CJ15 global PDF analysis with new electroweak data from the STAR and SeaQuest experiments

Park, Sanghwa; Accardi, Alberto; Jing, Xiaoxian; Owens, J. F.

doi:10.48550/arxiv.2108.05786

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…There are several groups providing regular updates of PDFs via global fits, see refs. [12][13][14][15][16][17][18][19] for recent results on PDF determinations. The difference between those PDF sets is mainly due to the choice of the experimental data sets, the theoretical calculations used, and the parametrization form of PDFs.…”

Section: Introductionmentioning

confidence: 99%

Machine learning of log-likelihood functions in global analysis of parton distributions

Liu

Sun

Gao

2022

J. High Energ. Phys.

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Modern analysis on parton distribution functions (PDFs) requires calculations of the log-likelihood functions from thousands of experimental data points, and scans of multi-dimensional parameter space with tens of degrees of freedom. In conventional analysis the Hessian approximation has been widely used for the estimation of the PDF uncertainties. The Lagrange Multiplier (LM) scan while being a more faithful method is less used due to computational limitations, and is the main focus of this study. We propose to use Neural Networks (NNs) and machine learning techniques to model the profile of the log-likelihood functions or cross sections for multi-dimensional parameter space in order to overcome those limitations which work beyond the quadratic approximations and meanwhile ensures efficient scans of the full parameter space. We demonstrate the efficiency of the new approach in the framework of the CT18 global analysis of PDFs by constructing NNs for various target functions, and performing LM scans on PDFs and cross sections at hadron colliders. We further study the impact of the NOMAD dimuon data on constraining PDFs with the new approach, and find enhanced strange-quark distributions and reduced PDF uncertainties. Moreover, we show how the approach can be used to constrain new physics beyond the Standard Model (BSM) by a joint fit of both PDFs and Wilson coefficients of operators in the SM effective field theory.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine learning of log-likelihood functions in global analysis of parton distributions

Liu

Sun

Gao

2022

J. High Energ. Phys.

View full text Add to dashboard Cite

show abstract

“…There are several groups providing regular updates of PDFs via global fits, see Refs. [12][13][14][15][16][17][18][19] for recent results on PDF determinations. The difference between those PDF sets is mainly due to the choice of the experimental data sets, the theoretical calculations used, and the parametrization form of PDFs.…”

mentioning

confidence: 99%

Machine learning of log-likelihood functions in global analysis of parton distributions

Liu¹,

Sun²,

Gao³

2022

Preprint

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Modern analysis on parton distribution functions (PDFs) requires calculations of the log-likelihood functions from thousands of experimental data points, and scans of multidimensional parameter space with tens of degrees of freedom. In conventional analysis the Hessian method has been widely used to approximate profile of the log-likelihood functions in neighborhood of the best-fit, and to estimate the PDF uncertainties. We propose to use Neural Networks (NNs) and machine learning techniques to model profile of the log-likelihood functions or cross sections for multi-dimensional parameter space, which works beyond the quadratic approximations and meanwhile ensures efficient scans of the full parameter space. We demonstrate efficiency of the new approach in the framework of CT18 global analysis of PDFs by constructing NNs for various target functions, and performing Lagrangian multiplier scans on PDFs and cross sections at hadron colliders. We further study impact of the NOMAD dimuon data on constraining PDFs with the new approach, and find enhanced strange-quark distributions and reduced PDF uncertainties. Moreover, we show how the approach can be used to constrain new physics beyond the Standard Model (BSM) by a joint fit of both PDFs and Wilson coefficients of operators in the SM effective field theory.

show abstract

CJ15 global PDF analysis with new electroweak data from the STAR and SeaQuest experiments

Cited by 2 publications

References 11 publications

Machine learning of log-likelihood functions in global analysis of parton distributions

Machine learning of log-likelihood functions in global analysis of parton distributions

Machine learning of log-likelihood functions in global analysis of parton distributions

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