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Stochastic differential equations describe well many physical, biological and sociological systems, despite the simplification often made in their derivation. Here the usage of simple stochastic differential equations to characterize and classify complex dynamical systems is proposed within a Bayesian framework. To this end, we develop a dynamic system classifier (DSC). The DSC first abstracts training data of a system in terms of time dependent coefficients of the descriptive stochastic differential equation. Thereby the DSC identifies unique correlation structures within the training data. For definiteness we restrict the presentation of DSC to oscillation processes with a time dependent frequency {\omega}(t) and damping factor {\gamma}(t). Although real systems might be more complex, this simple oscillator captures many characteristic features. The {\omega} and {\gamma} timelines represent the abstract system characterization and permit the construction of efficient signal classifiers. Numerical experiments show that such classifiers perform well even in the low signal-to-noise regime., Comment: 11 pages, 8 figures

We present a new algorithm to reconstruct the Galactic free electron density from pulsar dispersion measures. The algorithm performs a nonparametric tomography for a density field with an arbitrary amount of degrees of freedom. It is based on approximating the Galactic free electron density as the product of a profile function with a statistically isotropic and homogeneous log-normal field. Under this approximation the algorithm generates a map of the free electron density as well as an uncertainty estimate without the need of information about the power spectrum. The uncertainties of the pulsar distances are treated consistently by an iterative procedure. We test the algorithm using the NE2001 model with modified fluctuations as a Galaxy model, pulsar populations generated from the Lorimer population model, and mock observations emulating the upcoming Square Kilometer Array. We show the quality of the reconstruction for mock data sets containing between 1000 and 10000 pulsars with distance uncertainties up to 25%. Our results show, that with the SKA nonparametric tomography of the Galactic free electron density becomes feasible, but the quality of the reconstruction is very sensitive to the distance uncertainties., Comment: 18 pages, 16 figures

The kinematic dynamo (KD) describes the growth of magnetic fields generated by the flow of a conducting medium in the limit of vanishing backaction of the fields onto the flow. The KD is therefore an important model system for understanding astrophysical magnetism. Here, the mathematical correspondence between the KD and a specific stochastic differential equation (SDE) viewed from the perspective of the supersymmetric theory of stochastics (STS) is discussed. The STS is a novel, approximation-free framework to investigate SDEs. The correspondence reported here permits insights from the STS to be applied to the theory of KD and vice versa. It was previously known that the fast KD in the idealistic limit of no magnetic diffusion requires chaotic flows. The KD-STS correspondence shows that this is also true for the diffusive KD. From the STS perspective, the KD possesses a topological supersymmetry and the dynamo effect can be viewed as its spontaneous breakdown. This supersymmetry breaking can be regarded as the stochastic generalization of the concept of dynamical chaos. As this supersymmetry breaking happens in both the diffusive and the non-diffusive case, the necessity of the underlying SDE being chaotic is given in either case. The observed exponentially growing and oscillating KD modes prove physically that dynamical spectra of the STS evolution operator that break the topological supersymmetry exist with both, real and complex ground state eigenvalues. Finally, we comment on the non-existence of dynamos for scalar quantities., Comment: 10 pages, 1 figure, submitted

Predefined spatial templates to describe the background of $\gamma$-ray emission from astrophysical processes, like cosmic ray interactions, are used in previous searches for the $\gamma$-ray signatures of annihilating galactic dark matter. In this proceeding, we investigate the GeV excess in the inner Galaxy using an alternative approach, in which the astrophysical components are identified solely by their spectral and morphological properties. We confirm the reported GeV excess and derive related parameters for dark matter interpretation, which are consistent with previous results. We investigate the morphology of this spectral excess as preferred by the data only. This emission component exhibits a central Galaxy cusp as expected for a dark matter annihilation signal. However, Galactic disk regions with a morphology of that of the hot interstellar medium also host such a spectral component. This points to a possible astrophysical origin of the excess and requests a more detailed understanding of astrophysical $\gamma$-ray emitting processes in the galactic center region before definite claims about a dark matter annihilation signal can be made., Comment: 5 pages, 4 figures. Prepared for the proceedings of the TAUP Conference 2015, Turin

Previous searches for the $\gamma$-ray signatures of annihilating galactic dark matter used predefined spatial templates to describe the background of $\gamma$-ray emission from astrophysical processes like cosmic ray interactions. In this work, we aim to establish an alternative approach, in which the astrophysical components are identified solely by their spectral and morphological properties. To this end, we adopt the recent reconstruction of the diffuse $\gamma$-ray sky from Fermi data by the D$^{3}$PO algorithm and the fact that more than 90\% of its flux can be represented by only two spectral components, resulting form the dense and dilute interstellar medium. Under these presumptions, we confirm the reported DM annihilation-like signal in the inner Galaxy and derive upper limits for dark matter annihilation cross sections. We investigate whether the DM signal could be a residual of the simplified modeling of astrophysical emission by inspecting the morphology of the regions, which favor a dark matter component. The central galactic region favors strongest for such a component with the expected spherically symmetric and radially declining profile. However, astrophysical structures, in particular sky regions which seem to host most of the dilute interstellar medium, obviously would benefit from a DM annihilation-like component as well. Although these regions do not drive the fit, they warn that a more detailed understanding of astrophysical $\gamma$-ray emitting processes in the galactic center region are necessary before definite claims about a DM annihilation signal can be made. The regions off the Galactic plane actually disfavor the best fit DM annihilation cross section from the inner Galactic region unless the radial decline of the Galactic DM density profile in the outer regions is significantly steeper than that usually assumed., Comment: 17 pages, 11 figures, matches version published in JCAP

