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Jens Jasche

Publications and source records attributed to Jens Jasche.

At least 73 records · Page 4Linked to original sources

IMAGINE: modeling the Galactic magnetic field

The IMAGINE Consortium aims to bring modeling of the magnetic field of the Milky Way to a next level, by using Bayesian inference. IMAGINE includes an open-source modular software pipeline that optimizes parameters in a user-defined Galactic magnetic field model against various selected observational datasets. Bayesian priors can be added as external probabilistic constraints of the model parameters. These conference proceedings describe the science goals of the IMAGINE Consortium, the software pipeline and its inputs, viz observational data sets, Galactic magnetic field models, and Bayesian priors.

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A rigorous EFT-based forward model for large-scale structure

Conventional approaches to cosmology inference from galaxy redshift surveys are based on n-point functions, which are under rigorous perturbative control on sufficiently large scales. Here, we present an alternative approach, which employs a likelihood at the level of the galaxy density field. By integrating out small-scale modes based on effective-field theory arguments, we prove that this likelihood is under perturbative control if certain specific conditions are met. We further show that the information captured by this likelihood is equivalent to the combination of the next-to-leading order galaxy power spectrum, leading-order bispectrum, and BAO reconstruction. Combined with MCMC sampling and MAP optimization techniques, our results allow for fully Bayesian cosmology inference from large-scale structure that is under perturbative control. We illustrate this via a first demonstration of unbiased cosmology inference from nonlinear large-scale structure using this likelihood. In particular, we show unbiased estimates of the power spectrum normalization $σ_{8}$ from a catalog of simulated dark matter halos, where nonlinear information is crucial in breaking the $b_{1} - σ_{8}$ degeneracy.

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The Fifth Force in the Local Cosmic Web

Extensions of the standard models of particle physics and cosmology often lead to long-range fifth forces with properties dependent on gravitational environment. Fifth forces on astrophysical scales are best studied in the cosmic web where perturbation theory breaks down. We present constraints on chameleon- and symmetron-screened fifth forces with Yukawa coupling and megaparsec range -- as well as unscreened fifth forces with differential coupling to galactic mass components -- by searching for the displacements they predict between galaxies' stars and gas. Taking data from the Alfalfa HI survey, identifying galaxies' gravitational environments with the maps of Desmond et al. (2018a) and forward-modelling with a Bayesian likelihood framework, we set upper bounds on fifth-force strength relative to Newtonian gravity from $ΔG/G_N < \text{few} \: \times 10^{-4}$ for range $λ_C = 50$ Mpc, to $ΔG/G_N \lesssim 0.1$ for $λ_C = 500$ kpc. In $f(R)$ gravity this requires $f_{R0} < \text{few} \: \times \: 10^{-8}$. The analogous bounds without screening are $ΔG/G_N < \text{few} \: \times 10^{-4}$ and $ΔG/G_N < \text{few} \times 10^{-3}$. These are the tightest and among the only fifth-force constraints on galaxy scales. We show how our results may be strengthened with future survey data and identify the key features of an observational programme for furthering fifth-force tests beyond the Solar System.

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Cosmological inference from Bayesian forward modelling of deep galaxy redshift surveys

