a NumFOCUS-sponsored project.
Table of Contents
Public user content licensed CC BY 4.0 unless otherwise specified.
ISSN 2475-9066