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Photospheric electric fields, estimated from sequences of vector magnetic field and Doppler measurements, can be used to estimate the flux of magnetic energy (the Poynting flux) into the corona and as time-dependent boundary conditions for…

Solar and Stellar Astrophysics · Physics 2014-10-13 Maria D. Kazachenko , George H. Fisher , Brian T. Welsch

A kernel method for estimating a probability density function (pdf) from an i.i.d. sample drawn from such density is presented. Our estimator is a linear combination of kernel functions, the coefficients of which are determined by a linear…

Statistics Theory · Mathematics 2023-04-20 Yoshihito Kazashi , Fabio Nobile

Very often, in the course of uncertainty quantification tasks or data analysis, one has to deal with high-dimensional random variables (RVs). A high-dimensional RV can be described by its probability density (pdf) and/or by the…

Numerical Analysis · Mathematics 2022-09-09 Alexander Litvinenko , Youssef Marzouk , Hermann G. Matthies , Marco Scavino , Alessio Spantini

Machine learning models based on convolutional neural networks have been used for predicting space groups of crystal structures from their atomic pair distribution function (PDF). However, the PDFs used to train the model are calculated…

Materials Science · Physics 2022-06-20 Ling Lan , Chia-Hao Liu , Qiang Du , Simon J. L. Billinge

We provide an assessment of the impact of parton distributions on the determination of LHC processes, and of the accuracy with which parton distributions (PDFs) can be extracted from data, in particular from current and forthcoming HERA…

We present the first Monte Carlo based global QCD analysis of spin-averaged and spin-dependent parton distribution functions (PDFs) that includes nucleon isovector matrix elements in coordinate space from lattice QCD. We investigate the…

High Energy Physics - Phenomenology · Physics 2021-01-13 J. Bringewatt , N. Sato , W. Melnitchouk , Jian-Wei Qiu , F. Steffens , M. Constantinou

This paper provides tight bounds on the R\'enyi entropy of a function of a discrete random variable with a finite number of possible values, where the considered function is not one-to-one. To that end, a tight lower bound on the R\'enyi…

Information Theory · Computer Science 2018-12-11 Igal Sason

Transverse and longitudinal electroweak gauge boson parton distribution functions (PDFs) are computed in terms of deep-inelastic scattering structure functions, following the recently developed method to determine the photon PDF. The…

High Energy Physics - Phenomenology · Physics 2018-05-18 Bartosz Fornal , Aneesh V. Manohar , Wouter J. Waalewijn

The random variable simulation problem consists in using a $k$-dimensional i.i.d. random vector $X^{k}$ with distribution $P_{X}^{k}$ to simulate an $n$-dimensional i.i.d. random vector $Y^{n}$ so that its distribution is approximately…

Information Theory · Computer Science 2018-12-24 Lei Yu , Vincent Y. F. Tan

This paper investigates probability density functions (PDFs) that are continuous everywhere, nearly uniform around the mode of distribution, and adaptable to a variety of distribution shapes ranging from bell-shaped to rectangular. From the…

Machine Learning · Computer Science 2022-04-01 Osamu Fujita

An overwhelming number of theoretical predictions for hadron colliders require parton distribution functions (PDFs), which are an important ingredient of theory infrastructure for the next generation of high-energy experiments. This…

Two typical fixed-length random number generation problems in information theory are considered for general sources. One is the source resolvability problem and the other is the intrinsic randomness problem. In each of these problems, the…

Information Theory · Computer Science 2024-05-14 Ryo Nomura , Hideki Yagi

We present a method developed by the NNPDF Collaboration that allows the inclusion of new experimental data into an existing set of parton distribution functions without the need for a complete refit. A Monte Carlo ensemble of PDFs may be…

High Energy Physics - Phenomenology · Physics 2015-06-03 Francesco Cerutti , Nathan Hartland

Most of the existing classification methods are aimed at minimization of empirical risk (through some simple point-based error measured with loss function) with added regularization. We propose to approach this problem in a more information…

Machine Learning · Computer Science 2015-01-22 Wojciech Marian Czarnecki , Jacek Tabor

We consider some trends, achievements and a series of remaining problems in the precision determination of parton distribution functions. For the description of the scaling violations of the deep-inelastic scattering data, forming the key…

High Energy Physics - Phenomenology · Physics 2018-09-05 S. Alekhin , J. Bluemlein , S. -O. Moch

Reliable density estimation is fundamental for numerous applications in statistics and machine learning. In many practical scenarios, data are best modeled as mixtures of component densities that capture complex and multimodal patterns.…

Machine Learning · Computer Science 2025-09-30 Mustafa Musab , Joseph K. Chege , Arie Yeredor , Martin Haardt

We propose a field theoretic framework for calculating the dependence of R\'enyi entropies on the shape of the entangling surface in a conformal field theory. Our approach rests on regarding the corresponding twist operator as a conformal…

High Energy Physics - Theory · Physics 2016-08-24 Lorenzo Bianchi , Marco Meineri , Robert C. Myers , Michael Smolkin

A task is randomly drawn from a finite set of tasks and is described using a fixed number of bits. All the tasks that share its description must be performed. Upper and lower bounds on the minimum $\rho$-th moment of the number of performed…

Information Theory · Computer Science 2014-10-07 Christoph Bunte , Amos Lapidoth

We study the statistical complexity of estimating partition functions given sample access to a proposal distribution and an unnormalized density ratio for a target distribution. While partition function estimation is a classical problem,…

Machine Learning · Statistics 2026-03-02 Adam Block , Abhishek Shetty

Fant\^omas is a C++ toolkit for exploring the parametrization dependence of parton distribution functions (PDFs) and other correlator functions in quantum chromodynamics (QCD). Fant\^omas facilitates the generation of adaptable polynomial…

High Energy Physics - Phenomenology · Physics 2025-08-01 Lucas Kotz , Aurore Courtoy , T. J. Hobbs , Pavel Nadolsky , Fredrick Olness , Maximiliano Ponce-Chavez , Varada Purohit
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