We present a large-scale Bayesian inference framework to constrain cosmological parameters using galaxy redshift surveys, via an application of the Alcock-Paczyński (AP) test. Our physical model of the non-linearly evolved density field, as probed by galaxy surveys, employs Lagrangian perturbation theory (LPT) to connect Gaussian initial conditions to the final density field, followed by a coordinate transformation to obtain the redshift space representation for comparison with data. We generate realizations of primordial and present-day matter fluctuations given a set of observations. This hierarchical approach encodes a novel AP test, extracting several orders of magnitude more information from the cosmological expansion compared to classical approaches, to infer cosmological parameters and jointly reconstruct the underlying 3D dark matter density field. The novelty of this AP test lies in constraining the comoving-redshift transformation to infer the appropriate cosmology which yields isotropic correlations of the galaxy density field, with the underlying assumption relying purely on the cosmological principle. Such an AP test does not rely explicitly on modelling the full statistics of the field. We verify in depth via simulations that this renders our test robust to model misspecification. This leads to another crucial advantage, namely that the cosmological parameters exhibit extremely weak dependence on the currently unresolved phenomenon of galaxy bias, thereby circumventing a potentially key limitation. This is consequently among the first methods to extract a large fraction of information from statistics other than that of direct density contrast correlations, without being sensitive to the amplitude of density fluctuations. We perform several statistical efficiency and consistency tests on a mock galaxy catalogue, using the SDSS-III survey as template.

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Fifth force constraints from galaxy warps

Intra-galaxy signals contain a wealth of information on fundamental physics, both the dark sector and the nature of gravity. While so far largely unexplored, such probes are set to rise dramatically in importance as upcoming surveys provide data of unprecedented quantity and quality on galaxy structure and dynamics. In this paper, we use warping of stellar disks to test the chameleon- or symmetron-screened fifth forces which generically arise when new fields couple to matter. We take r-band images of mostly late-type galaxies from the Nasa Sloan Atlas and develop an automated algorithm to quantify the degree of U-shaped warping they exhibit. We then forward-model the warp signal as a function of fifth-force strength $ΔG/G_N$ and range $λ_C$, and the gravitational environments and internal properties of the galaxies, including full propagation of the non-Gaussian uncertainties. Convolving this fifth-force likelihood function with a Gaussian describing astrophysical and observational noise and then constraining $ΔG/G_N$ and $λ_C$ by Markov Chain Monte Carlo, we find the overall likelihood to be significant increased ($Δ\log(\mathcal{L}) \simeq 20$) by adding a screened fifth force with $λ_C \simeq 2$ Mpc, $ΔG/G_N \simeq 0.01$. The variation of $Δ\log(\mathcal{L})$ with $λ_C$ is quantitatively as expected from the correlation of the magnitude of the fifth-force field with the force's range, and a similar model without screening achieves no increase in likelihood over the General Relativistic case $ΔG=0$. Although these results are in good agreement with a previous analysis of the same model using offsets between galaxies' stellar and gas mass centroids (Desmond et al. 2018), we caution that the effects of confounding baryonic and dark matter physics must be thoroughly investigated for the results of the inference to be unambiguous.

astro-ph.CO↗

Fifth force constraints from the separation of galaxy mass components

One of the most common consequences of extensions to the standard models of particle physics or cosmology is the emergence of a fifth force. While generic fifth forces are tightly constrained at Solar System scales and below, they may escape detection by means of a screening mechanism which effectively removes them in dense environments. We constrain the strength $ΔG/G_N$ and range $λ_C$ of a chameleon- or symmetron-screened fifth force with Yukawa coupling -- as well as an unscreened fifth force with differential coupling to galactic mass components -- by searching for the displacement it predicts between galaxies' stellar and gas mass centroids. Taking data from the Alfalfa HI survey, identifying galaxies' gravitational environments with the maps of Desmond et al. (2018a) and forward-modelling with a Bayesian likelihood framework, we find $6.6σ$ evidence for $ΔG>0$ at $λ_C \simeq 2$ Mpc, with $ΔG/G_N = 0.025$ at maximum-likelihood. A similar fifth-force model without screening gives no increase in likelihood over the case $ΔG = 0$ for any $λ_C$. Although we validate these results by several methods, we do not claim screened modified gravity to provide the only possible explanation for the signal: this would require knowing that "galaxy formation" physics could not be responsible. We show also the results of a more conservative -- though less well motivated -- noise model which yields only upper limits on $ΔG/G_N$, ranging from $\sim10^{-1}$ for $λ_C \simeq 0.5$ Mpc to $\sim \: \text{few} \times 10^{-4}$ at $λ_C \simeq 50$ Mpc. We show how these constraints may be improved by future galaxy surveys and identify the key features of an observational programme for directly constraining fifth forces on galactic scales. This paper provides a complete description of the analysis summarised in Desmond et al. (2018b).

