422 citations · 2.3k across the 35 of their papers we have counts for
12 papers · 1 filter
An efficient method for removing point sources from full-sky radio interferometric maps
Philippe Berger, Niels Oppermann, Ue-Li Pen +1
A new generation of wide-field radio interferometers designed for 21-cm surveys is being built as drift scan instruments allowing them to observe large fractions of the sky. With l…
Accurate Polarization Calibration at 800 MHz with the Green Bank Telescope
Yu-Wei Liao, Tzu-Ching Chang, Cheng-Yu Kuo +4
Polarization leakage of foreground synchrotron emission is a critical issue in HI intensity mapping experiments. While the sought-after HI emission is unpolarized, polarized foregr…
Holographic Beam Mapping of the CHIME Pathfinder Array
Philippe Berger, Laura B. Newburgh, Mandana Amiri +32
The Canadian Hydrogen Intensity Mapping Experiment (CHIME) Pathfinder radio telescope is currently surveying the northern hemisphere between 400 and 800 MHz. By mapping the large s…
Bayesian CMB foreground separation with a correlated log-normal model
Niels Oppermann, Torsten A. Enßlin
The extraction of foreground and CMB maps from multi-frequency observations relies mostly on the different frequency behavior of the different components. Existing Bayesian methods…
Estimating extragalactic Faraday rotation
Niels Oppermann, Henrik Junklewitz, Maksim Greiner +11
(abridged) Observations of Faraday rotation for extragalactic sources probe magnetic fields both inside and outside the Milky Way. Building on our earlier estimate of the Galactic…
Diagnostics for insufficiencies of posterior calculations in Bayesian signal inference
Sebastian Dorn, Niels Oppermann, Torsten A. Enßlin
We present an error-diagnostic validation method for posterior distributions in Bayesian signal inference, an advancement of a previous work. It transfers deviations from the corre…
NIFTY - Numerical Information Field Theory - a versatile Python library for signal inference
Marco Selig, Michael R. Bell, Henrik Junklewitz +5
NIFTY, "Numerical Information Field Theory", is a software package designed to enable the development of signal inference algorithms that operate regardless of the underlying spati…
Improved CLEAN reconstructions for rotation measure synthesis with maximum likelihood estimation
M. R. Bell, N. Oppermann, A. Crai +1
The CLEAN deconvolution algorithm has well-known limitations due to the restriction of locating point source model components on a discretized grid. In this letter we demonstrate t…
Reconstruction of Gaussian and log-normal fields with spectral smoothness
Niels Oppermann, Marco Selig, Michael R. Bell +1
We develop a method to infer log-normal random fields from measurement data affected by Gaussian noise. The log-normal model is well suited to describe strictly positive signals wi…
Improving stochastic estimates with inference methods: calculating matrix diagonals
Marco Selig, Niels Oppermann, Torsten A. Enßlin
Estimating the diagonal entries of a matrix, that is not directly accessible but only available as a linear operator in the form of a computer routine, is a common necessity in man…
Reconstructing signals from noisy data with unknown signal and noise covariance
Niels Oppermann, Georg Robbers, Torsten A. Ensslin
We derive a method to reconstruct Gaussian signals from linear measurements with Gaussian noise. This new algorithm is intended for applications in astrophysics and other sciences.…
Probing magnetic helicity with synchrotron radiation and Faraday rotation
N. Oppermann, H. Junklewitz, G. Robbers +1
We present a first application of the recently proposed LITMUS test for magnetic helicity, as well as a thorough study of its applicability under different circumstances. In order…