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RESEARCH PRODUCT
Bayesian PDF reweighting meets the Hessian methods
Pia ZuritaHannu PaukkunenHannu Paukkunensubject
Hessian matrixPhysicsNuclear and High Energy PhysicsParticle physicsLarge Hadron Colliderta114parton distribution functionsJet (mathematics)010308 nuclear & particles physicsBayesian probabilityPartonJET DATAre-weighting methodsPROTON114 Physical sciences01 natural sciencesBayesian re-weightingsymbols.namesakeError analysisPARTON DISTRIBUTIONS0103 physical sciencessymbolsLHCHessian re-weighting010306 general physicsdescription
Volume: 273 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. Peer reviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-04-01 | Nuclear and Particle Physics Proceedings |