Published 2021 | Version v1
Journal article

An open-source machine learning framework for global analyses of parton distributions

  • 1. The Higgs Centre for Theoretical Physics, University of Edinburgh, JCMB, KB, Mayfield Rd, EH9 3JZ, Edinburgh (United Kingdom)
  • 2. Tif Lab, Dipartimento di Fisica, Università di Milano and INFN, Sezione di Milano, Via Celoria 16, 20133, Milan (Italy)
  • 3. Nikhef Theory Group, Science Park 105, 1098 XG, Amsterdam (Netherlands)
  • 4. Department of Physics and Astronomy, VU Amsterdam, 1081 HV, Amsterdam (Netherlands)
  • 5. DAMTP, University of Cambridge, Wilberforce Road, CB3 0WA, Cambridge (United Kingdom)
  • 6. Qilimanjaro Quantum Tech, Barcelona (Spain)
  • 7. Center for Quantum Technologies, National University of Singapore, Singapore (Singapore)
  • 8. Quantum Research Centre, Technology Innovation Institute, Abu Dhabi (United Arab Emirates)
  • 9. Cavendish Laboratory, University of Cambridge, CB3 0HE, Cambridge (United Kingdom)

Description

We present the software framework underlying the NNPDF4.0 global determination of parton distribution functions (PDFs). The code is released under an open source licence and is accompanied by extensive documentation and examples. The code base is composed by a PDF fitting package, tools to handle experimental data and to efficiently compare it to theoretical predictions, and a versatile analysis framework. In addition to ensuring the reproducibility of the NNPDF4.0 (and subsequent) determination, the public release of the NNPDF fitting framework enables a number of phenomenological applications and the production of PDF fits under user-defined data and theory assumptions.

Availability note (English)

Available from: http://dx.doi.org/10.1140/epjc/s10052-021-09747-9

Additional details

Publishing Information

Journal Title
European Physical Journal. C, Particles and Fields (Online)
Journal Volume
81
Journal Issue
10
Journal Page Range
vp.
ISSN
1434-6052
CODEN
EPCFFB

INIS

Country of Publication
Germany
Country of Input or Organization
Germany
INIS RN
53017966
Subject category
S72: PHYSICS OF ELEMENTARY PARTICLES AND FIELDS;
Descriptors DEI
COMPUTER CODES; DISTRIBUTION FUNCTIONS; DOCUMENTATION; GLOBAL ANALYSIS; GLUONS; MACHINE LEARNING; QUARKS
Descriptors DEC
ALGORITHMS; ARTIFICIAL INTELLIGENCE; BOSONS; FERMIONS; FUNCTIONS; LEARNING; MATHEMATICAL LOGIC; MATHEMATICS

Optional Information

Notes
AID: 958
Collaborations
NNPDF Collaboration