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Bayesian inferences in high energy physics often use uniform prior distributions for parameters about which little or no information is available before data are collected. The resulting posterior distributions are therefore sensitive to…

Applications · Statistics 2011-06-03 Luc Demortier , Supriya Jain , Harrison B. Prosper

Within a Bayesian statistical framework using the standard Skyrme-Hartree-Fock model, the maximum {\it a posteriori} (MAP) values and uncertainties of nuclear matter incompressibility and isovector interaction parameters are inferred from…

Nuclear Theory · Physics 2021-12-02 Jun Xu , Zhen Zhang , Bao-An Li

Background: Uncertainty quantification for nuclear theories has gained a more prominent role in the field, with more and more groups attempting to understand the uncertainties on their calculations. However, recent studies have shown that…

Nuclear Theory · Physics 2021-12-03 M. Catacora-Rios , G. B. King , A. E. Lovell , F. M. Nunes

In decommissioning projects of nuclear facilities, the radiological characterisation step aims to estimate the quantity and spatial distribution of different radionuclides. To carry out the estimation, measurements are performed on site to…

Methodology · Statistics 2023-05-15 Martin Wieskotten , Marielle Crozet , Bertrand Iooss , Céline Lacaux , Amandine Marrel

The Best Estimate plus Uncertainty (BEPU) approach for nuclear systems modeling and simulation requires that the prediction uncertainty must be quantified in order to prove that the investigated design stays within acceptance criteria. A…

Computation · Statistics 2023-03-24 Ziyu Xie , Farah Alsafadi , Xu Wu

Breakjunction experiments allow investigating electronic and spintronic properties at the atomic and molecular scale. These experiments generate by their very nature broad and asymmetric distributions of the observables of interest, and…

Mesoscale and Nanoscale Physics · Physics 2023-09-20 Dylan Dyer , Oliver L. A. Monti

The NURE (NUclear REactions for neutrinoless double beta decay) project has been selected for receiving funding in the call Starting Grant 2016 of European Research Council (ERC). The project, which takes advantage of nuclear physics…

Nuclear Experiment · Physics 2020-01-28 M. Cavallaro

Accurately detecting crack boundaries is crucial for reliability assessment and risk management of structures and materials, such as structural health monitoring, diagnostics, prognostics, and maintenance scheduling. Uncertainty…

Computer Vision and Pattern Recognition · Computer Science 2023-06-16 Rahul Rathnakumar , Yutian Pang , Yongming Liu

Cosmological experiments often employ Bayesian workflows to derive constraints on cosmological and astrophysical parameters from their data. It has been shown that these constraints can be combined across different probes such as Planck and…

Cosmology and Nongalactic Astrophysics · Physics 2022-11-28 Harry Bevins , Will Handley , Pablo Lemos , Peter Sims , Eloy de Lera Acedo , Anastasia Fialkov

Following the completion of the second neutron beam line and the related experimental area (EAR2) at the n_TOF spallation neutron source at CERN, several experiments were planned and performed. The high instantaneous neutron flux available…

