A compression algorithm for the combination of PDF sets

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 e...

Full description

Saved in:
Bibliographic Details
Published in:The European physical journal. C, Particles and fields Vol. 75; no. 10; pp. 474 - 32
Main Authors: Carrazza, Stefano, Latorre, José I., Rojo, Juan, Watt, Graeme
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2015
Springer
Springer Nature B.V
Subjects:
Algorithms
Astronomy
Astrophysics and Cosmology
Compressing
Distribution (Probability theory)
Elementary Particles
Hadrons
Heavy Ions
Measurement Science and Instrumentation
Monte Carlo methods
Nuclear Energy
Nuclear Physics
Partons
Physics
Physics and Astronomy
Probability density functions
Quantum Field Theories
Quantum Field Theory
Regular - Theoretical Physics
Regular Article - Theoretical Physics
Representations
Strategy
String Theory
Uncertainty
Inclusive Cross Section
Monte Carlo Representation
PDF4LHC Recommendation
Monte Carlo
Compression Algorithm
ISSN:1434-6044, 1434-6052
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1434-6044
1434-6052
DOI:10.1140/epjc/s10052-015-3703-3