The Asymmetry of Antimatter in the Proton

Dove, J.; Kerns, B.; McClellan, R. E.; Miyasaka, S.; Morton, D. H.; Nagai, K.; Prasad, S.; Sanftl, F.; Scott, M. B. C.; Tadepalli, A. S.; Aidala, C. A.; Arrington, J.; Ayuso, C.; Barker, C. L.; Brown, C. N.; Chang, W. C.; Chen, A.; Christian, D. C.; Dannowitz, B. P.; Daugherity, M.; Diefenthaler, M.; Fassi, L. El; Geesaman, D. F.; Gilman, R.; Goto, Y.; Guo, L.; Guo, R.; Hague, T. J.; Holt, R. J.; Isenhower, D.; Kinney, E. R.; Kitts, N.; Klein, A.; Kleinjan, D. W.; Kudo, Y.; Leung, C.; Lin, P. -J.; Liu, K.; Liu, M. X.; Lorenzon, W.; Makins, N. C. R.; de Medeiros, M. Mesquita; McGaughey, P. L.; Miyachi, Y.; Mooney, I.; Nakahara, K.; Nakano, K.; Nara, S.; Peng, J. -C.; Puckett, A. J.; Ramson, B. J.; Reimer, P. E.; Rubin, J. G.; Sawada, S.; Sawada, T.; Shibata, T. -A.; Su, D.; Teo, M.; Tice, B. G; Towell, R. S.; Uemura, S.; Watson, S.; Wang, S. G.; Wickes, A. B.; Wu, J.; Xi, Z.; Ye, Z.

doi:10.48550/arxiv.2103.04024

Cited by 5 publications

(18 citation statements)

References 28 publications

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“…7.3.5. For fixed-target DY, we include the recent measurement for the proton-deuteron to proton-proton differential cross-section ratio performed by the E906/SeaQuest experiment [115].…”

Section: Overview Of the Nnpdf40 Datasetmentioning

confidence: 99%

“…We consider the new measurement recently performed by the SeaQuest experiment at Fermilab [115] for production of a Z boson decaying into muon pairs. Like the previous NuSea measurement [45], which was included in the NNPDF3.1 dataset, the SeaQuest experiment measures the ratio of the scattering crosssection of a proton beam off a deuterium target to the cross-section off a proton target.…”

Section: Fixed-target Drell-yan Productionmentioning

confidence: 99%

“…( 10) in Ref. [115]. Fast interpolation tables accurate to NLO are generated with APFEL; these are then supplemented with a NNLO K-factor computed with a version of Vrap [160] that we modified to account for the isoscalarity of the deuteron target.…”

Section: Fixed-target Drell-yan Productionmentioning

confidence: 99%

“…Overall, 1416 and 1417 data points come from nuclear measurements in the NLO and NNLO fits respectively, which amount to about 30% of the full dataset. All of these datasets but SeaQuest [115] were already included in the previous NNPDF3.1 determination [5].…”

Section: Treatment Of Nuclear Correctionsmentioning

confidence: 99%

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The Path to Proton Structure at One-Percent Accuracy

Ball,

Carrazza,

Cruz-Martinez

et al. 2021

Preprint

144

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We present a new set of parton distribution functions (PDFs) based on a fully global dataset and machine learning techniques: NNPDF4.0. We expand the NNPDF3.1 determination with 44 new datasets, mostly from the LHC. We derive a novel methodology through hyperparameter optimisation, leading to an efficient fitting algorithm built upon stochastic gradient descent. We use NNLO QCD calculations and account for NLO electroweak corrections and nuclear uncertainties. Theoretical improvements in the PDF description include a systematic implementation of positivity constraints and integrability of sum rules. We validate our methodology by means of closure tests and "future tests" (i.e. tests of backward and forward data compatibility), and assess its stability, specifically upon changes of PDF parametrization basis. We study the internal compatibility of our dataset, and investigate the dependence of results both upon the choice of input dataset and of fitting methodology. We perform a first study of the phenomenological implications of NNPDF4.0 on representative LHC processes. The software framework used to produce NNPDF4.0 is made available as an open-source package together with documentation and examples.