We know that magnetic fields are pervasive across all scales in the Universe and over all of cosmic time and yet our understanding of many of the properties of magnetic fields is still limited. We do not yet know when, where or how the first magnetic fields in the Universe were formed, nor do we fully understand their role in fundamental processes such as galaxy formation or cosmic ray acceleration or how they influence the evolution of astrophysical objects. The greatest challenge to addressing these issues has been a lack of deep, broad bandwidth polarimetric data over large areas of the sky. The Square Kilometre Array will radically improve this situation via an all-sky polarisation survey that delivers both high quality polarisation imaging in combination with observations of 7-14 million extragalactic rotation measures. Here we summarise how this survey will improve our understanding of a range of astrophysical phenomena on scales from individual Galactic objects to the cosmic web., Comment: 18 pages, 5 figures. To appear in Proceedings of Advancing Astrophysics with the Square Kilometre Array (AASKA14). 9 -13 June, 2014. Giardini Naxos, Italy. PoS(AASKA14)092

Matrix determinants play an important role in data analysis, in particular when Gaussian processes are involved. Due to currently exploding data volumes, linear operations - matrices - acting on the data are often not accessible directly but are only represented indirectly in form of a computer routine. Such a routine implements the transformation a data vector undergoes under matrix multiplication. While efficient probing routines to estimate a matrix's diagonal or trace, based solely on such computationally affordable matrix-vector multiplications, are well known and frequently used in signal inference, there is no stochastic estimate for its determinant. We introduce a probing method for the logarithm of a determinant of a linear operator. Our method rests upon a reformulation of the log-determinant by an integral representation and the transformation of the involved terms into stochastic expressions. This stochastic determinant determination enables large-size applications in Bayesian inference, in particular evidence calculations, model comparison, and posterior determination., Comment: 8 pages, 5 figures

To better understand the origin and properties of cosmological magnetic fields, a detailed knowledge of magnetic fields in the large-scale structure of the Universe (galaxy clusters, filaments) is crucial. We propose a new statistical approach to study magnetic fields on large scales with the rotation measure grid data that will be obtained with the new generation of radio interferometers., Comment: 9 pages; to appear as part of 'Cosmic Magnetism' in Proceedings 'Advancing Astrophysics with the SKA (AASKA14)', PoS(AASKA14)114

An essential quantity required to understand the physics of the early Universe, in particular the inflationary epoch, is the primordial scalar potential $\Phi$ and its statistics. We present for the first time an all-sky reconstruction of $\Phi$ with corresponding $1\sigma$-uncertainty from WMAP's cosmic microwave background (CMB) temperature data -- a map of the very early Universe right after the inflationary epoch. This has been achieved by applying a Bayesian inference method that separates the whole inverse problem of the reconstruction into many independent ones, each of them solved by an optimal linear filter (Wiener filter). In this way, the three-dimensional potential $\Phi$ gets reconstructed slice by slice resulting in a thick shell of nested spheres around the comoving distance to the last scattering surface. Each slice represents the primordial scalar potential $\Phi$ projected onto a sphere with corresponding distance. Furthermore, we present an advanced method for inferring $\Phi$ and its power spectrum simultaneously from data, but argue that applying it requires polarization data with high signal-to-noise levels not available yet. Future CMB data should improve results significantly, as polarization data will fill the present $\ell-$blind gaps of the reconstruction.

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. The idea of CURE, developed in the framework of information field theory, is starting with an assumed calibration to successively include more and more portions of calibration uncertainty into the signal inference equations and to absorb the resulting corrections into renormalized signal (and calibration) solutions. Thereby, the signal inference and calibration problem turns into solving a single system of ordinary differential equations and can be identified with common resummation techniques used in field theories. We verify CURE by applying it to a simplistic toy example and compare it against existent self-calibration schemes, Wiener filter solutions, and Markov Chain Monte Carlo sampling. We conclude that the method is able to keep up in accuracy with the best self-calibration methods and serves as a non-iterative alternative to it.

