https://doi.org/10.1140/epjc/s10052-021-09747-9
Special Article - Tools for Experiment and Theory
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, Scotland, UK
2
Tif Lab, Dipartimento di Fisica, Università di Milano and INFN, Sezione di Milano, Via Celoria 16, 20133, Milan, Italy
3
DAMTP, University of Cambridge, Wilberforce Road, CB3 0WA, Cambridge, UK
4
Quantum Research Centre, Technology Innovation Institute, Abu Dhabi, United Arab Emirates
5
Center for Quantum Technologies, National University of Singapore, Singapore, Singapore
6
Qilimanjaro Quantum Tech, Barcelona, Spain
7
Department of Physics and Astronomy, VU Amsterdam, 1081 HV, Amsterdam, The Netherlands
8
Nikhef Theory Group, Science Park 105, 1098 XG, Amsterdam, The Netherlands
9
Cavendish Laboratory, University of Cambridge, CB3 0HE, Cambridge, UK
Received:
17
September
2021
Accepted:
10
October
2021
Published online:
30
October
2021
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.
© The Author(s) 2021
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Funded by SCOAP3
