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Torsten A. Enßlin

Publications and source records attributed to Torsten A. Enßlin.

At least 55 records · Page 3Linked to original sources

Jets, Bubbles, and Heat Pumps in Galaxy Clusters

Feedback from AGN jets has been proposed to counteract the catastrophic cooling in many galaxy clusters. However, it is still unclear which physical processes are acting to couple the energy from the bi-directional jets to the ICM. We study the long-term evolution of rising bubbles that were inflated by AGN jets using MHD simulations. In the wake of the rising bubbles, a significant amount of low-entropy gas is brought into contact with the hot cluster gas. We assess the energy budget of the uplifted gas and find it comparable to the total energy injected by the jets. Although our simulation does not include explicit thermal conduction, we find that, for reasonable assumptions about the conduction coefficient, the rate is fast enough that much of the uplifted gas may be thermalized before it sinks back to the core. Thus, we propose that the AGN can act like a heat pump to move low-entropy gas from the cluster core to the heat reservoir and will be able to heat the inner cluster more efficiently than would be possible by direct energy transfer from jets alone. We show that the maximum efficiency of this mechanism, i.e. the ratio between the conductive thermal energy and the work needed to lift the gas, $ξ_{\mathrm{max}}$ can exceed 100 per cent. While $ξ$ < $ξ_{\mathrm{max}}$ in realistic scenarios, AGN-induced thermal conduction has the potential to significantly increase the efficiency with which AGN can heat cool-core clusters and transform the bursty AGN activities into a smoother and enduring heating process.

astro-ph.GA↗

Determining the composition of radio plasma via circular polarization: the prospects of the Cygnus A hot spots

The composition of the relativistic plasma produced in active galactic nuclei and ejected via powerful jets into the interstellar/intergalactic medium is still a major unsettled issue. It might be a positron-electron plasma in case the plasma was created by pair production in the intense photon fields near accreting super-massive black holes. Alternatively, it might be an electron-proton plasma in case magnetic fields lift and accelerate the thermal gas of accretion discs into relativistic jets as the recent detection of $γ$-rays from blazars indicates. Despite various attempts to unambiguously establish the composition of the relativistic jets, this remains a major unknown. Here, we propose a way to settle the question via sensitive measurements of circular polarization (CP) in the radio emission of the hot spots of bright radio galaxies like Cygnus A. The CP of synchrotron emission is determined by the circular motions of the radiating relativistic leptons. In case of charge symmetric energy spectra of a electron-positron plasma, it should be exactly zero. In case of an electron-proton plasma the electrons imprint their gyration onto the CP and we expect the hot spots of Cygnus A to exhibit a fractional CP at a level of $10^{-3}\,(ν/\mbox{GHz})^{-{1}/{2}}$, which is challenging to measure, but not completely unfeasible.

astro-ph.HE↗

The Galactic Faraday depth sky revisited

The Galactic Faraday depth sky is a tracer for both the Galactic magnetic field and the thermal electron distribution. It has been previously reconstructed from polarimetric measurements of extra-galactic point sources. Here, we improve on these works by using an updated inference algorithm as well as by taking into account the free-free emission measure map from the Planck survey. In the future, the data situation will improve drastically with the next generation Faraday rotation measurements from SKA and its pathfinders. Anticipating this, the aim of this paper is to update the map reconstruction method with the latest development in imaging based on information field theory. We demonstrate the validity of the new algorithm by applying it to the Oppermann et al. (2012) data compilation and compare our results to the previous map.\\ Despite using exactly the previous data set, a number of novel findings are made: A non-parametric reconstruction of an overall amplitude field resembles the free-free emission measure map of the Galaxy. Folding this free-free map into the analysis allows for more detailed predictions. The joint inference enables us to identify regions with deviations from the assumed correlations between the free-free and Faraday data, thereby pointing us to Galactic structures with distinguishably different physics. We e.g. find evidence for an alignment of the magnetic field within the line of sights along both directions of the Orion arm.

astro-ph.GA↗

Encoding prior knowledge in the structure of the likelihood

The inference of deep hierarchical models is problematic due to strong dependencies between the hierarchies. We investigate a specific transformation of the model parameters based on the multivariate distributional transform. This transformation is a special form of the reparametrization trick, flattens the hierarchy and leads to a standard Gaussian prior on all resulting parameters. The transformation also transfers all the prior information into the structure of the likelihood, hereby decoupling the transformed parameters a priori from each other. A variational Gaussian approximation in this standardized space will be excellent in situations of relatively uninformative data. Additionally, the curvature of the log-posterior is well-conditioned in directions that are weakly constrained by the data, allowing for fast inference in such a scenario. In an example we perform the transformation explicitly for Gaussian process regression with a priori unknown correlation structure. Deep models are inferred rapidly in highly and slowly in poorly informed situations. The flat model show exactly the opposite performance pattern. A synthesis of both, the deep and the flat perspective, provides their combined advantages and overcomes the individual limitations, leading to a faster inference.

