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A compression algorithm for the combination of PDF sets.

Carrazza S, Latorre JI, Rojo J, Watt G - Eur Phys J C Part Fields (2015)

Bottom Line: We illustrate our strategy with the combination and compression of the recent NNPDF3.0, CT14 and MMHT14 NNLO PDF sets.The resulting compressed Monte Carlo PDF sets are validated at the level of parton luminosities and LHC inclusive cross sections and differential distributions.We determine that around 100 replicas provide an adequate representation of the probability distribution for the original combined PDF set, suitable for general applications to LHC phenomenology.

View Article: PubMed Central - PubMed

Affiliation: Dipartimento di Fisica, Università di Milano and INFN, Sezione di Milano, Via Celoria 16, 20133 Milan, Italy.

ABSTRACT

The current PDF4LHC recommendation to estimate uncertainties due to parton distribution functions (PDFs) in theoretical predictions for LHC processes involves the combination of separate predictions computed using PDF sets from different groups, each of which comprises a relatively large number of either Hessian eigenvectors or Monte Carlo (MC) replicas. While many fixed-order and parton shower programs allow the evaluation of PDF uncertainties for a single PDF set at no additional CPU cost, this feature is not universal, and, moreover, the a posteriori combination of the predictions using at least three different PDF sets is still required. In this work, we present a strategy for the statistical combination of individual PDF sets, based on the MC representation of Hessian sets, followed by a compression algorithm for the reduction of the number of MC replicas. We illustrate our strategy with the combination and compression of the recent NNPDF3.0, CT14 and MMHT14 NNLO PDF sets. The resulting compressed Monte Carlo PDF sets are validated at the level of parton luminosities and LHC inclusive cross sections and differential distributions. We determine that around 100 replicas provide an adequate representation of the probability distribution for the original combined PDF set, suitable for general applications to LHC phenomenology.

No MeSH data available.


Comparison of the individual NNPDF3.0, CT14 and MMHT14 NNLO sets with the corresponding Monte Carlo combination MC900. The comparison is performed at a typical LHC scale of  GeV, and the PDFs are normalized to the central value of the combined set MC900
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Fig1: Comparison of the individual NNPDF3.0, CT14 and MMHT14 NNLO sets with the corresponding Monte Carlo combination MC900. The comparison is performed at a typical LHC scale of  GeV, and the PDFs are normalized to the central value of the combined set MC900

Mentions: In Fig. 1 we show the comparison of the individual PDF sets, NNPDF3.0, CT14, and MMHT14, with their Monte Carlo combination with . In the following, we will denote by MC900 this prior combination. The comparison is performed at a typical LHC scale of  GeV, and the PDFs are normalized to the central value of the combined set. As can be seen there is reasonable agreement between the three individual sets, and the resulting combined set is a good measure of their common overlap. Note that at large-x differences between the three sets are rather marked, and we expect the resulting combined probability distribution to be rather non-Gaussian.


A compression algorithm for the combination of PDF sets.

Carrazza S, Latorre JI, Rojo J, Watt G - Eur Phys J C Part Fields (2015)

Comparison of the individual NNPDF3.0, CT14 and MMHT14 NNLO sets with the corresponding Monte Carlo combination MC900. The comparison is performed at a typical LHC scale of  GeV, and the PDFs are normalized to the central value of the combined set MC900
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4594162&req=5

Fig1: Comparison of the individual NNPDF3.0, CT14 and MMHT14 NNLO sets with the corresponding Monte Carlo combination MC900. The comparison is performed at a typical LHC scale of  GeV, and the PDFs are normalized to the central value of the combined set MC900
Mentions: In Fig. 1 we show the comparison of the individual PDF sets, NNPDF3.0, CT14, and MMHT14, with their Monte Carlo combination with . In the following, we will denote by MC900 this prior combination. The comparison is performed at a typical LHC scale of  GeV, and the PDFs are normalized to the central value of the combined set. As can be seen there is reasonable agreement between the three individual sets, and the resulting combined set is a good measure of their common overlap. Note that at large-x differences between the three sets are rather marked, and we expect the resulting combined probability distribution to be rather non-Gaussian.

Bottom Line: We illustrate our strategy with the combination and compression of the recent NNPDF3.0, CT14 and MMHT14 NNLO PDF sets.The resulting compressed Monte Carlo PDF sets are validated at the level of parton luminosities and LHC inclusive cross sections and differential distributions.We determine that around 100 replicas provide an adequate representation of the probability distribution for the original combined PDF set, suitable for general applications to LHC phenomenology.

View Article: PubMed Central - PubMed

Affiliation: Dipartimento di Fisica, Università di Milano and INFN, Sezione di Milano, Via Celoria 16, 20133 Milan, Italy.

ABSTRACT

The current PDF4LHC recommendation to estimate uncertainties due to parton distribution functions (PDFs) in theoretical predictions for LHC processes involves the combination of separate predictions computed using PDF sets from different groups, each of which comprises a relatively large number of either Hessian eigenvectors or Monte Carlo (MC) replicas. While many fixed-order and parton shower programs allow the evaluation of PDF uncertainties for a single PDF set at no additional CPU cost, this feature is not universal, and, moreover, the a posteriori combination of the predictions using at least three different PDF sets is still required. In this work, we present a strategy for the statistical combination of individual PDF sets, based on the MC representation of Hessian sets, followed by a compression algorithm for the reduction of the number of MC replicas. We illustrate our strategy with the combination and compression of the recent NNPDF3.0, CT14 and MMHT14 NNLO PDF sets. The resulting compressed Monte Carlo PDF sets are validated at the level of parton luminosities and LHC inclusive cross sections and differential distributions. We determine that around 100 replicas provide an adequate representation of the probability distribution for the original combined PDF set, suitable for general applications to LHC phenomenology.

No MeSH data available.