arXiv subjects

V. Dose

Publications and source records attributed to V. Dose.

5 recordsLinked to original sources

arXiv2009

Background-Source Separation in astronomical images with Bayesian probability theory (I): the method

A probabilistic technique for the joint estimation of background and sources with the aim of detecting faint and extended celestial objects is described. Bayesian probability theory is applied to gain insight into the coexistence of background and sources through a probabilistic two-component mixture model, which provides consistent uncertainties of background and sources. A multi-resolution analysis is used for revealing faint and extended objects in the frame of the Bayesian mixture model. All the revealed sources are parameterized automatically providing source position, net counts, morphological parameters and their errors.

astro-ph.IM↗

arXiv2002

Bayesian analysis of magnetic island dynamics

We examine a first order differential equation with respect to time coming up in the description of magnetic islands in magnetically confined plasmas. The free parameters of this equation are obtained by employing Bayesian probability theory. Additionally a typical Bayesian change point is solved in the process of obtaining the data.

physics.data-an↗

arXiv2002

Marginalization using the metric of the likelihood

Although the likelihood function is normalizeable with respect to the data there is no guarantee that the same holds with respect to the model parameters. This may lead to singularities in the expectation value integral of these parameters, especially if the prior information is not sufficient to take care of finite integral values. However, the problem may be solved by obeying the correct Riemannian metric imposed by the likelihood. This will be demonstrated for the example of the electron temperature evaluation in hydrogen plasmas.

physics.data-an↗

arXiv2002

Decomposition of multicomponent mass spectra using Bayesian probability theory

We present a method for the decomposition of mass spectra of mixture gases using Bayesian probability theory. The method works without any calibration measurement and therefore applies also to the analysis of spectra containing unstable species. For the example of mixtures of three different hydrocarbon gases the algorithm provides concentrations and cracking coefficients of each mixture component as well as their confidence intervals. The amount of information needed to obtain reliable results and its relation to the accuracy of our analysis are discussed.

physics.data-an↗

arXiv1998

Depth Profile Reconstruction from Rutherford Backscattering Data

An adaptive kernel method in the Bayesian framework together with a new simulation program for Rutherford backscattering spectroscopy (RBS) have been applied to the analysis of RBS data. Even in the case of strongly overlapping RBS peaks a depth profile reconstruction without noise fitting has been achieved. The adaptive kernel method leads to the simplest depth profile consistent with the data. Erosion and redeposition rates of carbon divertor plates in the fusion experiment ASDEX Upgrade could be determined by RBS-analysis of thin film probes before and after exposition to plasma discharges.

physics.data-an↗