We analyze the 6.5yr all-sky data from the Fermi LAT restricted to gamma-ray photons with energies between 0.6-307.2GeV. Raw count maps show a superposition of diffuse and point-like emission structures and are subject to shot noise and instrumental artifacts. Using the D3PO inference algorithm, we model 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. D3PO performs a Bayesian inference in this setting without the use of spatial or spectral templates;i.e., it removes the shot noise, deconvolves the instrumental response, and yields estimates for the two flux components separately. 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 gamma-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 2nd(3rd) Fermi catalog. We observe gamma-ray emission in the direction of a few galaxy clusters hosting radio halos., Comment: re-submission after referee report (A&A); 17 pages, many colorful figures, 4 tables; bug fixed, flux scale now consistent with Fermi, even lower residual level, pDF -> 1DF source catalog, tentative detection of a few clusters of galaxies, online material http://www.mpa-garching.mpg.de/ift/fermi/

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 additionally make use of a Gaussian prior for the CMB whose correlation structure is described by an unknown angular power spectrum. We argue for the natural extension of this by using non-trivial priors also for the foreground components. Focusing on diffuse Galactic foregrounds, we propose a log-normal model including unknown spatial correlations within each component and cross-correlations between the different foreground components. We present case studies at low resolution that demonstrate the superior performance of this model when compared to an analysis with flat priors for all components., Comment: 3 pages, 1 figure; proceedings of IAU symposium 306, Statistical challenges in 21st century cosmology

The presence of multiple fields during inflation might seed a detectable amount of non-Gaussianity in the curvature perturbations, which in turn becomes observable in present data sets like the cosmic microwave background (CMB) or the large scale structure (LSS). Within this proceeding we present a fully analytic method to infer inflationary parameters from observations by exploiting higher-order statistics of the curvature perturbations. To keep this analyticity, and thereby to dispense with numerically expensive sampling techniques, a saddle-point approximation is introduced whose precision has been validated for a numerical toy example. Applied to real data, this approach might enable to discriminate among the still viable models of inflation., Comment: Proceedings IAU Symposium No. 306

Non-parametric imaging and data analysis in astrophysics and cosmology can be addressed by information field theory (IFT), 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 algorithms. It exploits spatial correlations of the signal fields even for nonlinear and non-Gaussian signal inference problems. The alleviation of a perception threshold for recovering signals of unknown correlation structure by using IFT will be discussed in particular as well as a novel improvement on instrumental self-calibration schemes. IFT can be applied to many areas. Here, applications in in cosmology (cosmic microwave background, large-scale structure) and astrophysics (galactic magnetism, radio interferometry) are presented., Comment: 4 pages, 2 figures, accepted chapter to the conference proceedings for MaxEnt 2013, to be published by AIP

(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 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. We vary our assumptions about the data, showing 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., Comment: 20 + 6 pages, 19 figures; minor changes after bug-fix; version accepted for publication by A&A; results are available at http://www.mpa-garching.mpg.de/ift/faraday/

We present a generic inference method for inflation models from observational data by the usage of higher-order statistics of the curvature perturbation on uniform density hypersurfaces. This method is based on the calculation of the posterior for the primordial non-Gaussianity parameters $f_\text{NL}$ and $g_\text{NL}$, which in general depend on specific parameters of inflation and reheating models, and enables to discriminate among the still viable inflation models. To keep analyticity as far as possible to dispense with numerically expensive sampling techniques a saddle-point approximation is introduced, whose precision is validated for a numerical toy example. The mathematical formulation is done in a generic way so that the approach remains applicable to cosmic microwave background data as well as to large scale structure data. Additionally, we review a few currently interesting inflation models and present numerical toy examples thereof in two and three dimensions to demonstrate the efficiency of the higher-order statistics method. A second quantity of interest is the primordial power spectrum. Here, we present two Bayesian methods to infer it from observational data, the so called critical filter and an extension thereof with smoothness prior, both allowing for a non-parametric spectrum reconstruction. These methods are able to reconstruct the spectra of the observed perturbations and the primordial ones of curvature perturbation even in case of non-Gaussianity and partial sky coverage. We argue that observables like $T-$ and $B-$modes permit to measure both spectra. This also allows to infer the level of non-Gaussianity generated since inflation., Comment: version 2: few new references added, minor changes

Response calibration is the process of inferring how much the measured data depend on the signal one is interested in. It is essential for any quantitative signal estimation on the basis of the data. Here, we investigate self-calibration methods for linear signal measurements and linear dependence of the response on the calibration parameters. The common practice is to augment an external calibration solution using a known reference signal with an internal calibration on the unknown measurement signal itself. Contemporary self-calibration schemes try to find a self-consistent solution for signal and calibration by exploiting redundancies in the measurements. This can be understood in terms of maximizing the joint probability of signal and calibration. However, the full uncertainty structure of this joint probability around its maximum is thereby not taken into account by these schemes. Therefore better schemes -- in sense of minimal square error -- can be designed by accounting for asymmetries in the uncertainty of signal and calibration. We argue that at least a systematic correction of the common self-calibration scheme should be applied in many measurement situations in order to properly treat uncertainties of the signal on which one calibrates. Otherwise the calibration solutions suffer from a systematic bias, which consequently distorts the signal reconstruction. Furthermore, we argue that non-parametric, signal-to-noise filtered calibration should provide more accurate reconstructions than the common bin averages and provide a new, improved self-calibration scheme. We illustrate our findings with a simplistic numerical example., Comment: 17 pages, 3 figures, revised version, title changed

