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$\mu$Dose is a novel compact analytical instrument for assessing low level $^{238}$U, $^{235}$U, $^{232}$Th decay chains and $^{40}$K radioactivity. The system is equipped with a dual $\alpha$/$\beta$ scintillator allowing to discriminate…

Most conventional risk analysis methods rely on a single best estimate of exposure per person which does not allow for adjustment for exposure-related uncertainty. Here, we propose a Bayesian model averaging method to properly quantify the…

Applications · Statistics 2020-04-07 Deukwoo Kwon , F. Owen Hoffman , Brian E. Moroz , Steven L. Simon

The paper presents alternative statistical methods for biological dosimetry, such as the Bayesian and Monte Carlo method. The classical Gaussian and robust Bayesian fit algorithms for the linear, linear-quadratic as well as saturated and…

Methodology · Statistics 2014-12-08 Krzysztof Wojciech Fornalski

In radiotherapy, a trade-off exists between computational workload/speed and dose calculation accuracy. Calculation methods like pencil-beam convolution can be much faster than Monte-Carlo methods, but less accurate. The dose difference,…

Medical Physics · Physics 2020-05-18 Yixun Xing , Ph. D. , You Zhang , Ph. D. , Dan Nguyen , Ph. D. , Mu-Han Lin , Ph. D. , Weiguo Lu , Ph. D. , Steve Jiang , Ph. D

In this work, a method is proposed for combining differential and integral benchmark experimental data within a Bayesian framework for nuclear data adjustments and multi-level uncertainty propagation using the Total Monte Carlo method.…

Nuclear Theory · Physics 2019-05-29 E. Alhassan , D. Rochman , H. Sjöstrand , A. Vasiliev , A. J. Koning , H. Ferroukhi

Context: Recent reaction rate evaluations include reaction rate uncertainties that have been determined in a statistically meaningful manner. Furthermore, reaction rate probability density distributions have been determined and published in…

Instrumentation and Methods for Astrophysics · Physics 2015-06-11 Richard Longland

We present a technique for estimating the number of future neutrinoless double-beta decay results using several distinct nuclei to optimize the physics reach of upcoming experiments. We use presently available matrix element calculations…

High Energy Physics - Phenomenology · Physics 2008-11-26 V. M. Gehman , S. R. Elliott

The propagation of uncertainties in reaction cross sections and rates of neutron-, proton-, and $\alpha$-induced reactions into the final isotopic abundances obtained in nucleosynthesis models is an important issue in studies of…

High Energy Astrophysical Phenomena · Physics 2020-12-24 T. Rauscher

This study developed a new statistical model and method for analyzing the precision of binary measurement methods from collaborative studies. The model is based on beta-binomial distributions. In other words, it assumes that the sensitivity…

Applications · Statistics 2026-02-03 Jun-ichi Takeshita , Tomomichi Suzuki

Radioactivity is understood to be described by a Poisson process, yet some measurements of nuclear decays appear to exhibit unexpected variations. Generally, the isotopes reporting these variations have long half lives, which are plagued by…

The precision of double-beta ($\beta\beta$) decay experimental half-lives and their uncertainties is reevaluated. A complementary analysis of the decay uncertainties indicates deficiencies due to small size of statistical samples, and…

Nuclear Experiment · Physics 2015-05-22 B. Pritychenko

Worldwide several radiation sources contribute to the delivered dose to the human population. This radiation also acts as natural background when detecting radiation, for instance from radioactive sources. In this work a medium-size plastic…

Instrumentation and Detectors · Physics 2017-09-20 Luis Peralta

We investigate the Monte Carlo approach to propagation of experimental uncertainties within the context of the established "MSTW 2008" global analysis of parton distribution functions (PDFs) of the proton at next-to-leading order in the…

High Energy Physics - Phenomenology · Physics 2012-08-13 G. Watt , R. S. Thorne

The high precision and conformity of intensity-modulated particle therapy (IMPT) comes at the cost of susceptibility to treatment uncertainties in particle range and patient set-up. Dose uncertainty quantification and mitigation, which is…

Medical Physics · Physics 2021-11-17 Pia Stammer , Lucas Burigo , Oliver Jäkel , Martin Frank , Niklas Wahl

There is wide interest in studying how the distribution of a continuous response changes with a predictor. We are motivated by environmental applications in which the predictor is the dose of an exposure and the response is a health…

Methodology · Statistics 2018-05-10 Antonio Canale , Daniele Durante , David Dunson

The next great leap toward improving treatment of cancer with radiation will require the combined use of online adaptive and magnetic resonance guided radiation therapy techniques with automatic X-ray beam orientation selection.…

Medical Physics · Physics 2019-08-14 Ryan Neph , Yangsibo Huang , Youming Yang , Ke Sheng

We apply random matrix theory to study the impact of measurement uncertainty on dynamic mode decomposition. Specifically, when the measurements follow a normal probability density function, we show how the moments of that density propagate…

Methodology · Statistics 2025-09-04 P. Algikar , P. Sharma , M. Netto , L. Mili

Multiple high precision $\beta$-decay measurements are being carried out these days on various nuclei, in search of beyond the Standard Model signatures. These measurements necessitate accurate standard model theoretical predictions to be…

Nuclear Theory · Physics 2022-09-28 Ayala Glick-Magid , Doron Gazit

We propose the BayesDose-Framework, a Bayesian approach for fast and accurate dose prediction in proton therapy. Our framework is based on a previously published deterministic LSTM model and is trained and evaluated on simulated beamlet…

Medical Physics · Physics 2023-07-04 Luke Voss , Ahmad Neishabouri , Tim Ortkamp , Andrea Mairani , Niklas Wahl

This study introduces an approach to estimate the uncertainty in bibliometric indicator values that is caused by data errors. This approach utilizes Bayesian regression models, estimated from empirical data samples, which are used to…

Digital Libraries · Computer Science 2024-12-11 Paul Donner
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