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Related papers: PartonDensity.jl: a novel parton density determina…

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Over the past decade, new data from HERMES, Jefferson Lab, Fermilab, and RHIC that connect to parton propagation and hadron formation have become available. Semi-inclusive DIS on nuclei, the Drell-Yan reaction, and heavy-ion collisions all…

Nuclear Experiment · Physics 2009-04-02 W. K. Brooks , H. Hakobyan

In this paper, we consider Bayesian point estimation and predictive density estimation in the binomial case. After presenting preliminary results on these problems, we compare the risk functions of the Bayes estimators based on the…

Statistics Theory · Mathematics 2021-09-13 Yasuyuki Hamura

We present an analysis to extract kaon parton distribution functions (PDFs) for the first time using meson-induced Drell-Yan and quarkonium production data. Starting from the statistical model first developed for determining the partonic…

High Energy Physics - Phenomenology · Physics 2023-09-21 Claude Bourrely , Franco Buccella , Wen-Chen Chang , Jen-Chieh Peng

In this paper, we consider the problem of estimating a conditional density in moderately large dimensions. Much more informative than regression functions, conditional densities are of main interest in recent methods, particularly in the…

Methodology · Statistics 2018-01-22 Minh-Lien Jeanne Nguyen

We analyse the latest H1 large rapidity gap data to obtain diffractive parton distributions, using a procedure based on perturbative QCD, and compare them with distributions obtained from the simplified Regge factorisation type of analysis.…

High Energy Physics - Phenomenology · Physics 2008-11-26 A. D. Martin , M. G. Ryskin , G. Watt

We discuss the determination of polarized parton distributions from a next-to-leading order analysis of recent experimental data. We extract the first moment of the polarized quark and gluon distribution and assess the corresponding…

High Energy Physics - Phenomenology · Physics 2007-05-23 Stefano Forte , Richard D. Ball , Giovanni Ridolfi

We study packing densities for set partitions, which is a generalization of packing words. We use results from the literature about packing densities for permutations and words to provide packing densities for set partitions. These results…

Combinatorics · Mathematics 2015-04-10 Adam M. Goyt , Lara K. Pudwell

We introduce a new nonparametric density estimator inspired by Markov Chains, and generalizing the well-known Kernel Density Estimator (KDE). Our estimator presents several benefits with respect to the usual ones and can be used…

Methodology · Statistics 2020-09-15 Andrea De Simone , Alessandro Morandini

We propose a novel and computationally efficient approach for nonparametric conditional density estimation in high-dimensional settings that achieves dimension reduction without imposing restrictive distributional or functional form…

Econometrics · Economics 2025-10-14 Jianhua Mei , Fu Ouyang , Thomas T. Yang

Our focus is on constructing a multiscale nonparametric prior for densities. The Bayes density estimation literature is dominated by single scale methods, with the exception of Polya trees, which favor overly-spiky densities even when the…

Methodology · Statistics 2014-10-06 Antonio Canale , David B. Dunson

Physics prospective of the high density matter using heavy ions collisions is presented. The J-PARC-HI project which is a unique lab to tackle the high density matter physics is described. The world highest rate of heavy ion beam of…

Nuclear Experiment · Physics 2019-04-30 Takao Sakaguchi

A framework is proposed that addresses both conditional density estimation and latent variable discovery. The objective function maximizes explanation of variability in the data, achieved through the optimal transport barycenter generalized…

Optimization and Control · Mathematics 2026-02-18 Hongkang Yang , Esteban G. Tabak

An up-to-date global QCD analysis of high energy lepton-hadron and hadron-hadron interactions is performed to better determine the gluon and quark parton distributions in the nucleon. Improved experimental data on inclusive jet production,…

High Energy Physics - Phenomenology · Physics 2008-11-26 H. L. Lai , J. Huston , S. Kuhlmann , J. Morfin , F. Olness , J. F. Owens , J. Pumplin , W. K. Tung

An updated next-to-leading order (NLO) QCD analysis of all presently available longitudinally polarized deep-inelastic scattering (DIS) data is presented in the framework of the radiative parton model.

High Energy Physics - Phenomenology · Physics 2009-10-31 M. Stratmann

A survey is given on the present knowledge of the polarized parton distribution functions. We give an outlook for further developments desired both on the theoretical as well on the experimental side to complete the understanding of the…

High Energy Physics - Phenomenology · Physics 2007-08-13 Johannes Blümlein

Non perturbative corrections to deep inelastic scattering are computed.

High Energy Physics - Phenomenology · Physics 2016-09-01 Ugo Aglietti

We present MAPPDFpol1.0, a new determination of the helicity-dependent parton distribution functions (PDFs) of the proton from a set of longitudinally polarised inclusive and semi-inclusive deep-inelastic scattering data. The determination…

High Energy Physics - Phenomenology · Physics 2025-04-23 MAP , Collaboration , : , Valerio Bertone , Amedeo Chiefa , Emanuele R. Nocera

Uncertainties in the parametrization of Parton Distribution Functions (PDFs) are becoming a serious limiting systematic uncertainty in Large Hadron Collider (LHC) searches for Beyond the Standard Model physics. This is especially true for…

High Energy Physics - Experiment · Physics 2019-03-13 Christopher Willis , Raymond Brock , Daniel Hayden , Tie-Jiun Hou , Joshua Isaacson , Carl Schmidt , Chien-Peng Yuan

We present fits to determine Parton Distribution Functions using a diverse set of measurements from the ATLAS experiment at the LHC, including inclusive $W$ and $Z$ boson production, $t\bar{t}$ production, $W$+jets and $Z$+jets production,…

High Energy Physics - Experiment · Physics 2022-07-04 Francesco Giuli

A novel nonparametric clustering algorithm is proposed using the interpoint distances between the members of the data to reveal the inherent clustering structure existing in the given set of data, where we apply the classical nonparametric…

Methodology · Statistics 2024-09-02 Soumita Modak