相关论文: Reweighting NNPDFs: the W lepton asymmetry
Probabilistic models analyze data by relying on a set of assumptions. Data that exhibit deviations from these assumptions can undermine inference and prediction quality. Robust models offer protection against mismatch between a model's…
A precise knowledge of nuclear parton distribution functions (nPDFs) is -- among other things -- important for the unambiguous interpretation of hard process data taken in pA and AA collisions at the Relativistic Heavy Ion Collider (RHIC)…
We show how a precise knowledge of parton distribution functions, in particular those of the u and d quarks, can be used to constrain a certain class of New Physics models in which new heavy charged resonances couple to quarks and leptons.…
Adaptive networks rely on in-network and collaborative processing among distributed agents to deliver enhanced performance in estimation and inference tasks. Information is exchanged among the nodes, usually over noisy links. The…
We report a parton distribution function (PDF) analysis of a complete set of hadron scattering data, in which a color-octet fermion (such as a gluino of supersymmetry) is incorporated as an extra parton constituent along with the usual…
In global fits of parton distribution functions (PDFs) a large fraction of data points, mostly from the HERA collider, lies in a region of $x$ and $Q^2$ that is sensitive to small-$x$ logarithmic enhancements. Thus, the proper theoretical…
A new parametrization for the parton distribution functions with a higher flexibility in the small-$x$ region is presented. It has been implemented in the xFitter open-source PDF fitting tool, and compared to the default xFitter…
We propose a two-component mixture of a noninformative (diffuse) and an informative prior distribution, weighted through the data in such a way to prefer the first component if a prior-data conflict arises. The data-driven approach for…
Dirichlet process mixture model (DPMM) is a popular Bayesian nonparametric model. In this paper, we apply this model to weighted data and then estimate the un-weighted distribution from the corresponding weighted distribution using the…
The quantum statistical parton distributions approach proposed more than one decade ago is revisited by considering a larger set of recent and accurate Deep Inelastic Scattering experimental results. It enables us to improve the description…
We discuss the current developments by the European Twisted Mass Collaboration in extracting parton distribution functions from the quasi-PDF approach. We concentrate on the non-perturbative renormalization prescription recently developed…
A new QCD analysis of Deep Inelastic Scattering (DIS) data is presented. All available neutrino and anti-neutrino cross sections are reanalysed and included in the fit, along with charged-lepton DIS and Drell-Yan data. A massive…
To address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categories, i.e., static regularization-based pruning and dynamic…
We revise the relation between Parton Distribution Functions (PDFs) and matrix elements computable from lattice QCD, focusing on the quasi-Parton Distribution Functions (qPDFs) approach. We exploit the relation between PDFs and qPDFs in the…
We study the charged current Drell-Yan process and we evaluate the proton parton densities uncertainties on the lepton transverse momentum distribution and their impact on the determination of the W-boson mass. We consider the global PDF…
We present data-driven methods for the full reconstruction of jets in heavy ion collisions, for inclusive and co-incidence jet measurements at both RHIC and LHC. The complex structure of heavy ion events generates a large background of…
Deep Neural Networks (DNNs) are computationally and memory intensive, which makes their hardware implementation a challenging task especially for resource constrained devices such as IoT nodes. To address this challenge, this paper…
Estimations of physical parameters using data usually involve non-uniform experimental efficiencies. In this article, a method of maximum likelihood fit is introduced using the efficiency as a weight, while the probability distribution…
In this work, we consider the problem of imbalanced data in a regression framework when the imbalanced phenomenon concerns continuous or discrete covariates. Such a situation can lead to biases in the estimates. In this case, we propose a…
We present recent W and charged lepton asymmetry measurements from the CDF and D0 experiments. Theoretical predictions agree with the CDF W asymmetry, measured using a new matrix element technique. These theoretical predictions are less…