Niels Oppermann

An improved map of the galactic Faraday sky

We aim to summarize the current state of knowledge regarding Galactic Faraday rotation in an all-sky map of the Galactic Faraday depth. For this we have assembled the most extensive catalog of Faraday rotation data of compact extragalactic polarized radio sources to date. In the map-making procedure we used a recently developed algorithm that reconstructs the map and the power spectrum of a statistically isotropic and homogeneous field while taking into account uncertainties in the noise statistics. This procedure is able to identify some rotation angles that are offset by an integer multiple of π. The resulting map can be seen as an improved version of earlier such maps and is made publicly available, along with a map of its uncertainty. For the angular power spectrum we find a power law behavior C� ∝ � −2.17 for a Faraday sky where an overall variance profile as a function of Galactic latitude has been removed, in agreement with earlier work. We show that this is in accordance with a 3D Fourier power spectrum P(k) ∝ k −2.17 of the underlying

Estimating extragalactic Faraday rotation

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 contribution, we set out to estimate the extragalactic contributions. We discuss the problems involved; in particular, we point out that taking the difference between the observed values and the Galactic foreground reconstruction is not a good estimate for the extragalactic contributions. We point out a degeneracy between the contributions to the observed values due to extragalactic magnetic fields and observational noise and comment on the dangers of over-interpreting an estimate without taking into account its uncertainty information. To overcome these difficulties, we develop an extended reconstruction algorithm based on the assumption that the observational uncertainties are accurately described for a subset of the data, which can overcome the degeneracy with the extragalactic contributions. We present a probabilistic derivation of the algorithm and demonstrate its performance using a simulation, yielding a high quality reconstruction of the Galactic Faraday rotation foreground, a precise estimate of the typical extragalactic contribution, and a well-defined probabilistic description of the extragalactic contribution for each data point. We then apply this reconstruction technique to a catalog of Faraday rotation observations for extragalactic sources. The analysis is done for several different scenarios, for which we consider the error bars of different subsets of the data to accurately describe the observational uncertainties. By comparing the results, we argue that a split that singles out only data near the Galactic poles is the most robust approach. We find that the dispersion of extragalactic contributions to observed Faraday depths is most likely lower than 7 rad/m(2), in agreement with earlier results, and that the extragalactic contribution to an individual data point is poorly constrained by the data in most cases.

FRB repetition and non-Poissonian statistics

We discuss some of the claims that have been made regarding the statistics of fast radio bursts (FRBs). In an earlier paper \citep{2015arXiv150505535C} we conjectured that flicker noise associated with FRB repetition could show up in non-cataclysmic neutron star emission models, like supergiant pulses. We show how the current limits of repetition would be significantly weakened if their repeat rate really were non-Poissonian and had a pink or red spectrum. Repetition and its statistics have implications for observing strategy, generally favouring shallow wide-field surveys, since in the non-repeating scenario survey depth is unimportant. We also discuss the statistics of the apparent latitudinal dependence of FRBs, and offer a simple method for calculating the significance of this effect. We provide a generalized Bayesian framework for addressing this problem, which allows for direct model comparison. It is shown how the evidence for a steep latitudinal gradient of the FRB rate is less strong than initially suggested and simple explanations like increased scattering and sky temperature in the plane are sufficient to decrease the low-latitude burst rate, given current data. The reported dearth of bursts near the plane is further complicated if FRBs have non-Poissonian repetition, since in that case the event rate inferred from observation depends on observing strategy.

NIFTY – Numerical Information Field Theory - A versatile PYTHON library for signal inference

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 eciency. NIFTy oers a toolkit that abstracts discretized representations of continuous spaces, fields in these spaces, and operators acting on fields into classes. Thereby, the correct normalization of operations on fields is taken care of automatically without concerning the user. This allows for an abstract formulation and programming of inference algorithms, including those derived within information field theory. Thus, NIFTy permits its user to rapidly prototype algorithms in 1D, and then apply the developed code in higher-dimensional settings of real world problems. The set of spaces on which NIFTy operates comprises point sets, n-dimensional regular grids, spherical spaces, their harmonic counterparts, and product spaces constructed as combinations of those. The functionality and diversity of the package is demonstrated by a Wiener filter code example that successfully runs without modification regardless of the space on which the inference problem is defined.

Improving stochastic estimates with inference methods: calculating matrix diagonals

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 many computational applications, especially in image reconstruction and statistical inference. Here, methods of statistical inference are used to improve the accuracy or the computational costs of matrix probing methods to estimate matrix diagonals. In particular, the generalized Wiener filter methodology, as developed within information field theory, is shown to significantly improve estimates based on only a few sampling probes, in cases in which some form of continuity of the solution can be assumed. The strength, length scale, and precise functional form of the exploited autocorrelation function of the matrix diagonal is determined from the probes themselves. The developed algorithm is successfully applied to mock and real world problems. These performance tests show that, in situations where a matrix diagonal has to be calculated from only a small number of computationally expensive probes, a speedup by a factor of 2 to 10 is possible with the proposed method.

