The PDF4LHC21 combination of global PDF fits for the LHC Run III

Ball, Richard D.; Butterworth, Jon; Cooper-Sarkar, Amanda M.; Courtoy, Aurore; Cridge, Thomas; De Roeck, Albert; Feltesse, Joel; Forte, Stefano; Giuli, Francesco; Gwenlan, Claire; Harland-Lang, Lucian A.; Hobbs, T. J.; Hou, Tie-Jiun; Huston, Joey; McNulty, Ronan; Nadolsky, Pavel M.; Nocera, Emanuele R.; Rabemananjara, Tanjona R.; Rojo, Juan; Thorne, Robert S.; Xie, Keping; Yuan, C. -P.

doi:10.48550/arxiv.2203.05506

Cited by 8 publications

(17 citation statements)

References 164 publications

(284 reference statements)

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“…When CT18 [5] and NNPDF3.1.1 [4] NNLO PDFs were compared in Sec. 2 of the 2021 benchmarking study by the PDF4LHC group [15], the former systematically predicted a larger uncertainty in the moderate x region than the latter. The magnitudes of the MSHT20 NNLO uncertainties [7] in these comparisons tended to lie between the CT18 and NNPDF3.1 ones.…”

Section: Introductionmentioning

confidence: 88%

“…energy 14 TeV. The cross sections are computed with N p = 900 error sets of the baseline PDF4LHC21 PDF ensemble [15] consisting of 300 MSHT20, 300 NNPDF3.1.1, and 300 CT18 replicas. [The replicas are ordered as in the actual 900replica baseline ensemble.…”

Section: B a Toy Examplementioning

confidence: 99%

“…The possible existence of an unstated source of the PDF uncertainty is suggested by an observation that, while several recent global analyses constrain the PDFs with comparably strong sets of fitted experimental data, in some phenomenologically important cases these analyses arrive at noticeably different estimates of uncertainties on the PDFs and PDF-dependent predictions. For example, the estimates for the correlated uncertainties on key LHC and Tevatron total cross sections presented in [14,15] vary between the recent PDF fits. In Fig.…”

Section: Introductionmentioning

confidence: 99%

“…More intriguingly, in the course of the PDF4LHC21 exercise, the three global PDF groups conducted fits to a set of common data, using The Higgs cross sections are obtained at NNLO multiplied by an N3LO/NNLO K factor of 1.097 and by an EW K factor of 1.0514. Predictions are shown for PDF4LHC21 [15], PDF4LHC15 [16], NNPDF4.0 [8], CT18 [5], MSHT20 [7], ABMP16 [3], and ATLASpdf21 [9] NNLO PDFs with αs(MZ ) = 0.118.…”

Section: Introductionmentioning

confidence: 99%

“…The common data set (termed the "reduced set") was diverse enough to provide constraints on all PDF flavors, but limited enough so that all groups were expected to find similar estimates of PDF uncertainties. In the fits to the same reduced data set [15,Sec. 3, especially Figs.…”

Section: Introductionmentioning

confidence: 99%

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Parton distributions need representative sampling

Courtoy¹,

Houston²,

Nadolsky³

et al. 2022

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In global QCD fits of parton distribution functions (PDFs), a large part of the estimated uncertainty on the PDFs originates from the choices of parametric functional forms and fitting methodology. We argue that these types of uncertainties can be underestimated with common PDF ensembles in high-stake measurements at the Large Hadron Collider and Tevatron. A fruitful approach to quantify these uncertainties is to view them as arising from sampling of allowed PDF solutions in a multidimensional parametric space. This approach applies powerful insights gained in recent statistical studies of large-scale population surveys and quasi-Monte Carlo integration methods. In particular, PDF fits may be affected by the big data paradox, which stipulates that more experimental data do not automatically raise the accuracy of PDFs -close attention to the data quality and sampling of possible PDF solutions is as essential. To test if the sampling of the PDF uncertainty of an experimental observable is truly representative of all acceptable solutions, we introduce a technique ("a hopscotch scan") based on a combination of parameter scans and stochastic sampling. With this technique, we show that the PDF uncertainty on key LHC cross sections at 13 TeV obtained with the public NNPDF4.0 fitting code is larger than the nominal uncertainty obtained with the published NNPDF4.0 Monte-Carlo replica sets. In the PDF ensembles obtained in the analytic minimization (Hessian) formalism, the tolerance on the PDF uncertainty must be based on sufficiently complete sampling of PDF functional forms and choices of the experiments.

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Section: Introductionmentioning

confidence: 88%

Section: B a Toy Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parton distributions need representative sampling

Courtoy¹,

Houston²,

Nadolsky³

et al. 2022

Preprint

Self Cite

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The path to proton structure at 1% accuracy

et al. 2022

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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 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.

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Parton distributions and new physics searches: the Drell–Yan forward–backward asymmetry as a case study

et al. 2022

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We discuss the sensitivity of theoretical predictions of observables used in searches for new physics to parton distributions (PDFs) at large momentum fraction x. Specifically, we consider the neutral-current Drell–Yan production of gauge bosons with invariant masses in the TeV range, for which the forward–backward asymmetry of charged leptons from the decay of the gauge boson in its rest frame is a traditional probe of new physics. We show that the qualitative behaviour of the asymmetry depends strongly on the assumptions made in determining the underlying PDFs. We discuss and compare the large-x behaviour of various different PDF sets, and find that they differ significantly. Consequently, the shape of the asymmetry observed at lower dilepton invariant masses, where all PDF sets are in reasonable agreement because of the presence of experimental constraints, is not necessarily reproduced at large masses where the PDFs are mostly unconstrained by data. It follows that the shape of the asymmetry at high masses may depend on assumptions made in the PDF parametrization, and thus deviations from the traditionally expected behaviour cannot be taken as a reliable indication of new physics. We demonstrate that forward–backward asymmetry measurements could help in constraining PDFs at large x and discuss the accuracy that would be required to disentangle the effects of new physics from uncertainties in the PDFs in this region.

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The PDF4LHC21 combination of global PDF fits for the LHC Run III

Cited by 8 publications

References 164 publications

Parton distributions need representative sampling

Parton distributions need representative sampling

The path to proton structure at 1% accuracy

Parton distributions and new physics searches: the Drell–Yan forward–backward asymmetry as a case study

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