stat.ML↗

HMCF - Hamiltonian Monte Carlo Sampling for Fields - A Python framework for HMC sampling with NIFTy

HMCF "Hamiltonian Monte Carlo for Fields" is a software add-on for the NIFTy "Numerical Information Field Theory" framework implementing Hamiltonian Monte Carlo (HMC) sampling in Python. HMCF as well as NIFTy are designed to address inference problems in high-dimensional spatially correlated setups such as image reconstruction. HMCF adds an HMC sampler to NIFTy that automatically adjusts the many free parameters steering the HMC sampling machinery. A wide variety of features ensure efficient full-posterior sampling for high-dimensional inference problems. These features include integration step size adjustment, evaluation of the mass matrix, convergence diagnostics, higher order symplectic integration and simultaneous sampling of parameters and hyperparameters in Bayesian hierarchical models.

physics.data-an↗

Information theory for fields

A physical field has an infinite number of degrees of freedom since it has a field value at each location of a continuous space. Therefore, it is impossible to know a field from finite measurements alone and prior information on the field is essential for field inference. An information theory for fields is needed to join the measurement and prior information into probabilistic statements on field configurations. Such an information field theory (IFT) is built upon the language of mathematical physics, in particular on field theory and statistical mechanics. IFT permits the mathematical derivation of optimal imaging algorithms, data analysis methods, and even computer simulation schemes. The application of IFT algorithms to astronomical datasets provides high fidelity images of the Universe and facilitates the search for subtle statistical signals from the Big Bang. The concepts of IFT might even pave the road to novel computer simulations that are aware of their own uncertainties.

astro-ph.CO↗

Denoising, deconvolving and decomposing multi-domain photon observations- The D4PO algorithm

Astronomical imaging based on photon count data is a non-trivial task. In this context we show how to denoise, deconvolve, and decompose multi-domain photon observations. The primary objective is to incorporate accurate and well motivated likelihood and prior models in order to give reliable estimates about morphologically different but superimposed photon flux components present in the data set. Thereby we denoise and deconvolve photon counts, while simultaneously decomposing them into diffuse, point-like and uninteresting background radiation fluxes. The decomposition is based on a probabilistic hierarchical Bayesian parameter model within the framework of information field theory (IFT). In contrast to its predecessor D$^3$PO, D$^4$PO reconstructs multi-domain components. Thereby each component is defined over its own direct product of multiple independent domains, for example location and energy. D$^4$PO has the capability to reconstruct correlation structures over each of the sub-domains of a component separately. Thereby the inferred correlations implicitly define the morphologically different source components, except for the spatial correlations of the point-like flux. Point-like source fluxes are spatially uncorrelated by definition. The capabilities of the algorithm are demonstrated by means of a synthetic, but realistic, mock data set, providing spectral and spatial information about each detected photon. D$^4$PO successfully denoised, deconvolved, and decomposed a photon count image into diffuse, point-like and background flux, each being functions of location as well as energy. Moreover, uncertainty estimates of the reconstructed fields as well as of their correlation structure are provided employing their posterior density function and accounting for the manifolds the domains reside on.

astro-ph.IM↗

Separating diffuse from point-like sources - a Bayesian approach

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 independent from each other and to follow a power-law brightness distribution. The diffuse emission is described as a non-parametric log-normal model with a priori unknown correlation structure. This model enforces positivity of the underlying emission and allows for variation in the order of magnitudes. The correlation structure is recovered non-parametrically in addition to the diffuse flux and is used for the separation of the point sources. Additionally many measurement artifacts appear as point-like or quasi-point-like effects, not compatible with superimposed diffuse emission. An estimate of the separation uncertainty can be provided as well. We demonstrate the capabilities of the derived method on synthetic data and data obtained by the Hubble Space Telescope, emphasizing its effect on instrumental artifacts as well as physical sources. The performance of this method is compared to the background estimation of the SExtractor method, as well as to a denoising auto-encoder.

astro-ph.IM↗

Radio Imaging With Information Field Theory

Data from radio interferometers provide a substantial challenge for statisticians. It is incomplete, noise-dominated and originates from a non-trivial measurement process. The signal is not only corrupted by imperfect measurement devices but also from effects like fluctuations in the ionosphere that act as a distortion screen. In this paper we focus on the imaging part of data reduction in radio astronomy and present RESOLVE, a Bayesian imaging algorithm for radio interferometry in its new incarnation. It is formulated in the language of information field theory. Solely by algorithmic advances the inference could be sped up significantly and behaves noticeably more stable now. This is one more step towards a fully user-friendly version of RESOLVE which can be applied routinely by astronomers.