We investigate whether non-linear effects on the large-scale power spectrum of dark matter, namely the increase in small-scale power and the smearing of baryon acoustic oscillations, can be decreased by a log-transformation or emulated by an exponential transformation of the linear spectrum. To that end we present a formalism to convert the power spectrum of a log-normal field to the power spectrum of the logarithmic Gaussian field and vice versa. All ingredients of our derivation can already be found in various publications in cosmology and other fields. We follow a more pedagogical approach providing a detailed derivation, application examples, and a discussion of implementation subtleties in one text. We use the formalism to show that the non-linear increase in small-scale power in the matter power spectrum is significantly smaller for the log-transformed spectrum which fits the linear spectrum (with less than 20% error) for redshifts down to 1 and $k\leq1.0\,h\,\mathrm{Mpc}$. For lower redshifts the fit to the linear spectrum is not as good, but the reduction of non-linear effects is still significant. Similarly, we show that applying the linear growth factor to the logarithmic density leads to an automatic increase in small-scale power for low redshifts fitting to third-order perturbation spectra and Cosmic Emulator spectra with an error of less than $20%$. Smearing of baryon acoustic oscillations is at least three times weaker, but still present., Comment: 12 pages, 9 figures

The analysis of astronomical images is a non-trivial task. The D3PO 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 order to discriminate between these morphologically different signal components, a probabilistic algorithm is derived in the language of information field theory based on a hierarchical Bayesian parameter model. The signal inference exploits prior information on the spatial correlation structure of the diffuse component and the brightness distribution of the spatially uncorrelated point-like sources. A maximum a posteriori solution and a solution minimizing the Gibbs free energy of the inference problem using variational Bayesian methods are discussed. Since the derivation of the solution is not dependent on the underlying position space, the implementation of the D3PO algorithm uses the NIFTY package to ensure applicability to various spatial grids and at any resolution. The fidelity of the algorithm is validated by the analysis of simulated data, including a realistic high energy photon count image showing a 32 x 32 arcmin^2 observation with a spatial resolution of 0.1 arcmin. In all tests the D3PO algorithm successfully denoised, deconvolved, and decomposed the data into a diffuse and a point-like signal estimate for the respective photon flux components., Comment: 22 pages, 8 figures, 2 tables, accepted by Astronomy & Astrophysics; refereed version, 1 figure added, results unchanged, software available at http://www.mpa-garching.mpg.de/ift/d3po/

PRISM (Polarized Radiation Imaging and Spectroscopy Mission) was proposed to ESA in May 2013 as a large-class mission for investigating within the framework of the ESA Cosmic Vision program a set of important scientific questions that require high resolution, high sensitivity, full-sky observations of the sky emission at wavelengths ranging from millimeter-wave to the far-infrared. PRISM's main objective is to explore the distant universe, probing cosmic history from very early times until now as well as the structures, distribution of matter, and velocity flows throughout our Hubble volume. PRISM will survey the full sky in a large number of frequency bands in both intensity and polarization and will measure the absolute spectrum of sky emission more than three orders of magnitude better than COBE FIRAS. The aim of this Extended White Paper is to provide a more detailed overview of the highlights of the new science that will be made possible by PRISM, Comment: Figure 8 replaced by the correct version, affiliations of authors added, and minor changes to match the published JCAP version

We present an approximate calculation of the full Bayesian posterior probability distribution for the local non-Gaussianity parameter $f_{\text{nl}}$ from observations of cosmic microwave background anisotropies within the framework of information field theory. The approximation that we introduce allows us to dispense with numerically expensive sampling techniques. We use a novel posterior validation method (DIP test) in cosmology to test the precision of our method. It transfers inaccuracies of the calculated posterior into deviations from a uniform distribution for a specially constructed test quantity. For this procedure we study toy cases that use one- and two-dimensional flat skies, as well as the full spherical sky. We find that we are able to calculate the posterior precisely under a flat-sky approximation, albeit not in the spherical case. We argue that this is most likely due to an insufficient precision of the used numerical implementation of the spherical harmonic transform, which might affect other non-Gaussianity estimators as well. Furthermore, we present how a nonlinear reconstruction of the primordial gravitational potential on the full spherical sky can be obtained in principle. Using the flat-sky approximation, we find deviations for the posterior of $f_{\text{nl}}$ from a Gaussian shape that become more significant for larger values of the underlying true $f_{\text{nl}}$. We also perform a comparison to the well-known estimator of Komatsu et al. [Astrophys. J. 634, 14 (2005)] and finally derive the posterior for the local non-Gaussianity parameter $g_{\text{nl}}$ as an example of how to extend the introduced formalism to higher orders of non-Gaussianity.

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 correct posterior into characteristic deviations from a uniform distribution of a quantity constructed for this purpose. We show that this method is able to reveal and discriminate several kinds of numerical and approximation errors, as well as their impact on the posterior distribution. For this we present four typical analytical examples of posteriors with incorrect variance, skewness, position of the maximum, or normalization. We show further how this test can be applied to multidimensional signals.

