Bayesian PDF reweighting meets the Hessian methods - INSPIRE

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Bayesian PDF reweighting meets the Hessian methods

Jun 2, 2016

7 pages

Part of Proceedings, 37th International Conference on High Energy Physics (ICHEP 2014) : Valencia, Spain, July 2-9, 2014, 1532-1538

Published in:

Nucl.Part.Phys.Proc. 273-275 (2016) 1532-1538

Contribution to:

- ICHEP 2014
, 1532-1538

Published: Jun 2, 2016

DOI:

10.1016/j.nuclphysbps.2015.09.248

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Abstract: (Elsevier)

New data coming from the LHC experiments have a potential to extend the current knowledge of parton distribution functions (PDFs). As a short cut to the cumbersome and time consuming task of performing a new PDF fit, re-weighting methods have been proposed. In this talk, we introduce the so-called Hessian re-weighting, valid for PDF fits that carried out a Hessian error analysis, and compare it with the better-known Bayesian methods. We determine the existence of an agreement between the two approaches, and illustrate this using the inclusive jet production at the LHC.

CERN LHC Coll
p p: scattering
jet: inclusive production
nucleon: parton: distribution function
quantum chromodynamics
statistical analysis: Bayesian
numerical calculations: Monte Carlo

References(31)

Figures(0)

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