astro-ph.IM↗

Search for quasi-periodic signals in magnetar giant flares

Quasi-periodic oscillations (QPOs) discovered in the decaying tails of giant flares of magnetars are believed to be torsional oscillations of neutron stars. These QPOs have a high potential to constrain properties of high-density matter. In search for quasi-periodic signals, we study the light curves of the giant flares of SGR 1806-20 and SGR 1900+14, with a non-parametric Bayesian signal inference method called D$^3$PO. The D$^3$PO algorithm models the raw photon counts as a continuous flux and takes the Poissonian shot noise as well as all instrument effects into account. It reconstructs the logarithmic flux and its power spectrum from the data. Using this fully noise-aware method, we do not confirm previously reported frequency lines at $ν\gtrsim17\,$Hz because they fall into the noise-dominated regime. However, we find two new potential candidates for oscillations at $9.2\,$Hz (SGR 1806-20) and $7.7\,$Hz (SGR 1900+14). If these are real and the fundamental magneto-elastic oscillations of the magnetars, current theoretical models would favour relatively weak magnetic fields $\bar B\sim 6\times10^{13} - 3\times10^{14}\,$G (SGR 1806-20) and a relatively low shear velocity inside the crust compared to previous findings.

astro-ph.HE↗

Inference of signals with unknown correlation structure from nonlinear measurements

We present a method to reconstruct autocorrelated signals together with their autocorrelation structure from nonlinear, noisy measurements for arbitrary monotonous nonlinear instrument response. In the presented formulation the algorithm provides a significant speedup compared to prior implementations, allowing for a wider range of application. The nonlinearity can be used to model instrument characteristics or to enforce properties on the underlying signal, such as positivity. Uncertainties on any posterior quantities can be provided due to independent samples from an approximate posterior distribution. We demonstrate the methods applicability via simulated and real measurements, using different measurement instruments, nonlinearities and dimensionality.

stat.ME↗

Imprints of the large-scale structure on AGN formation and evolution

Black hole masses are found to correlate with several global properties of their host galaxies, suggesting that black holes and galaxies have an intertwined evolution and that active galactic nuclei (AGN) have a significant impact on galaxy evolution. Since the large-scale environment can also affect AGN, this work studies how their formation and properties depend on the environment. We have used a reconstructed three-dimensional high-resolution density field obtained from a Bayesian large-scale structure reconstruction method applied to the 2M++ galaxy sample. A web-type classification relying on the shear tensor is used to identify different structures on the cosmic web, defining voids, sheets, filaments, and clusters. We confirm that the environmental density affects the AGN formation and their properties. We found that the AGN abundance is equivalent to the galaxy abundance, indicating that active and inactive galaxies reside in similar dark matter halos. However, occurrence rates are different for each spectral type and accretion rate. These differences are consistent with the AGN evolutionary sequence suggested by previous authors, Seyferts and Transition objects transforming into LINERs (Low-Ionization Nuclear Emission Line Regions), the weaker counterpart of Seyferts. We conclud that AGN properties depend on the environmental density more than on the web-type. More powerful starbursts and younger stellar populations are found in high densities, where interactions and mergers are more likely. AGN hosts show smaller masses in clusters for Seyferts and Transition objects, which might be due to gas stripping. In voids, the AGN population is dominated by the most massive galaxy hosts.

astro-ph.GA↗

Inferring Galactic magnetic field model parameters using IMAGINE - An Interstellar MAGnetic field INference Engine

Context. The Galactic magnetic field (GMF) has a huge impact on the evolution of the Milky Way. Yet currently there exists no standard model for it, as its structure is not fully understood. In the past many parametric GMF models of varying complexity have been developed that all have been fitted to an individual set of observational data complicating comparability. Aims. Our goal is to systematize parameter inference of GMF models. We want to enable a statistical comparison of different models in the future, allow for simple refitting with respect to newly available data sets and thereby increase the research area's transparency. We aim to make state-of-the-art Bayesian methods easily available and in particular to treat the statistics related to the random components of the GMF correctly. Methods. To achieve our goals, we built IMAGINE, the Interstellar Magnetic Field Inference Engine. It is a modular open source framework for doing inference on generic parametric models of the Galaxy. We combine highly optimized tools and technology such as the MultiNest sampler and the information field theory framework NIFTy in order to leverage existing expertise. Results. We demonstrate the steps needed for robust parameter inference and model comparison. Our results show how important the combination of complementary observables like synchrotron emission and Faraday depth is while building a model and fitting its parameters to data. IMAGINE is open-source software available under the GNU General Public License v3 (GPL-3) at: https://gitlab.mpcdf.mpg.de/ift/IMAGINE

