A new generation of simultaneous fits to LHC data using deep learning
Publication Date
2022-05Journal Title
Journal of High Energy Physics
Publisher
Springer Science and Business Media LLC
Volume
2022
Issue
5
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Iranipour, S., & Ubiali, M. (2022). A new generation of simultaneous fits to LHC data using deep learning. Journal of High Energy Physics, 2022 (5) https://doi.org/10.1007/jhep05(2022)032
Abstract
<jats:title>A<jats:sc>bstract</jats:sc>
</jats:title><jats:p>We present a new methodology that is able to yield a simultaneous determination of the Parton Distribution Functions (PDFs) of the proton alongside any set of parameters that determine the theory predictions; whether within the Standard Model (SM) or beyond it. The SIMUnet methodology is based on an extension of the NNPDF4.0 neural network architecture, which allows the addition of an extra layer to simultaneously determine PDFs alongside an arbitrary number of such parameters. We illustrate its capabilities by simultaneously fitting PDFs with a subset of Wilson coefficients within the Standard Model Effective Field Theory framework and show how the methodology extends naturally to larger subsets of Wilson coefficients and to other SM precision parameters, such as the strong coupling constant or the heavy quark masses.</jats:p>
Keywords
Regular Article - Theoretical Physics, Parton Distributions, SMEFT
Identifiers
jhep05(2022)032, 18320
External DOI: https://doi.org/10.1007/jhep05(2022)032
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336917
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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