Reconstruction of Gaussian and log-normal fields with spectral smoothness

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 with fluctuations whose amplitude varies over several orders of magnitude. We use the formalism of minimum Gibbs free energy to derive an algorithm that uses the signals correlation structure to regularize the reconstruction. The correlation structure, described by the signals power spectrum, is thereby reconstructed from the same data set. We show that the minimization of the Gibbs free energy, corresponding to a Gaussian approximation to the posterior marginalized over the power spectrum, is equivalent to the empirical Bayes ansatz, in which the power spectrum is fixed to its maximum a posteriori value. We further introduce a prior for the power spectrum that enforces spectral smoothness. The appropriateness of this prior in different scenarios is discussed and its effects on the reconstructions results are demonstrated. We validate the performance of our reconstruction algorithm in a series of one- and two-dimensional test cases with varying degrees of non-linearity and different noise levels.

Constraints on the FRB rate at 700–900 MHz

Estimating the all-sky rate of fast radio bursts (FRBs) has been dicult due to smallnumber statistics and the fact that they are seen by disparate surveys in dierent regions of the sky. In this paper we provide limits for the FRB rate at 800 MHz based on the only burst detected at frequencies below L-band, FRB 110523. We discuss the diculties in rate estimation, particularly in providing an all-sky rate above a single uence threshold. We nd an implied rate between 700-900 MHz that is consistent with the rate at 1.4 GHz, scaling to 8:9 +40:7 6:7 10 3 sky 1 day 1 for an HTRU-like

The denoised, deconvolved, and decomposed Fermi γ-ray sky - An application of the D3PO algorithm

We analyze the 6.5 year all-sky data from the Fermi Large Area Telescope that are restricted to γ -ray photons with energies between 0.6–307.2 GeV. Raw count maps show a superposition of diffuse and point-like emission structures and are subject to shot noise and instrumental artifacts. Using the D 3 PO inference algorithm, we modeled the observed photon counts as the sum of a diffuse and a point-like photon flux, convolved with the instrumental beam and subject to Poissonian shot noise. The D 3 PO algorithm performs a Bayesian inference without the use of spatial or spectral templates; that is, it removes the shot noise, deconvolves the instrumental response, and yields separate estimates for the two flux components. The non-parametric reconstruction uncovers the morphology of the diffuse photon flux up to several hundred GeV. We present an all-sky spectral index map for the diffuse component. We show that the diffuse γ -ray flux can be described phenomenologically by only two distinct components: a soft component, presumably dominated by hadronic processes, tracing the dense, cold interstellar medium, and a hard component, presumably dominated by leptonic interactions, following the hot and dilute medium and outflows such as the Fermi bubbles. A comparison of the soft component with the Galactic dust emission indicates that the dust-to-soft-gamma ratio in the interstellar medium decreases with latitude. The spectrally hard component exists in a thick Galactic disk and tends to flow out of the Galaxy at some locations. Furthermore, we find the angular power spectrum of the diffuse flux to roughly follow a power law with an index of 2.47 on large scales, independent of energy. Our first catalog of source candidates includes 3106 candidates of which we associate 1381 (1897) with known sources from the second (third) Fermi source catalog. We observe γ -ray emission in the direction of a few galaxy clusters hosting known radio halos.

The Euclidean distribution of fast radio bursts

We investigate whether current data on the distribution of observed flux densities of Fast Radio Bursts (FRBs) are consistent with a constant source density in Euclidean space. We use the number of FRBs detected in two surveys with different characteristics along with the observed signal-to-noise ratios of the detected FRBs in a formalism similar to a V/V_max-test to constrain the distribution of flux densities. We find consistency between the data and a Euclidean distribution. Any extension of this model is therefore not data-driven and needs to be motivated separately. As a byproduct we also obtain new improved limits for the FRB rate at 1.4 GHz, which had not been constrained in this way before.

Reconstructing signals from noisy data with unknown signal and noise covariance

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. The starting point of our considerations is the principle of minimum Gibbs free energy, which was previously used to derive a signal reconstruction algorithm handling uncertainties in the signal covariance. We extend this algorithm to simultaneously uncertain noise and signal covariances using the same principles in the derivation. The resulting equations are general enough to be applied in many different contexts. We demonstrate the performance of the algorithm by applying it to specific example situations and compare it to algorithms not allowing for uncertainties in the noise covariance. The results show that the method we suggest performs very well under a variety of circumstances and is indeed qualitatively superior to the other methods in cases where uncertainty in the noise covariance is present.