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 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., Comment: 9 pages, 3 tables, 4 figures, accepted by Astronomy & Astrophysics; refereed version, 1 figure added, results unchanged

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 algorithms even for non-linear and non-Gaussian signal inference problems. IFT algorithms exploit spatial correlations of the signal fields and benefit from techniques developed to investigate quantum and statistical field theories, such as Feynman diagrams, re-normalisation calculations, and thermodynamic potentials. The theory can be used in many areas, and applications in cosmology and numerics are presented., Comment: 8 pages, in-a-nutshell introduction to information field theory (see http://www.mpa-garching.mpg.de/ift), accepted for the proceedings of MaxEnt 2012, the 32nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

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 signal's correlation structure to regularize the reconstruction. The correlation structure, described by the signal's 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 reconstruction's 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., Comment: 18 pages, 12 figures

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 models' dynamics over a large parameter space renders full-fledged stochastic simulations impractical, motivating approximation schemes. Here we propose an approximation scheme which improves upon the standard linear noise approximation while retaining similar computational complexity. The underlying idea is to minimize, at each time step, the Kullback-Leibler divergence between the true time evolved probability distribution and a Gaussian approximation (entropic matching). This condition leads to ordinary differential equations for the mean and the covariance matrix of the Gaussian. For cases of weak nonlinearity, the method is more accurate than the linear method when both are compared to stochastic simulations., Comment: 23 pages, 6 figures; significantly revised version

The experimental situation of Dark Matter Direct Detection has reached an exciting cross-roads, with potential hints of a discovery of Dark Matter (DM) from the CDMS, CoGeNT, CRESST-II and DAMA experiments in tension with null-results from xenon-based experiments such as XENON100 and LUX. Given the present controversial experimental status, it is important that the analytical method used to search for DM in Direct Detection experiments is both robust and flexible enough to deal with data for which the distinction between signal and background points is difficult, and hence where the choice between setting a limit or defining a discovery region is debatable. In this article we propose a novel (Bayesian) analytical method, which can be applied to all Direct Detection experiments and which extracts the maximum amount of information from the data. We apply our method to the XENON100 experiment data as a worked example, and show that firstly our exclusion limit at 90% confidence is in agreement with their own for the 225 Live Days data, but is several times stronger for the 100 Live Days data. Secondly we find that, due to the two points at low values of S1 and S2 in the 225 days data-set, our analysis points to either weak consistency with low-mass Dark Matter or the possible presence of an unknown background. Given the null-result from LUX, the latter scenario seems the more plausible., Comment: v3: Updated to match version accepted to PRD. Contains a discussion now also of the LUX experiment. 10 pages, 3 figures

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 sub-grid structure as many schemes do. IFD constructs an ensemble of non-parametric sub-grid field configurations from the combination of the data in computer memory, representing constraints on possible field configurations, and prior assumptions on the sub-grid field statistics. Each of these field configurations can formally be evolved to a later moment since any differential operator of the dynamics can act on fields living in continuous space. However, these virtually evolved fields need again a representation by data in computer memory. The maximum entropy principle of information theory guides the construction of updated datasets via entropic matching, optimally representing these field configurations at the later time. The field dynamics thereby become represented by a finite set of evolution equations for the data that can be solved numerically. The sub-grid dynamics is treated within an auxiliary analytic consideration and the resulting scheme acts solely on the data space. It should provide a more accurate description of the physical field dynamics than simulation schemes constructed ad-hoc, due to the more rigorous accounting of sub-grid physics and the space discretization process. Assimilation of measurement data into an IFD simulation is conceptually straightforward since measurement and simulation data can just be merged. The IFD approach is illustrated using the example of a coarsely discretized representation of a thermally excited classical Klein-Gordon field. This should pave the way towards the construction of schemes for more complex systems like turbulent hydrodynamics., Comment: 19 pages, 3 color figures, accepted by Phys. Rev. E

In 2011, the XENON100 experiment has set unprecedented constraints on dark matter-nucleon interactions, excluding dark matter candidates with masses down to 6 GeV if the corresponding cross section is larger than 10^{-39} cm^2. The dependence of the exclusion limit in terms of the scintillation efficiency (Leff) has been debated at length. To overcome possible criticisms XENON100 performed an analysis in which Leff was considered as a nuisance parameter and its uncertainties were profiled out by using a Gaussian likelihood in which the mean value corresponds to the best fit Leff value smoothly extrapolated to zero below 3 keVnr. Although such a method seems fairly robust, it does not account for more extreme types of extrapolation nor does it enable to anticipate on how much the exclusion limit would vary if new data were to support a flat behaviour for Leff below 3 keVnr, for example. Yet, such a question is crucial for light dark matter models which are close to the published XENON100 limit. To answer this issue, we use a maximum Likelihood ratio analysis, as done by the XENON100 collaboration, but do not consider Leff as a nuisance parameter. Instead, Leff is obtained directly from the fits to the data. This enables us to define frequentist confidence intervals by marginalising over Leff., Comment: 10 pages;, 9 figures; references added

We endorse the comment on our recent paper [En{\ss}lin and Weig, Phys. Rev. E 82, 051112 (2010)] by Iatsenko, Stefanovska and McClintock [Phys. Rev. E 85 033101 (2012)] and we try to clarify the origin of the apparent controversy on two issues. The aim of the minimal Gibbs free energy approach to provide a signal estimate is not affected by their Comment. However, if one wants to extend the method to also infer the a posteriori signal uncertainty any tempering of the posterior has to be undone at the end of the calculations, as they correctly point out. Furthermore, a distinction is made here between maximum entropy, the maximum entropy principle, and the so-called maximum entropy method in imaging, hopefully clarifying further the second issue of their Comment paper., Comment: 1 page, no figures, Reply to Comment paper

