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26 April 2024 |
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An Unbiased Hessian Representation for Monte Carlo PDFs | | Stefano Carrazza
; Stefano Forte
; Zahari Kassabov
; Jose Ignacio Latorre
; Juan Rojo
; | | Date: |
25 May 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 (CMC-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 CMC-H PDF set. | | Source: | arXiv, 1505.6736 | | Services: | Forum | Review | PDF | Favorites | |
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