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2004
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J. Pumplin, Data set diagonalization in a global fit
Original
2009
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S. Forte, Parton distributions at the dawn of the LHC
Original
2010
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H.-L. Lai et al., Uncertainty induced by QCD coupling in the CTEQ global analysis of parton distributions
Original
2010
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The NNPDF
Original
2010
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F. Demartin, S. Forte, E. Mariani, J. Rojo, and A. Vicini, The impact of PDF and α s \alpha_{s} uncertainties on Higgs Production in gluon fusion at hadron colliders
Original
2010
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T. Carli, D. Clements, A. Cooper-Sarkar, C. Gwenlan, G. P. Salam, et al., A posteriori inclusion of parton density functions in NLO QCD final-state calculations at hadron colliders: The APPLGRID Project
Original
2010
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A. De Roeck and R. Thorne, Structure Functions
Original
2011
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2015

An Unbiased Hessian Representation for Monte Carlo PDFs

Carrazza, Stefano, Forte, Stefano, Kassabov, Zahari et al.

Understand

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.