arXiv subjects

Winfried Mitaroff

Publications and source records attributed to Winfried Mitaroff.

5 recordsLinked to original sources

arXiv2023

Statistical analysis of event classification in experimental data

The paper addresses general aspects of experimental data analysis, dealing with the separation of ``signal vs. background''. It consists of two parts. Part I is a tutorial on statistical event classification, Bayesian inference, and test optimization. Aspects of the base data sample if being created by Poisson processes are discussed, and a method for estimating the unknown numbers of signal and background events is presented. Data quality of the selected events sample is assessed by the expected purity and background contamination. Part II contains a rigorous statistical analysis of the methods discussed in Part I. Both Bayesian and frequentist estimators of the unknown signal/background content are investigated. The estimates and their stochastic uncertainties are calculated for various conjugate priors in the Bayesian case, and for three choices of the virtual parent population in the frequentist case.

physics.data-an↗

arXiv2021

A new discrete distribution arising from a generalised random game and its asymptotic properties

The rules of a game of dice are extended to a "hyper-die" with $n\in\mathbb{N}$ equally probable faces, numbered from 1 to $n$. We derive recursive and explicit expressions for the probability mass function and the cumulative distribution function of the gain $G_n$ for arbitrary values of $n$. A numerical study suggests the conjecture that for $n \to \infty$ the expectation of the scaled gain $\mathbb{E}[H_n]=\mathbb{E}[G_n/\sqrt{n}\,]$ converges to $\sqrt{π/\,2}$. The conjecture is proved by deriving an analytic expression of the expected gain $\mathbb{E}[G_n]$. An analytic expression of the variance of the gain $G_n$ is derived by a similar technique. Finally, it is proved that $H_n$ converges weakly to the Rayleigh distribution with scale parameter~1.

math.PR↗

arXiv2012

Forward Tracking in the ILD Detector

The reconstruction software for ILD is currently subject to a major revision, aiming at improving its accuracy, speed, efficiency and maintainability in time for the upcoming DBD Report. This requires replacing old code by novel methods for track search and fit, together with modern standards for interfaces and tools. Track reconstruction in the "forward region", defined by the silicon Forward Tracking Detector (FTD), relies heavily on a powerful stand-alone track search. The new software makes use of a Cellular Automaton, a Kalman filter, and a Hopfield Neural Network. We give an overview of the project, its methods and merits.

physics.data-an↗

arXiv2009

LiC Detector Toy - Tracking detector optimization with fast simulation and its application to the ILD design

The "LiC Detector Toy" is a fast single-track simulation and reconstruction tool, aiming at the optimization of tracking detector design, i.e. geometric layout and material budgets. Its implementation is based on the MATLAB system. Improvements over the last year include correct handling of complex forward regions (arbitrary mixture of cylindrical and plane surfaces), enhanced detector description, flexible data presentation of results (e.g. fitted track resolutions and impact parameters), and support by an integrated GUI. In addition a non-GUI version running under open-source OCTAVE has been implemented. The tool has recently been used for fixing the "reference design" layout of the silicon tracker (SIT, SET and FTD) of the ILD detector concept.

physics.ins-det↗

arXiv2009

Implementation and application of kinematic vertex fitting in the software environment of ILD

The vertex reconstruction toolkit RAVE has been extended by an option for the inclusion of kinematic constraints, and embedded into the ILD analysis framework Marlin. The new tools have been tested with an exemplary reconstruction of WW and ZZ decays. The presented results show the improvements achieved in precision of the fitted masses, and demonstrate the usage and functionality of the toolkit.

physics.data-an↗