Instrumentation and Detectors · Physics 2018-03-14 M. Barbagallo , J. Andrzejewski , M. Mastromarco , J. Perkowski , L. A. Damone , A. Gawlik , L. Cosentino , P. Finocchiaro , E. A. Maugeri , A. Mazzone , R. Dressler , S. Heinitz , N. Kivel , D. Schumann , N. Colonna , O. Aberle , S. Amaducci , L. Audouin , M. Bacak , J. Balibrea , F. Bečvář , G. Bellia , E. Berthoumieux , J. Billowes , D. Bosnar , A. Brown , M. Caamaño , F. Calviño , M. Calviani , D. Cano-Ott , R. Cardella , A. Casanovas , F. Cerutti , Y. H. Chen , E. Chiaveri , G. Cortés , M. A. Cortés-Giraldo , S. Cristallo , M. Diakaki , M. Dietz , C. Domingo-Pardo , E. Dupont , I. Durán , B. Fernández-Domínguez , A. Ferrari , P. Ferreira , V. Furman , K. Göbel , A. R. García , S. Gilardoni , T. Glodariu , I. F. Gonçalves , E. González-Romero , E. Griesmayer , C. Guerrero , F. Gunsing , H. Harada , J. Heyse , D. G. Jenkins , E. Jericha , K. Johnston , F. Käppeler , Y. Kadi , A. Kalamara , P. Kavrigin , A. Kimura , M. Kokkoris , M. Krtička , D. Kurtulgil , C. Lederer , H. Leeb , J. Lerendegui-Marco , S. Lo Meo , S. J. Lonsdale , D. Macina , A. Manna , J. Marganiec , T. Martínez , J. G. Martins-Correia , A. Masi , C. Massimi , P. Mastinu , E. Mendoza , A. Mengoni , P. M. Milazzo , F. Mingrone , A. Musumarra , A. Negret , R. Nolte , A. Oprea , A. D. Pappalardo , N. Patronis , A. Pavlik , M. Piscopo , I. Porras , J. Praena , J. M. Quesada , D. Radeck , T. Rauscher , R. Reifarth , M. S. Robles , C. Rubbia , J. A. Ryan , M. Sabaté-Gilarte , A. Saxena , J. Schell , P. Schillebeeckx , P. Sedyshev , A. G. Smith , N. V. Sosnin , A. Stamatopoulos , G. Tagliente , J. L. Tain , A. Tarifeño-Saldivia , L. Tassan-Got , S. Valenta , G. Vannini , V. Variale , P. Vaz , A. Ventura , V. Vlachoudis , R. Vlastou , A. Wallner , S. Warren , C. Weiss , P. J. Woods , T. Wright , P. Žugec

With the help of radial basis function (RBF) and the Garvey-Kelson relation, the accuracy and predictive power of some global nuclear mass models are significantly improved. The rms deviation between predictions from four models and 2149…

Nuclear Theory · Physics 2011-11-18 Ning Wang , Min Liu

We describe the Bayesian Analysis of Nuclear Dynamics (BAND) framework, a cyberinfrastructure that we are developing which will unify the treatment of nuclear models, experimental data, and associated uncertainties. We overview the…

The summation method for the calculation of reactor $\bar{\nu}_e$ fluxes and spectra is methodically revised and improved. For the first time, a complete uncertainty budget accounting for all known effects likely to impact these…

The nuclear physics input from the 3He(alpha,gamma)7Be cross section is a major uncertainty in the fluxes of 7Be and 8B neutrinos from the Sun predicted by solar models and in the 7Li abundance obtained in big-bang nucleosynthesis…

In this work, we propose a two-stage algorithm based on Bayesian modeling and computation aiming at quantifying analyte concentrations or quantities in complex mixtures with Raman spectroscopy. A hierarchical Bayesian model is built for…

Applications · Statistics 2018-05-22 Ningren Han , Rajeev J. Ram

Nuclear cross sections are basic inputs to any nuclear computation. Campaigns of experiments are fitted with the parametric R-matrix model of quantum nuclear interactions, and the resulting cross sections are documented - both point-wise…

The $\mu$Dose system was developed to allow the measurement of environmental levels of natural radioactive isotopes. The system records $\alpha$ and $\beta$ particles along with four decay pairs arising from subsequent decays of…

Neutron capture cross sections are one of the most important nuclear inputs to models of stellar nucleosynthesis of the elements heavier than iron. The activation technique and the time-of-flight method are mostly used to determine the…

The design of reliable indicators to anticipate critical transitions in complex systems is an im portant task in order to detect a coming sudden regime shift and to take action in order to either prevent it or mitigate its consequences. We…

Data Analysis, Statistics and Probability · Physics 2022-12-14 Martin Heßler , Oliver Kamps

We present a simple and efficient Bayesian recursive algorithm for the data-pattern scheme for quantum state reconstruction, which is applicable to situations where measurement settings can be controllably varied efficiently. The algorithm…

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