Published 2021
| Version v1
Journal article
An open-source machine learning framework for global analyses of parton distributions
Creators
- Ball, Richard D.1
- Del Debbio, Luigi1
- Pearson, Rosalyn L.1
- Wilson, Michael1
- Carrazza, Stefano2
- Cruz-Martinez, Juan2
- Forte, Stefano2
- Stegeman, Roy2
- Schwan, Christopher2
- Giani, Tommaso3, 4
- Rojo, Juan3, 4
- Iranipour, Shayan5
- Kassabov, Zahari5
- Ubiali, Maria5
- Latorre, Jose I.6, 7, 8
- Nocera, Emanuele R.3, 1
- Voisey, Cameron9
- NNPDF Collaboration
- 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/s10052-021-09747-9Additional details
Identifiers
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