show abstract

“…7.3.5. For fixed-target DY, we include the recent measurement for the proton-deuteron to proton-proton differential cross-section ratio performed by the E906/SeaQuest experiment [115].…”

Section: Overview Of the Nnpdf40 Datasetmentioning

confidence: 99%

Section: Fixed-target Drell-yan Productionmentioning

confidence: 99%

Section: Fixed-target Drell-yan Productionmentioning

confidence: 99%

Section: Treatment Of Nuclear Correctionsmentioning

confidence: 99%

See 2 more Smart Citations

The Path to Proton Structure at One-Percent Accuracy

Ball,

Carrazza,

Cruz-Martinez

et al. 2021

Preprint

144

View full text Add to dashboard Cite

show abstract

“…Eq. (21) is the Bayesian answer to the inverse problem, our knowledge of the model u is encoded in the probability measure µ G X , which is fully specified by the density π G X . There are several ways to characterise a probability distribution, a task that becomes increasingly difficult in high-dimensional spaces.…”

Section: Statement Of the Problemmentioning

confidence: 99%

Bayesian Approach to Inverse Problems: an Application to NNPDF Closure Testing

Del Debbio,

Giani,

Wilson

2021

Preprint

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We discuss the Bayesian approach to the solution of inverse problems and apply the formalism to analyse the closure tests performed by the NNPDF collaboration. Starting from a comparison with the approach that is currently used for the determination of parton distributions (PDFs) by the NNPDF collaboration, we discuss some analytical results that can be obtained for linear problems and use these results as a guidance for the more complicated non-linear problems. We show that, in the case of Gaussian distributions, the posterior probability density of the parametrized PDFs is fully determined by the results of the NNPDF fitting procedure. In the particular case that we consider, the fitting procedure and the Bayesian analysis yield exactly the same result. Building on the insight that we obtain from the analytical results, we introduce new estimators to assess the statistical faithfulness of the fit results in closure tests. These estimators are defined in data space, and can be studied analytically using the Bayesian formalism in a linear model in order to clarify their meaning. Finally we present numerical results from a number of closure tests performed with current NNPDF methodologies. These further tests allow us to validate the NNPDF4.0 methodology and provide a quantitative comparison of the NNPDF4.0 and NNPDF3.1 methodologies. As PDFs determinations move into precision territory, the need for a careful validation of the methodology becomes increasingly important: the error bar has become the focal point of contemporary PDFs determinations. In this perspective, theoretical assumptions and other sources of error are best formulated and analysed in the Bayesian framework, which provides an ideal language to address the precision and the accuracy of current fits.

show abstract

The path to proton structure at 1% accuracy

et al. 2022

View full text Add to dashboard Cite

We present a new set of parton distribution functions (PDFs) based on a fully global dataset and machine learning techniques: NNPDF4.0. We expand the NNPDF3.1 determination with 44 new datasets, mostly from the LHC. We derive a novel methodology through hyperparameter optimization, leading to an efficient fitting algorithm built upon stochastic gradient descent. We use NNLO QCD calculations and account for NLO electroweak corrections and nuclear uncertainties. Theoretical improvements in the PDF description include a systematic implementation of positivity constraints and integrability of sum rules. We validate our methodology by means of closure tests and “future tests” (i.e. tests of backward and forward data compatibility), and assess its stability, specifically upon changes of PDF parametrization basis. We study the internal compatibility of our dataset, and investigate the dependence of results both upon the choice of input dataset and of fitting methodology. We perform a first study of the phenomenological implications of NNPDF4.0 on representative LHC processes. The software framework used to produce NNPDF4.0 is made available as an open-source package together with documentation and examples.

show abstract

The Asymmetry of Antimatter in the Proton

Cited by 5 publications

References 28 publications

The Path to Proton Structure at One-Percent Accuracy

The Path to Proton Structure at One-Percent Accuracy

Bayesian Approach to Inverse Problems: an Application to NNPDF Closure Testing

The path to proton structure at 1% accuracy

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