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A compression algorithm for the combination of PDF sets

Carrazza, Stefano; Latorre, José I.; Rojo, Juan; Watt, Graeme

A compression algorithm for the combination of PDF sets Thumbnail


Stefano Carrazza

José I. Latorre

Juan Rojo


The current PDF4LHC recommendation to estimate uncertainties due to parton distribution functions (PDFs) in theoretical predictions for LHC processes involves the combination of separate predictions computed using PDF sets from different groups, each of which comprises a relatively large number of either Hessian eigenvectors or Monte Carlo (MC) replicas. While many fixed-order and parton shower programs allow the evaluation of PDF uncertainties for a single PDF set at no additional CPU cost, this feature is not universal, and, moreover, the a posteriori combination of the predictions using at least three different PDF sets is still required. In this work, we present a strategy for the statistical combination of individual PDF sets, based on the MC representation of Hessian sets, followed by a compression algorithm for the reduction of the number of MC replicas. We illustrate our strategy with the combination and compression of the recent NNPDF3.0, CT14 and MMHT14 NNLO PDF sets. The resulting compressed Monte Carlo PDF sets are validated at the level of parton luminosities and LHC inclusive cross sections and differential distributions. We determine that around 100 replicas provide an adequate representation of the probability distribution for the original combined PDF set, suitable for general applications to LHC phenomenology.

Journal Article Type Article
Acceptance Date Sep 25, 2015
Online Publication Date Oct 5, 2015
Publication Date Oct 31, 2015
Deposit Date Apr 18, 2019
Publicly Available Date Apr 18, 2019
Journal European Physical Journal C: Particles and Fields
Print ISSN 1434-6044
Electronic ISSN 1434-6052
Publisher SpringerOpen
Peer Reviewed Peer Reviewed
Volume 75
Issue 10
Article Number 474
Public URL


Published Journal Article (7.4 Mb)

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Copyright Statement
© The Author(s) 2015. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Funded by SCOAP3.

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