astro-ph.IM↗

Towards information optimal simulation of partial differential equations

Most simulation schemes for partial differential equations (PDEs) focus on minimizing a simple error norm of a discretized version of a field. This paper takes a fundamentally different approach; the discretized field is interpreted as data providing information about a real physical field that is unknown. This information is sought to be conserved by the scheme as the field evolves in time. Such an information theoretic approach to simulation was pursued before by information field dynamics (IFD). In this paper we work out the theory of IFD for nonlinear PDEs in a noiseless Gaussian approximation. The result is an action that can be minimized to obtain an informationally optimal simulation scheme. It can be brought into a closed form using field operators to calculate the appearing Gaussian integrals. The resulting simulation schemes are tested numerically in two instances for the Burgers equation. Their accuracy surpasses finite-difference schemes on the same resolution. The IFD scheme, however, has to be correctly informed on the subgrid correlation structure. In certain limiting cases we recover well-known simulation schemes like spectral Fourier Galerkin methods. We discuss implications of the approximations made.

stat.ME↗

Bayesian weak lensing tomography: Reconstructing the 3D large-scale distribution of matter with a lognormal prior

We present a Bayesian reconstruction algorithm that infers the three-dimensional large-scale matter distribution from the weak gravitational lensing effects measured in the image shapes of galaxies. The algorithm is designed to also work with non-Gaussian posterior distributions which arise, for example, from a non-Gaussian prior distribution. In this work, we use a lognormal prior and compare the reconstruction results to a Gaussian prior in a suite of increasingly realistic tests on mock data. We find that in cases of high noise levels (i.e. for low source galaxy densities and/or high shape measurement uncertainties), both normal and lognormal priors lead to reconstructions of comparable quality, but with the lognormal reconstruction being prone to mass-sheet degeneracy. In the low-noise regime and on small scales, the lognormal model produces better reconstructions than the normal model: The lognormal model 1) enforces non-negative densities, while negative densities are present when a normal prior is employed, 2) better traces the extremal values and the skewness of the true underlying distribution, and 3) yields a higher pixel-wise correlation between the reconstruction and the true density.

astro-ph.CO↗

Field dynamics inference via spectral density estimation

Stochastic differential equations (SDEs) are of utmost importance in various scientific and industrial areas. They are the natural description of dynamical processes whose precise equations of motion are either not known or too expensive to solve, e.g., when modeling Brownian motion. In some cases, the equations governing the dynamics of a physical system on macroscopic scales occur to be unknown since they typically cannot be deduced from general principles. In this work, we describe how the underlying laws of a stochastic process can be approximated by the spectral density of the corresponding process. Furthermore, we show how the density can be inferred from possibly very noisy and incomplete measurements of the dynamical field. Generally, inverse problems like these can be tackled with the help of Information Field Theory (IFT). For now, we restrict to linear and autonomous processes. Though, this is a non-conceptual limitation that may be omitted in future work. To demonstrate its applicability we employ our reconstruction algorithm on a time-series and spatio-temporal processes.

stat.ME↗

Noisy independent component analysis of auto-correlated components

We present a new method for the separation of superimposed, independent, auto-correlated components from noisy multi-channel measurement. The presented method simultaneously reconstructs and separates the components, taking all channels into account and thereby increases the effective signal-to-noise ratio considerably, allowing separations even in the high noise regime. Characteristics of the measurement instruments can be included, allowing for application in complex measurement situations. Independent posterior samples can be provided, permitting error estimates on all desired quantities. Using the concept of information field theory, the algorithm is not restricted to any dimensionality of the underlying space or discretization scheme thereof.

stat.ME↗

The Galaxy in circular polarization: all-sky radio prediction, detection strategy, and the charge of the leptonic cosmic rays

The diffuse Galactic synchrotron emission should exhibit a low level of diffuse circular polarization (CP) due to the circular motions of the emitting relativistic electrons. This probes the Galactic magnetic field in a similar way as the product of total Galactic synchrotron intensity times Faraday depth. We use this to construct an all sky prediction of the so far unexplored Galactic CP from existing measurements. This map can be used to search for this CP signal in low frequency radio data even prior to imaging. If detected as predicted, it would confirm the expectation that relativistic electrons, and not positrons, are responsible for the Galactic radio emission. Furthermore, the strength of real to predicted circular polarization would provide statistical information on magnetic structures along the line-of-sights.

astro-ph.HE↗