Measuring radio waves at low frequencies offers a new window to study cosmic magnetism, and LOFAR is the ideal radio telescope to open this window widely. The LOFAR Magnetism Key Science Project (MKSP) draws together expertise from multiple fields of magnetism science and intends to use LOFAR to tackle fundamental questions on cosmic magnetism by exploiting a variety of observational techniques. Surveys will provide diffuse emission from the Milky Way and from nearby galaxies, tracking the propagation of long-lived cosmic-ray electrons through magnetic field structures, to search for radio halos around spiral and dwarf galaxies and for magnetic fields in intergalactic space. Targeted deep-field observations of selected nearby galaxies and suspected intergalactic filaments allow sensitive mapping of weak magnetic fields through Rotation Measure (RM) grids. High-resolution observations of protostellar jets and giant radio galaxies reveal structures on small physical scales and at high redshifts, whilst pulsar RMs map large-scale magnetic structures of the Galactic disk and halo in revolutionary detail. The MKSP is responsible for the development of polarization calibration and processing, thus widening the scientific power of LOFAR., Comment: Proceedings of "Magnetic Fields in the Universe: From Laboratory and Stars to Primordial Structures", 2011 Aug. 21-27 in Zakopane/Poland, eds. M. Soida et al

Observations of giant radio halos provide unambiguous evidence for the existence of cosmic ray (CR) electrons and magnetic fields in galaxy clusters. The physical mechanism generating radio halos is still heavily debated. We critically discuss the proposed models for the radio halo emission and highlight the weaknesses underlying each explanation. We present an idea how the interplay of CR propagation and turbulent advection selects a bimodal spatial CR distribution that is characteristic for the dynamical state of a cluster. As a result, strongly turbulent, merging clusters should have a more centrally concentrated CR energy density profile with respect to relaxed ones with very subsonic turbulence. This translates into a bimodality of the expected diffuse radio and gamma ray emission of clusters. Thus, the observed bimodality of cluster radio halos appears to be a natural consequence of the interplay of CR transport processes, independent of the model of radio halo formation, be it hadronic interactions of CR protons or re-acceleration of low-energy CR electrons., Comment: 7 pages, 5 figures, proceedings of an invited talk at "Non-Thermal Phenomena in Colliding Galaxy Clusters", held in Nice, 15-18 November 2010. Published in Mem. S.A.It

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., Comment: 9 pages, 6 figures, accepted by Phys. Rev. E; introduction revised, results unchanged; page proofs implemented

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., Comment: 12 pages, 6 figures; 1D example added; accepted for publication in Phys. Rev. E

Abell 2256 is a rich, nearby (z=0.0594) galaxy cluster that has significant evidence of merger activity. We present new radio and X-ray observations of this system. The low-frequency radio images trace the diffuse synchrotron emission of the Mpc-scale radio halo and relics as well as a number of recently discovered, more compact, steep spectrum sources. The spectral index across the relics steepens from the north-west toward the south-east. Analysis of the spectral index gradients between low and and high-frequencies shows spectral differences away from the north-west relic edge such that the low-frequency index is significantly flatter than the high frequency spectral index near the cluster core. This trend would be consistent with an outgoing merger shock as the origin of the relic emission. New X-ray data from XMM-Newton reveal interesting structures in the intracluster medium pressure, entropy and temperature maps. The pressure maps show an overall low pressure core co-incident with the radio halo emission, while the temperature maps reveal multiple regions of cool emission within the central regions of Abell 2256. The two cold fronts in Abell 2256 both appear to have motion in similar directions., Comment: 4 pages, 3 figures, proceedings of contributed talk at "Non-Thermal Phenomena in Colliding Galaxy Clusters", held in Nice, 15-18 November 2010. To be published in Mem. S.A.It

We update our earlier calculations of gamma ray and radio observational constraints on annihilations of dark matter particles lighter than 10 GeV. We predict the synchrotron spectrum as well as the morphology of the radio emission associated with light decaying and annihilating dark matter candidates in both the Coma cluster and the Galactic Centre. Our new results basically confirm our previous findings: synchrotron emission in the very inner part of the Milky Way constrains or even excludes dark matter candidates if the magnetic field is larger than 50 micro Gauss. In fact, our results suggest that light annihilating candidates must have a S-wave suppressed pair annihilation cross section into electrons (or the branching ratio into electron positron must be small). If dark matter is decaying, it must have a life time that is larger than t = 3. 10^{25} s. Therefore, radio emission should always be considered when one proposes a "light" dark matter candidate., Comment: Revised version. Numerical factors and typos corrected. Results have changed but the conclusions remain unchanged. 12p; 12 Figures