astro-ph.CO↗

IMAGINE: A comprehensive view of the interstellar medium, Galactic magnetic fields and cosmic rays

In this white paper we introduce the IMAGINE Consortium and its scientific background, goals and structure. Our purpose is to coordinate and facilitate the efforts of a diverse group of researchers in the broad areas of the interstellar medium, Galactic magnetic fields and cosmic rays, and our goal is to develop more comprehensive insights into the structures and roles of interstellar magnetic fields and their interactions with cosmic rays. To achieve a higher level of self-consistency, depth and rigour can only be achieved by the coordinated efforts of experts in diverse areas of astrophysics involved in observational, theoretical and numerical work. We present our view of the present status of this topic, identify its key unsolved problems and suggest a strategy that will underpin our work. The backbone of the consortium is the Interstellar MAGnetic field INference Engine, a publicly available Bayesian platform that employs robust statistical methods to explore the multi-dimensional likelihood space using any number of modular inputs. It provides an interpretation and modelling framework that has the power and flexibility to include a variety of observational, theoretical and numerical lines of evidence into a self-consistent and comprehensive picture of the thermal and non-thermal interstellar media. An important innovation is that a consistent understanding of the phenomena that are directly or indirectly influenced by the Galactic magnetic field, such as the deflection of ultra-high energy cosmic rays or extragalactic backgrounds, is made an integral part of the modelling. The IMAGINE Consortium, which is informal by nature and open to new participants, hereby presents a methodological framework for the modelling and understanding of Galactic magnetic fields that is available to all communities whose research relies on a state-of-the-art solution to this problem. (Abridged.)

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Physical Bayesian modelling of the non-linear matter distribution: new insights into the Nearby Universe

Accurate analyses of present and next-generation galaxy surveys require new ways to handle effects of non-linear gravitational structure formation in data. To address these needs we present an extension of our previously developed algorithm for Bayesian Origin Reconstruction from Galaxies to analyse matter clustering at non-linear scales in observations. This is achieved by incorporating a numerical particle mesh model of structure formation into our Bayesian inference framework. The algorithm simultaneously infers the 3D primordial matter fluctuations from which present non-linear observations formed and provides reconstructions of velocity fields and structure formation histories. The physical forward modelling approach automatically accounts for non-Gaussian features in evolved matter density fields and addresses the redshift space distortion problem associated with peculiar motions of galaxies. Our algorithm employs a hierarchical Bayes approach to jointly account for observational effects, such as galaxy biases, selection effects, and noise. Corresponding parameters are marginalized out via a sophisticated Markov Chain Monte Carlo approach relying on a combination of a multiple block sampling framework and a Hamiltonian Monte Carlo sampler. We demonstrate the performance of the method by applying it to the 2M++ galaxy compilation, tracing the matter distribution of the Nearby Universe. We show accurate and detailed inferences of the 3D non-linear dark matter distribution of the Nearby Universe. As exemplified in the case of the Coma cluster, we provide mass estimates that are compatible with those obtained from weak lensing and X-ray observations. For the first time, we reconstruct the vorticity of the non-linear velocity field from observations. In summary, our method provides plausible and detailed inferences of dark matter and velocity fields of our cosmic neighbourhood.

