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We propose a new method to calculate Faraday rotation measure maps from multi-frequency polarisation angle data. In order to solve the so called nπ-ambiguity problem which arises from the observationally ambiguity of the polarisation angle which is only determined up to additions of ±nπ, where n is an integer, we suggest using a global scheme. Instead of… (More)

- Marco Selig, Michael R. Bell, +5 authors Torsten A. Enßlin
- ArXiv
- 2013

NIFTy, " Numerical Information Field Theory " , is a software package designed to enable the development of signal inference algorithms that operate regardless of the underlying spatial grid and its resolution. Its object-oriented framework is written in Python, although it accesses libraries written in Cython, C++, and C for efficiency. NIFTy offers a… (More)

- Torsten A. Enßlin
- ArXiv
- 2013

Non-linear image reconstruction and signal analysis deal with complex inverse problems. To tackle such problems in a systematic way, I present information field theory (IFT) as a means of Bayesian, data based inference on spatially distributed signal fields. IFT is a statistical field theory, which permits the construction of optimal signal recovery… (More)

- Tiago Ramalho, Marco Selig, Ulrich Gerland, Torsten A Ensslin
- Physical review. E, Statistical, nonlinear, and…
- 2013

The simulation of complex stochastic network dynamics arising, for instance, from models of coupled biomolecular processes remains computationally challenging. Often, the necessity to scan a model's dynamics over a large parameter space renders full-fledged stochastic simulations impractical, motivating approximation schemes. Here we propose an… (More)

- Torsten A. Enßlin
- Physical review. E, Statistical, nonlinear, and…
- 2013

Information field dynamics (IFD) is introduced here as a framework to derive numerical schemes for the simulation of physical and other fields without assuming a particular subgrid structure as many schemes do. IFD constructs an ensemble of nonparametric subgrid field configurations from the combination of the data in computer memory, representing… (More)

- Reimar H. Leike, Torsten A. Enßlin
- ArXiv
- 2016

In Bayesian statistics probability distributions express beliefs. However, for many problems the beliefs cannot be computed analytically and approximations of beliefs are needed. We seek a ranking function that quantifies how " embarrassing " it is to communicate a given approximation. We show that there is only one ranking under the requirements that (1)… (More)

- Torsten A. Enßlin, Jakob Knollmüller
- ArXiv
- 2016

The inference of correlated signal fields with unknown correlation structures is of high scientific and technological relevance, but poses significant conceptual and numerical challenges. To address these, we develop the correlated signal inference (CSI) algorithm within information field theory (IFT) and discuss its numerical implementation. To this end,… (More)

- Theo Steininger, Maksim Greiner, Frederik Beaujean, Torsten A. Enßlin
- Journal of Big Data
- 2016

We introduce d2o, a Python module for cluster-distributed multi-dimensional numerical arrays. It acts as a layer of abstraction between the algorithm code and the data-distribution logic. The main goal is to achieve usability without losing numerical performance and scalability. d2o’s global interface is similar to the one of a numpy.ndarray, whereas the… (More)

The analysis of astronomical images is a non-trivial task. The D 3 PO algorithm addresses the inference problem of denoising, deconvolving, and decomposing photon observations. Its primary goal is the simultaneous but individual reconstruction of the diffuse and point-like photon flux given a single photon count image, where the fluxes are superimposed. In… (More)

- Sebastian Dorn, Torsten A. Enßlin, Maksim Greiner, Marco Selig, Vanessa Böhm
- Physical review. E, Statistical, nonlinear, and…
- 2015

The calibration of a measurement device is crucial for every scientific experiment, where a signal has to be inferred from data. We present CURE, the calibration-uncertainty renormalized estimator, to reconstruct a signal and simultaneously the instrument's calibration from the same data without knowing the exact calibration, but its covariance structure.… (More)