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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 correlation coefficients computed from the reference Monte Carlo combined set and from the CMC-PDFs with  replicas. We show here the results for the correlations between the inclusive LHC cross sections, using the settings described in the text. Each plot contains the correlation coefficient of a given cross section with respect to all the other inclusive cross sections considered here. To gauge the effectiveness of the compression algorithm, we also show the 68 % confidence-level interval for the correlation coefficients computed from  random partitions of  replicas each
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Fig24: Comparison of the correlation coefficients computed from the reference Monte Carlo combined set and from the CMC-PDFs with replicas. We show here the results for the correlations between the inclusive LHC cross sections, using the settings described in the text. Each plot contains the correlation coefficient of a given cross section with respect to all the other inclusive cross sections considered here. To gauge the effectiveness of the compression algorithm, we also show the 68 % confidence-level interval for the correlation coefficients computed from random partitions of replicas each

Mentions: To validate that the compression algorithm presented here also maintains the correlations of the original set, we have computed the correlations between all processes used in the previous section, both for the MC900 prior and for the CMC-PDF100 set. The results are shown in Fig. 24, for the NLO and NNLO inclusive cross sections shown in Figs. 21 and 25, for the case of differential distributions shown in Fig. 22. We have also verified that from replicas onwards the correlations are very well reproduced by the compressed set.


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 correlation coefficients computed from the reference Monte Carlo combined set and from the CMC-PDFs with  replicas. We show here the results for the correlations between the inclusive LHC cross sections, using the settings described in the text. Each plot contains the correlation coefficient of a given cross section with respect to all the other inclusive cross sections considered here. To gauge the effectiveness of the compression algorithm, we also show the 68 % confidence-level interval for the correlation coefficients computed from  random partitions of  replicas each
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig24: Comparison of the correlation coefficients computed from the reference Monte Carlo combined set and from the CMC-PDFs with replicas. We show here the results for the correlations between the inclusive LHC cross sections, using the settings described in the text. Each plot contains the correlation coefficient of a given cross section with respect to all the other inclusive cross sections considered here. To gauge the effectiveness of the compression algorithm, we also show the 68 % confidence-level interval for the correlation coefficients computed from random partitions of replicas each
Mentions: To validate that the compression algorithm presented here also maintains the correlations of the original set, we have computed the correlations between all processes used in the previous section, both for the MC900 prior and for the CMC-PDF100 set. The results are shown in Fig. 24, for the NLO and NNLO inclusive cross sections shown in Figs. 21 and 25, for the case of differential distributions shown in Fig. 22. We have also verified that from replicas onwards the correlations are very well reproduced by the compressed set.

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.