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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↗

Reconstructing the gravitational field of the local universe

Tests of gravity at the galaxy scale are in their infancy. As a first step to systematically uncovering the gravitational significance of galaxies, we map three fundamental gravitational variables -- the Newtonian potential, acceleration and curvature -- over the galaxy environments of the local universe to a distance of approximately 200 Mpc. Our method combines the contributions from galaxies in an all-sky redshift survey, halos from an N-body simulation hosting low-luminosity objects, and linear and quasi-linear modes of the density field. We use the ranges of these variables to determine the extent to which galaxies expand the scope of generic tests of gravity and are capable of constraining specific classes of model for which they have special significance. Finally, we investigate the improvements afforded by upcoming galaxy surveys.

astro-ph.GA↗

Bayesian power-spectrum inference with foreground and target contamination treatment

This work presents a joint and self-consistent Bayesian treatment of various foreground and target contaminations when inferring cosmological power-spectra and three dimensional density fields from galaxy redshift surveys. This is achieved by introducing additional block sampling procedures for unknown coefficients of foreground and target contamination templates to the previously presented ARES framework for Bayesian large scale structure analyses. As a result the method infers jointly and fully self-consistently three dimensional density fields, cosmological power-spectra, luminosity dependent galaxy biases, noise levels of respective galaxy distributions and coefficients for a set of a priori specified foreground templates. In addition this fully Bayesian approach permits detailed quantification of correlated uncertainties amongst all inferred quantities and correctly marginalizes over observational systematic effects. We demonstrate the validity and efficiency of our approach in obtaining unbiased estimates of power-spectra via applications to realistic mock galaxy observations subject to stellar contamination and dust extinction. While simultaneously accounting for galaxy biases and unknown noise levels our method reliably and robustly infers three dimensional density fields and corresponding cosmological power-spectra from deep galaxy surveys. Further our approach correctly accounts for joint and correlated uncertainties between unknown coefficients of foreground templates and the amplitudes of the power-spectrum. An effect amounting up to $10$ percent correlations and anti-correlations across large ranges in Fourier space.

astro-ph.CO↗

The phase-space structure of nearby dark matter as constrained by the SDSS

Previous studies using numerical simulations have demonstrated that the shape of the cosmic web can be described by studying the Lagrangian displacement field. We extend these analyses, showing that it is now possible to perform a Lagrangian description of cosmic structure in the nearby Universe based on large-scale structure observations. Building upon recent Bayesian large-scale inference of initial conditions, we present a cosmographic analysis of the dark matter distribution and its evolution, referred to as the dark matter phase-space sheet, in the nearby universe as probed by the Sloan Digital Sky Survey main galaxy sample. We consider its stretchings and foldings using a tetrahedral tessellation of the Lagrangian lattice. The method provides extremely accurate estimates of nearby density and velocity fields, even in regions of low galaxy density. It also measures the number of matter streams, and the deformation and parity reversals of fluid elements, which were previously thought inaccessible using observations. We illustrate the approach by showing the phase-space structure of known objects of the nearby Universe such as the Sloan Great Wall, the Coma cluster and the Boötes void. We dissect cosmic structures into four distinct components (voids, sheets, filaments, and clusters), using the Lagrangian classifiers DIVA, ORIGAMI, and a new scheme which we introduce and call LICH. Because these classifiers use information other than the sheer local density, identified structures explicitly carry physical information about their formation history. Accessing the phase-space structure of dark matter in galaxy surveys opens the way for new confrontations of observational data and theoretical models. We have made our data products publicly available.

astro-ph.CO↗

Comparing cosmic web classifiers using information theory

We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative performance of the classifiers T-web, DIVA and ORIGAMI for: (i) analyzing the morphology of the cosmic web, (ii) discriminating dark energy models, and (iii) predicting galaxy colors. Our study substantiates a data-supported connection between cosmic web analysis and information theory, and paves the path towards principled design of analysis procedures for the next generation of galaxy surveys. We have made the cosmic web maps, galaxy catalog, and analysis scripts used in this work publicly available.

astro-ph.CO↗

SOMBI: Bayesian identification of parameter relations in unstructured cosmological data

