Search results for "Monte Carlo -menetelmät"
showing 10 items of 36 documents
Proton Direct Ionization Upsets at Tens of MeV
2023
Experimental monoenergetic proton single-event upset (SEU) cross sections of a 65-nm low core-voltage static random access memory (SRAM) were found to be exceptionally high not only at low energies ($ 3 MeV and extending up to tens of MeV. The SEU cross Section from 20-MeV protons exceeds the 200-MeV proton SEU cross Section by almost a factor of 3. Similarly, monoenergetic neutron cross sections at 14 MeV are about a factor of 3 lower than the 20-MeV proton cross section. Because of Monte Carlo (MC) simulations, it was determined that this strong enhancement is due to the proton direct ionization process as opposed to the elastic and inelastic scattering processes that dominate the SEU res…
Proton Direct Ionization in Sub-Micron Technologies: Numerical Method for RPP Parameter Extraction
2022
This work introduces a numerical method to iteratively extract parameters of a rectangular parallelepiped (RPP) sensitive volume (SV) from experimental proton direct ionization SEU data. The method combines two separate numerical models. The first model estimates the average LET values for energetic ions, including protons and also heavy ions, in elemental solid targets. The second model describes the statistical variance in the energy deposition events of projectile-induced primary ionization within a RPP shaped target volume. To benchmark the method, simulated cross-section values based on RPP parameters derived with this method are compared with literature data from four SRAM devices. Th…
Benchmark of a multi-physics Monte Carlo simulation of an ionguide for neutron-induced fission products
2022
AbstractTo enhance the production of medium-heavy, neutron-rich nuclei, and to facilitate measurements of independent yields of neutron-induced fission, a proton-to-neutron converter and a dedicated ion guide for neutron-induced fission have been developed for the IGISOL facility at the University of Jyväskylä. The ion guide holds the fissionable targets, and the fission products emerging from the targets are collected in helium gas and transported to the downstream experimental stations. A computer model, based on a combination of MCNPX for modeling the neutron production, the fission code GEF, and GEANT4 for the transport of the fission products, was developed. The model will be used to i…
Importance sampling correction versus standard averages of reversible MCMCs in terms of the asymptotic variance
2017
We establish an ordering criterion for the asymptotic variances of two consistent Markov chain Monte Carlo (MCMC) estimators: an importance sampling (IS) estimator, based on an approximate reversible chain and subsequent IS weighting, and a standard MCMC estimator, based on an exact reversible chain. Essentially, we relax the criterion of the Peskun type covariance ordering by considering two different invariant probabilities, and obtain, in place of a strict ordering of asymptotic variances, a bound of the asymptotic variance of IS by that of the direct MCMC. Simple examples show that IS can have arbitrarily better or worse asymptotic variance than Metropolis-Hastings and delayed-acceptanc…
bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R
2021
We present an R package bssm for Bayesian non-linear/non-Gaussian state space modelling. Unlike the existing packages, bssm allows for easy-to-use approximate inference based on Gaussian approximations such as the Laplace approximation and the extended Kalman filter. The package accommodates also discretely observed latent diffusion processes. The inference is based on fully automatic, adaptive Markov chain Monte Carlo (MCMC) on the hyperparameters, with optional importance sampling post-correction to eliminate any approximation bias. The package implements also a direct pseudo-marginal MCMC and a delayed acceptance pseudo-marginal MCMC using intermediate approximations. The package offers …
Cluster priors in the Bayesian modelling of fMRI data
2001
Diffusion in evolving environment : Monte Carlo studies of discrete models
2011
Bayesian semiparametric long memory models for discretized event data
2020
We introduce a new class of semiparametric latent variable models for long memory discretized event data. The proposed methodology is motivated by a study of bird vocalizations in the Amazon rain forest; the timings of vocalizations exhibit self-similarity and long range dependence. This rules out Poisson process based models where the rate function itself is not long range dependent. The proposed class of FRActional Probit (FRAP) models is based on thresholding, a latent process. This latent process is modeled by a smooth Gaussian process and a fractional Brownian motion by assuming an additive structure. We develop a Bayesian approach to inference using Markov chain Monte Carlo and show g…
On computation in statistical models with a psychophysical application
2004
FM Janne Kujalan tieteellisen laskennan väitöskirjan ”On computation in statistical models with a psychophysical application” (Laskennasta tilastollisissa malleissa psykofysiikkaan soveltaen) tarkastustilaisuus. Vastaväittäjänä FT Keijo Ruotsalainen (Oulun yliopisto) ja kustoksena professori Pekka Neittaanmäki.Kujala tehosti väitöskirjatutkimuksessaan tilastollisten ja fysikaalisten mallien laskentaa. Vaikka erinäisiin ongelmiin voidaan varsin helposti keksiä laskennallisia malleja, niiden käyttäminen ja vertailu on usein käytännössä mahdotonta tietokoneiden rajallisen tehon takia. Kujala etsi työssään vähemmän tehoa vaativia oikoteitä laskennallisten mallien käyttöön. Esimerkiksi kahden no…
New Capabilities of the FLUKA Multi-Purpose Code
2022
We would like to deeply thank the CERN Knowledge Transfer and Legal Service teams for their essential and extended support. Our appreciation also goes to the FLUKA.CERN Collaboration Board members for their strong commitment.