We investigate the interplay of cosmic ray (CR) propagation and advection in galaxy clusters. Propagation in form of CR diffusion and streaming tends to drive the CR radial profiles towards being flat, with equal CR number density everywhere. Advection of CR by the turbulent gas motions tends to produce centrally enhanced profiles. We assume that the CR streaming velocity is of the order of the sound velocity. This is motivated by plasma physical arguments. The CR streaming is then usually larger than typical advection velocities and becomes comparable or lower than this only for periods with trans- and super-sonic cluster turbulence. As a consequence a bimodality of the CR spatial distribution results. Strongly turbulent, merging clusters should have a more centrally concentrated CR energy density profile with respect to relaxed ones with very subsonic turbulence. This translates into a bimodality of the expected diffuse radio and gamma-ray emission of clusters, since more centrally concentrated CR will find higher target densities for hadronic CR proton interactions, higher plasma wave energy densities for CR electron and proton re-acceleration, and stronger magnetic fields. Thus, the observed bimodality of cluster radio halos appears to be a natural consequence of the interplay of CR transport processes, independent of the model of radio halo formation, be it hadronic interactions of CR protons or re-acceleration of low-energy CR electrons. Energy dependence of the CR propagation should lead to spectral steepening of dying radio halos. Furthermore, we show that the interplay of CR diffusion with advection implies first order CR re-acceleration in the pressure-stratified atmospheres of galaxy clusters. Finally, we argue that CR streaming could be important in turbulent cool cores of galaxy clusters since it heats preferentially the central gas with highest cooling rate., Comment: 21 pages, 8 figures, 1 table, revised manuscript

We present status and results of AstroGrid-D, a joint effort of astrophysicists and computer scientists to employ grid technology for scientific applications. AstroGrid-D provides access to a network of distributed machines with a set of commands as well as software interfaces. It allows simple use of computer and storage facilities and to schedule or monitor compute tasks and data management. It is based on the Globus Toolkit middleware (GT4). Chapter 1 describes the context which led to the demand for advanced software solutions in Astrophysics, and we state the goals of the project. We then present characteristic astrophysical applications that have been implemented on AstroGrid-D in chapter 2. We describe simulations of different complexity, compute-intensive calculations running on multiple sites, and advanced applications for specific scientific purposes, such as a connection to robotic telescopes. We can show from these examples how grid execution improves e.g. the scientific workflow. Chapter 3 explains the software tools and services that we adapted or newly developed. Section 3.1 is focused on the administrative aspects of the infrastructure, to manage users and monitor activity. Section 3.2 characterises the central components of our architecture: The AstroGrid-D information service to collect and store metadata, a file management system, the data management system, and a job manager for automatic submission of compute tasks. We summarise the successfully established infrastructure in chapter 4, concluding with our future plans to establish AstroGrid-D as a platform of modern e-Astronomy., Comment: 14 pages, 12 figures Subjects: data analysis, image processing, robotic telescopes, simulations, grid. Accepted for publication in New Astronomy

Non-linear and non-Gaussian signal inference problems are difficult to tackle. Renormalization techniques permit us to construct good estimators for the posterior signal mean within information field theory (IFT), but the approximations and assumptions made are not very obvious. Here we introduce the simple concept of minimal Gibbs free energy to IFT, and show that previous renormalization results emerge naturally. They can be understood as being the Gaussian approximation to the full posterior probability, which has maximal cross information with it. We derive optimized estimators for three applications, to illustrate the usage of the framework: (i) reconstruction of a log-normal signal from Poissonian data with background counts and point spread function, as it is needed for gamma ray astronomy and for cosmography using photometric galaxy redshifts, (ii) inference of a Gaussian signal with unknown spectrum and (iii) inference of a Poissonian log-normal signal with unknown spectrum, the combination of (i) and (ii). Finally we explain how Gaussian knowledge states constructed by the minimal Gibbs free energy principle at different temperatures can be combined into a more accurate surrogate of the non-Gaussian posterior., Comment: 14 pages

We present a novel method to significantly speed up cosmological parameter sampling. The method relies on constructing an interpolation of the CMB-log-likelihood based on sparse grids, which is used as a shortcut for the likelihood-evaluation. We obtain excellent results over a large region in parameter space, comprising about 25 log-likelihoods around the peak, and we reproduce the one-dimensional projections of the likelihood almost perfectly. In speed and accuracy, our technique is competitive to existing approaches to accelerate parameter estimation based on polynomial interpolation or neural networks, while having some advantages over them. In our method, there is no danger of creating unphysical wiggles as it can be the case for polynomial fits of a high degree. Furthermore, we do not require a long training time as for neural networks, but the construction of the interpolation is determined by the time it takes to evaluate the likelihood at the sampling points, which can be parallelised to an arbitrary degree. Our approach is completely general, and it can adaptively exploit the properties of the underlying function. We can thus apply it to any problem where an accurate interpolation of a function is needed., Comment: Submitted to MNRAS, 13 pages, 13 figures

Reconstructing the matter density field from galaxy counts is a problem frequently addressed in current literature. Two main sources of error are shot noise from galaxy counts and insufficient knowledge of the correct galaxy position caused by peculiar velocities and redshift measurement uncertainty. Here we address the reconstruction problem of a Poissonian sampled log-normal density field with velocity distortions in a Bayesian way via a maximum a posteriory method. We test our algorithm on a 1D toy case and find significant improvement compared to simple data inversion. In particular, we address the following problems: photometric redshifts, mapping of extended sources in coded mask systems, real space reconstruction from redshift space galaxy distribution and combined analysis of data with different point spread functions., Comment: 19 pages, 10 figures, accepted

