Markov Chain Monte Carlo techniques applied to Parton Distribution Functions determination: proof of concept
| Autor: | Yémalin Gabin Gbedo, M. Mangin-Brinet |
|---|---|
| Přispěvatelé: | Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Laboratoire de Physique Subatomique et de Cosmologie ( LPSC ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut polytechnique de Grenoble - Grenoble Institute of Technology ( Grenoble INP ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) |
| Rok vydání: | 2017 |
| Předmět: |
Computer science
Monte Carlo method Bayesian probability FOS: Physical sciences parton: distribution function Inference Parton Bayesian inference Bayesian 01 natural sciences Monte Carlo: Markov chain Hybrid Monte Carlo symbols.namesake High Energy Physics - Phenomenology (hep-ph) statistical analysis 0103 physical sciences Applied mathematics Statistical physics 010306 general physics Monte Carlo algorithm Physics Markov chain hybrid 010308 nuclear & particles physics lattice field theory Markov chain Monte Carlo Hamiltonian High Energy Physics - Phenomenology Automatic Keywords Distribution function Quantum electrodynamics [PHYS.HPHE]Physics [physics]/High Energy Physics - Phenomenology [hep-ph] symbols Probability distribution Monte Carlo integration Monte Carlo method in statistical physics [ PHYS.HPHE ] Physics [physics]/High Energy Physics - Phenomenology [hep-ph] 010307 mathematical physics Particle filter statistical Monte Carlo molecular modeling acceptance |
| Zdroj: | Phys.Rev.D Phys.Rev.D, 2017, 96 (1), pp.014015. ⟨10.1103/PhysRevD.96.014015⟩ Physical Review D Physical Review D, American Physical Society, 2017, 96 (1), pp.014015. ⟨10.1103/PhysRevD.96.014015⟩ Phys.Rev.D, 2017, 96 (1), pp.014015. 〈10.1103/PhysRevD.96.014015〉 |
| ISSN: | 1550-7998 1550-2368 |
| Popis: | We present a new procedure to determine Parton Distribution Functions (PDFs), based on Markov Chain Monte Carlo (MCMC) methods. The aim of this paper is to show that we can replace the standard $\chi^2$ minimization by procedures grounded on Statistical Methods, and on Bayesian inference in particular, thus offering additional insight into the rich field of PDFs determination. After a basic introduction to these technics, we introduce the algorithm we have chosen to implement -- namely Hybrid (or Hamiltonian) Monte Carlo. This algorithm, initially developed for Lattice QCD, turns out to be very interesting when applied to PDFs determination by global analyses; we show that it allows to circumvent the difficulties due to the high dimensionality of the problem, in particular concerning the acceptance. A first feasibility study is performed and presented, which indicates that Markov Chain Monte Carlo can successfully be applied to the extraction of PDFs and of their uncertainties. Comment: 17 pages, 9 figures. Published in Phys. Rev. D |
| Databáze: | OpenAIRE |
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