This work describes the implementation and application of a correlation determination method based on Self Organizing Maps and Bayesian Inference (SOMBI). SOMBI aims to automatically identify relations between different observed parameters in unstructured cosmological or astrophysical surveys by automatically identifying data clusters in high-dimensional datasets via the Self Organizing Map neural network algorithm. Parameter relations are then revealed by means of a Bayesian inference within respective identified data clusters. Specifically such relations are assumed to be parametrized as a polynomial of unknown order. The Bayesian approach results in a posterior probability distribution function for respective polynomial coefficients. To decide which polynomial order suffices to describe correlation structures in data, we include a method for model selection, the Bayesian Information Criterion, to the analysis. The performance of the SOMBI algorithm is tested with mock data. As illustration we also provide applications of our method to cosmological data. In particular, we present results of a correlation analysis between galaxy and AGN properties provided by the SDSS catalog with the cosmic large-scale-structure (LSS). The results indicate that the combined galaxy and LSS dataset indeed is clustered into several sub-samples of data with different average properties (for example different stellar masses or web-type classifications). The majority of data clusters appear to have a similar correlation structure between galaxy properties and the LSS. In particular we revealed a positive and linear dependency between the stellar mass, the absolute magnitude and the color of a galaxy with the corresponding cosmic density field. A remaining subset of data shows inverted correlations, which might be an artifact of non-linear redshift distortions.

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Halo detection via large-scale Bayesian inference

We present a proof-of-concept of a novel and fully Bayesian methodology designed to detect halos of different masses in cosmological observations subject to noise and systematic uncertainties. Our methodology combines the previously published Bayesian large-scale structure inference algorithm, HADES, and a Bayesian chain rule (the Blackwell-Rao Estimator), which we use to connect the inferred density field to the properties of dark matter halos. To demonstrate the capability of our approach we construct a realistic galaxy mock catalogue emulating the wide-area 6-degree Field Galaxy Survey, which has a median redshift of approximately 0.05. Application of HADES to the catalogue provides us with accurately inferred three-dimensional density fields and corresponding quantification of uncertainties inherent to any cosmological observation. We then use a cosmological simulation to relate the amplitude of the density field to the probability of detecting a halo with mass above a specified threshold. With this information we can sum over the HADES density field realisations to construct maps of detection probabilities and demonstrate the validity of this approach within our mock scenario. We find that the probability of successful of detection of halos in the mock catalogue increases as a function of the signal-to-noise of the local galaxy observations. Our proposed methodology can easily be extended to account for more complex scientific questions and is a promising novel tool to analyse the cosmic large-scale structure in observations.

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Unmasking the Masked Universe: the 2M++ catalogue through Bayesian eyes

This work describes a full Bayesian analysis of the Nearby Universe as traced by galaxies of the 2M++ survey. The analysis is run in two sequential steps. The first step self-consistently derives the luminosity dependent galaxy biases, the power-spectrum of matter fluctuations and matter density fields within a Gaussian statistic approximation. The second step makes a detailed analysis of the three dimensional Large Scale Structures, assuming a fixed bias model and a fixed cosmology. This second step allows for the reconstruction of both the final density field and the initial conditions at z=1000 assuming a fixed bias model. From these, we derive fields that self-consistently extrapolate the observed large scale structures. We give two examples of these extrapolation and their utility for the detection of structures: the visibility of the Sloan Great Wall, and the detection and characterization of the Local Void using DIVA, a Lagrangian based technique to classify structures.

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Probabilistic cartography of the large-scale structure

The BORG algorithm is an inference engine that derives the initial conditions given a cosmological model and galaxy survey data, and produces physical reconstructions of the underlying large-scale structure by assimilating the data into the model. We present the application of BORG to real galaxy catalogs and describe the primordial and late-time large-scale structure in the considered volumes. We then show how these results can be used for building various probabilistic maps of the large-scale structure, with rigorous propagation of uncertainties. In particular, we study dynamic cosmic web elements and secondary effects in the cosmic microwave background.

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