We develop a methodology for the construction of a Hessian representation of Monte Carlo sets of parton distributions, based on the use of a subset of the Monte Carlo PDF replicas as an unbiased linear basis, and of a genetic algorithm for the determination of the optimal basis. We validate the methodology by first showing that it faithfully reproduces a native Monte Carlo PDF set (NNPDF3.0), and then, that if applied to Hessian PDF set (MMHT14) which was transformed into a Monte Carlo set, it gives back the starting PDFs with minimal information loss. We then show that, when applied to a large Monte Carlo PDF set obtained as combination of several underlying sets, the methodology leads to a Hessian representation in terms of a rather smaller set of parameters (MC-H PDFs), thereby providing an alternative implementation of the recently suggested Meta-PDF idea and a Hessian version of the recently suggested PDF compression algorithm (CMC-PDFs). The mc2hessian conversion code is made publicly available together with (through LHAPDF6) a Hessian representations of the NNPDF3.0 set, and the MC-H PDF set.

An unbiased Hessian representation for Monte Carlo PDFs / S. Carrazza, S. Forte, Z. Kassabov Zaharieva, J.I. Latorre, J. Rojo. - In: THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS. - ISSN 1434-6044. - 75:8(2015), pp. 369.1-369.20. [10.1140/epjc/s10052-015-3590-7]

An unbiased Hessian representation for Monte Carlo PDFs

S. Carrazza
;
S. Forte
Secondo
;
Z. Kassabov Zaharieva;
2015

Abstract

We develop a methodology for the construction of a Hessian representation of Monte Carlo sets of parton distributions, based on the use of a subset of the Monte Carlo PDF replicas as an unbiased linear basis, and of a genetic algorithm for the determination of the optimal basis. We validate the methodology by first showing that it faithfully reproduces a native Monte Carlo PDF set (NNPDF3.0), and then, that if applied to Hessian PDF set (MMHT14) which was transformed into a Monte Carlo set, it gives back the starting PDFs with minimal information loss. We then show that, when applied to a large Monte Carlo PDF set obtained as combination of several underlying sets, the methodology leads to a Hessian representation in terms of a rather smaller set of parameters (MC-H PDFs), thereby providing an alternative implementation of the recently suggested Meta-PDF idea and a Hessian version of the recently suggested PDF compression algorithm (CMC-PDFs). The mc2hessian conversion code is made publicly available together with (through LHAPDF6) a Hessian representations of the NNPDF3.0 set, and the MC-H PDF set.
physics and astronomy (miscellaneous); engineering (miscellaneous)
Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici
Settore FIS/04 - Fisica Nucleare e Subnucleare
2015
Article (author)
File in questo prodotto:
File Dimensione Formato  
art_10.1140_epjc_s10052-015-3590-7.pdf

accesso aperto

Tipologia: Publisher's version/PDF
Dimensione 2.62 MB
Formato Adobe PDF
2.62 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/344057
Citazioni
  • ???jsp.display-item.citation.pmc??? 9
  • Scopus 93
  • ???jsp.display-item.citation.isi??? 82
social impact