The optimal reconstruction of cosmic metric perturbations and other signals requires knowledge of their power spectra and other parameters. If these are not known a priori, they have to be measured simultaneously from the same data used for the signal reconstruction. We formulate the general problem of signal inference in the presence of unknown parameters within the framework of information field theory. We develop a generic parameter uncertainty renormalized estimation (PURE) technique and address the problem of reconstructing Gaussian signals with unknown power-spectrum with five different approaches: (i) separate maximum-a-posteriori power spectrum measurement and subsequent reconstruction, (ii) maximum-a-posteriori power reconstruction with marginalized power-spectrum, (iii) maximizing the joint posterior of signal and spectrum, (iv) guessing the spectrum from the variance in the Wiener filter map, and (v) renormalization flow analysis of the field theoretical problem providing the PURE filter. In all cases, the reconstruction can be described or approximated as Wiener filter operations with assumed signal spectra derived from the data according to the same recipe, but with differing coefficients. All of these filters, except the renormalized one, exhibit a perception threshold in case of a Jeffreys prior for the unknown spectrum. Data modes, with variance below this threshold do not affect the signal reconstruction at all. Filter (iv) seems to be similar to the so called Karhune-Loeve and Feldman-Kaiser-Peacock estimators for galaxy power spectra used in cosmology, which therefore should also exhibit a marginal perception threshold if correctly implemented. We present statistical performance tests and show that the PURE filter is superior to the others., Comment: 21 pages, 5 figures, accepted by PRD

We develop a novel maximum-a-posteriori method to measure magnetic power spectra from Faraday rotation data and implement it in the REALMAF code. A sophisticated model for the magnetic autocorrelation in real space permits us to alleviate previously required simplifying assumptions in the processing. We also introduce a way to treat the divergence relation of the magnetic field with a multiplicative factor in Fourier space, with which we can model the magnetic autocorrelation as a spherically symmetric function. Applied to the dataset of Hydra A north, we find a power law power spectrum on spatial scales between 0.3 kpc and 8 kpc, with no visible turnover at large scales within this range and a spectral index consistent with a Kolmogorov-like power law regime. The magnetic field strength profile seems to follow the electron density profile with an index alpha=1. A variation of alpha from 0.5 to 1.5 would lead to a spectral index between 1.55 and 2.05. The extrapolated magnetic field strength in the cluster centre highly depends on the assumed projection angle of the jet. For an angle of 45 degree we derive extrapolated 36 muG in the centre and directly probed 16 muG at 50 kpc radius., Comment: 13 pages, 13 figures, version accepted by A&A with restructured introduction and language improvements

We search for an unusual alignment of the preferred axes of the quadrupole and octopole, the so-called axis of evil, in the CMB temperature and polarization data from WMAP. We use the part of the polarization map which is uncorrelated with the temperature map as a statistically independent probe of the axis of evil, which helps to assess whether the latter has a cosmological origin or if is a mere chance fluctuation in the temperature. Note, though, that for certain models creating a preferred axis in the temperature map, we would not expect to see the axis in the uncorrelated polarization map. We find that the axis of the quadrupole of the uncorrelated polarization map roughly aligns with the axis of evil within our measurement precision, whereas the axis of the octopole does not. However, with our measurement uncertainty, the probability of such a scenario to happen by chance in an isotropic universe is of the order of 50 per cent. We also find that the so-called cold spot present in the CMB temperature map is even colder in the part of the temperature map which is uncorrelated with the polarization, although there is still a large uncertainty in the latter. Therefore, our analysis of the axis of evil and a future analysis of the cold spot in the uncorrelated temperature data will strongly benefit from the polarization data expected from the Planck satellite., Comment: Version accepted by MNRAS, added some comments, corrected typos

We present the largest Wiener reconstruction of the cosmic density field made to date. The reconstruction is based on the Sloan Digital Sky Survey data release 6 covering the northern Galactic cap. We use a novel supersampling algorithm to suppress aliasing effects and a Krylov-space inversion method to enable high performance with high resolution. These techniques are implemented in the ARGO computer code. We reconstruct the field over a 500 Mpc cube with Mpc grid-resolution while accounting both for the angular and radial selection functions of the SDSS, and the shot noise giving an effective resolution of the order of ~10 Mpc. In addition, we correct for the redshift distortions in the linear and nonlinear regimes in an approximate way. We show that the commonly used method of inverse weighting the galaxies by the corresponding selection function heads to excess noise in regions where the density of the observed galaxies is small. It is more accurate and conservative to adopt a Bayesian framework in which we model the galaxy selection/detection process to be Poisson-binomial. This results in heavier smoothing in regions of reduced sampling density. Our results show a complex cosmic web structure with huge void regions indicating that the recovered matter distribution is highly non-Gaussian. Filamentary structures are clearly visible on scales up to ~20 Mpc. We also calculate the statistical distribution of density after smoothing the reconstruction with Gaussian kernels of different radii r_S and find good agreement with a log-normal distribution for ~10 Mpc < r_S < ~30 Mpc., Comment: 24 pages, 12 figures, 2 tables