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Related papers: On Uncertainty of Compton Backscattering Process

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The backward Compton scattering is a basic process at future higher energy photon colliders. To obtain a high probability of e->gamma conversion the density of laser photons in the conversion region should be so high that simultaneous…

High Energy Physics - Phenomenology · Physics 2008-11-26 M. Galynskii , E. Kuraev , M. Levchuk , V. Telnov

A unique feature of generalised parton distributions is their relation to the QCD energy-momentum tensor. In particular, they provide access to the mechanical properties of the proton i.e. the distributions of pressure and shear stress…

High Energy Physics - Phenomenology · Physics 2021-04-28 H. Dutrieux , C. Lorcé , H. Moutarde , P. Sznajder , A. Trawiński , J. Wagner

This paper considers uncertainty quantification for an elliptic nonlocal equation. In particular, it is assumed that the parameters which define the kernel in the nonlocal operator are uncertain and a priori distributed according to a…

Computation · Statistics 2016-03-22 Ajay Jasra , Kody Law , Yan Zhou

This paper designs a statistical quantification towards the intermittent power uncertainty in power systems. A negative-exponential forecast uncertainty function is constructed to represent the relationship between the statistics of…

Systems and Control · Computer Science 2017-07-13 Zongjie Wang , Zhizhong Guo

We propose a new way to experimentally determine the subleading low-energy structure constant of doubly-virtual Compton scattering on a proton. Such empirical determination will reduce the theoretical model error in estimates of the…

High Energy Physics - Phenomenology · Physics 2020-09-16 Vladyslav Pauk , Carl E. Carlson , Marc Vanderhaeghen

Monte Carlo methods are widely used importance sampling techniques for studying complex physical systems. Integrating these methods with deep learning has significantly improved efficiency and accuracy in high-dimensional problems and…

Disordered Systems and Neural Networks · Physics 2024-12-24 Yixiong Ren , Jianhui Zhou

Among the various options to estimate uncertainty in deep neural networks, Monte-Carlo dropout is widely popular for its simplicity and effectiveness. However the quality of the uncertainty estimated through this method varies and choices…

Machine Learning · Computer Science 2021-07-14 Francesco Verdoja , Ville Kyrki

First-principles Markov Chain Monte Carlo sampling is used to investigate uncertainty quantification and uncertainty propagation in parameters describing hydrogen kinetics. Specifically, we sample the posterior distribution of thirty-one…

Numerical Analysis · Mathematics 2018-03-12 John Bell , Marcus Day , Jonathan Goodman , Ray Grout , Matthias Morzfeld

In order to investigate the effects of nonmagnetic impurities in strongly correlated systems, Quantum Monte Carlo (QMC) simulations have been carried out for the doped two-dimensional Hubbard model with one nonmagnetic impurity. Using a…

Strongly Correlated Electrons · Physics 2009-11-10 N. Bulut

Discrepancies play an important role in the study of uniformity properties of point sets. Their probability distributions are a help in the analysis of the efficiency of the Quasi Monte Carlo method of numerical integration, which uses…

High Energy Physics - Phenomenology · Physics 2007-05-23 A. F. W. van Hameren

The problem of estimating certain distributions over $\{0,1\}^d$ is considered here. The distribution represents a quantum system of $d$ qubits, where there are non-trivial dependencies between the qubits. A maximum entropy approach is…

Computation · Statistics 2019-03-08 Ryan Bennink , Ajay Jasra , Kody J. H. Law , Pavel Lougovski

Starting from the observation that soft rescattering of hard scattered partons from the colour charged remnant plays an important role in proton-proton collisions a similar model for soft scatterings in a quark-gluon plasma was constructed…

High Energy Physics - Phenomenology · Physics 2009-09-29 Korinna Zapp

Numerous studies have focused on learning and understanding the dynamics of physical systems from video data, such as spatial intelligence. Artificial intelligence requires quantitative assessments of the uncertainty of the model to ensure…

Machine Learning · Computer Science 2024-12-18 Aoming Liang , Qi Liu , Lei Xu , Fahad Sohrab , Weicheng Cui , Changhui Song , Moncef Gabbouj

Random matrix theory is a useful tool in the study of the physics of multiple scattering systems, often striking a balance between computation speed and physical rigour. Propagation of waves through thick disordered media, as arises in for…

Mathematical Physics · Physics 2024-05-07 Niall Byrnes , Gary R. W. Greaves , Matthew R. Foreman

We report several important observations that underscore the distinctions between the constrained-path Monte Carlo method and the continuum and lattice versions of the fixed-node method. The main distinctions stem from the differences in…

Condensed Matter · Physics 2009-10-31 J. Carlson , J. E. Gubernatis , G. Ortiz , S. Zhang

Electricity systems are experiencing increased effects of randomness and variability due to emerging stochastic assets. The increased effects introduce new uncertainties into power systems that can impact system operability and reliability.…

Systems and Control · Electrical Eng. & Systems 2022-11-10 Naeem Turner-Bandele , Amritanshu Pandey , Larry Pileggi

Computational models in chemistry rely on a number of approximations. The effect of such approximations on observables derived from them is often unpredictable. Therefore, it is challenging to quantify the uncertainty of a computational…

Chemical Physics · Physics 2017-04-21 Gregor N. Simm , Jonny Proppe , Markus Reiher

At the KATRIN experiment, the electron antineutrino mass is inferred from the shape of the $\beta$-decay spectrum of tritium. Important systematic effects in the Windowless Gaseous Tritium Source (WGTS) of the experiment include the energy…

Instrumentation and Methods for Astrophysics · Physics 2022-10-05 Jonas Kellerer , Felix Spanier

As penetration of wind power generation increases, system operators must account for its stochastic nature in a reliable and cost-efficient manner. These conflicting objectives can be traded-off by accounting for the variability and…

Optimization and Control · Mathematics 2015-11-11 Yury Dvorkin , Miles Lubin , Scott Backhaus , Michael Chertkov

We derive the general analytical expressions for the statistical uncertainties of cumulants up to fourth order including an efficiency correction. The analytical expressions have been tested with a toy Monte Carlo model analysis. An…

Nuclear Theory · Physics 2022-03-25 Fan Si , Yifei Zhang