1 citations · 1 across the 2 of their papers we have counts for
5 papers
kafe2 -- a Modern Tool for Model Fitting in Physics Lab Courses
Johannes Gäßler, Günter Quast, Daniel Savoiu +1
Fitting models to measured data is one of the standard tasks in the natural sciences, typically addressed early on in physics education in the context of laboratory courses, in whi…
Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters
Stefan Wunsch, Simon Jörger, Roger Wolf +1
Data analysis in science, e.g., high-energy particle physics, is often subject to an intractable likelihood if the observables and observations span a high-dimensional input space.…
Reducing the dependence of the neural network function to systematic uncertainties in the input space
Stefan Wunsch, Simon Jörger, Roger Wolf +1
Applications of neural networks to data analyses in natural sciences are complicated by the fact that many inputs are subject to systematic uncertainties. To control the dependence…
Dynamic Virtualized Deployment of Particle Physics Environments on a High Performance Computing Cluster
Felix Bührer, Frank Fischer, Georg Fleig +11
The NEMO High Performance Computing Cluster at the University of Freiburg has been made available to researchers of the ATLAS and CMS experiments. Users access the cluster from ext…
Identifying the relevant dependencies of the neural network response on characteristics of the input space
Stefan Wunsch, Raphael Friese, Roger Wolf +1
The relation between the input and output spaces of neural networks (NNs) is investigated to identify those characteristics of the input space that have a large influence on the ou…