81 citations · 136 across the 13 of their papers we have counts for
13 papers
El Gordo needs El Anzuelo: Probing the structure of cluster members with multi-band extended arcs in JWST data
A. Galan, G. B. Caminha, J. Knollmüller +2
Gravitational lensing by galaxy clusters involves hundreds of galaxies over a large redshift range and increases the likelihood of rare phenomena (supernovae, dark substructures, e…
Progress in the Partial-Wave Analysis Methods at COMPASS
Florian Markus Kaspar, Julien Beckers, Jakob Knollmüller
We study the excitation spectrum of light and strange mesons in diffractive scattering. We identify different hadron resonances through partial wave analysis, which inherently reli…
First spatio-spectral Bayesian imaging of SN1006 in X-ray
Margret Westerkamp, Vincent Eberle, Matteo Guardiani +6
Supernovae are an important source of energy in the interstellar medium. Young remnants of supernovae have a peak emission in the X-ray region, making them interesting objects for…
Bias-Free Estimation of Signals on Top of Unknown Backgrounds
Johannes Diehl, Jakob Knollmüller, Oliver Schulz
We present a method for obtaining unbiased signal estimates in the presence of a significant unknown background, eliminating the need for a parametric model for the background itse…
Multi-Component Imaging of the Fermi Gamma-ray Sky in the Spatio-spectral Domain
Lukas I. Platz, Jakob Knollmüller, Philipp Arras +4
We perform two distinct spatio-spectral reconstructions of the gamma-ray sky in the range of 0.56-316 GeV based on Fermi Large Area Telescope (LAT) data. Both describe the sky brig…
Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoders
Philipp Joppich, Sebastian Dorn, Oliver De Candido +2
Parametric and non-parametric classifiers often have to deal with real-world data, where corruptions like noise, occlusions, and blur are unavoidable - posing significant challenge…
Variable structures in M87* from space, time and frequency resolved interferometry
Philipp Arras, Philipp Frank, Philipp Haim +4
Observing the dynamics of compact astrophysical objects provides insights into their inner workings, thereby probing physics under extreme conditions. The immediate vicinity of an…
Bayesian Reasoning with Trained Neural Networks
Jakob Knollmüller, Torsten Enßlin
We showed how to use trained neural networks to perform Bayesian reasoning in order to solve tasks outside their initial scope. Deep generative models provide prior knowledge, and…
Separating diffuse from point-like sources - a Bayesian approach
Jakob Knollmüller, Philipp Frank, Torsten A. Enßlin
We present the starblade algorithm, a method to separate superimposed point sources from auto-correlated, diffuse flux using a Bayesian model. Point sources are assumed to be indep…
Inference of signals with unknown correlation structure from nonlinear measurements
Jakob Knollmüller, Theo Steininger, Torsten A. Enßlin
We present a method to reconstruct autocorrelated signals together with their autocorrelation structure from nonlinear, noisy measurements for arbitrary monotonous nonlinear instru…
NIFTy 3 - Numerical Information Field Theory - A Python framework for multicomponent signal inference on HPC clusters
Theo Steininger, Jait Dixit, Philipp Frank +10
NIFTy, "Numerical Information Field Theory", is a software framework designed to ease the development and implementation of field inference algorithms. Field equations are formulat…
Noisy independent component analysis of auto-correlated components
Jakob Knollmüller, Torsten A. Enßlin
We present a new method for the separation of superimposed, independent, auto-correlated components from noisy multi-channel measurement. The presented method simultaneously recons…
Correlated signal inference by free energy exploration
Torsten A. Enßlin, Jakob Knollmüller
The inference of correlated signal fields with unknown correlation structures is of high scientific and technological relevance, but poses significant conceptual and numerical chal…