arXiv subjects

Torsten A. Enßlin

Publications and source records attributed to Torsten A. Enßlin.

At least 19 recordsLinked to original sources

arXiv2026

Bayesian polarization calibration and imaging in very long baseline interferometry

Extracting polarimetric information from very long baseline interferometry (VLBI) data is demanding but vital for understanding the synchrotron radiation process and the magnetic fields of celestial objects, such as active galactic nuclei (AGNs). However, conventional CLEAN-based calibration and imaging methods provide suboptimal resolution without uncertainty estimation of calibration solutions, while requiring manual steering from an experienced user. We present a Bayesian polarization calibration and imaging method using Bayesian imaging software resolve for VLBI data sets, that explores the posterior distribution of antenna-based gains, polarization leakages, and polarimetric images jointly from pre-calibrated data. We demonstrate our calibration and imaging method with observations of the quasar 3C273 with the VLBA at 15 GHz and the blazar OJ287 with the GMVA+ALMA at 86 GHz. Compared to the CLEAN method, our approach provides physically realistic images that satisfy positivity of flux and polarization constraints and can reconstruct complex source structures composed of various spatial scales. Our method systematically accounts for calibration uncertainties in the final images and provides uncertainties of Stokes images and calibration solutions. The automated Bayesian approach for calibration and imaging will be able to obtain high-fidelity polarimetric images using high-quality data from next-generation radio arrays. The pipeline developed for this work is publicly available.

astro-ph.IM↗

arXiv2026

Variational Inference Using a Differentiable Multigrid Linear Solver

Gradient-based Bayesian inference methods require efficient access to Jacobian and adjoint-Jacobian operators of high-dimensional forward models. While multigrid solvers provide near-optimal complexity for elliptic partial differential equations, they are rarely available in forms compatible with automatic differentiation (AD). We develop a differentiable multigrid solver for steady-state diffusion-absorption problems and derive its adjoint operations analytically through the full multigrid hierarchy. The resulting solver, DMGS, is implemented in C++ and interfaced with JAX to provide efficient Jacobian-vector and vector-Jacobian products for variational inference in the NIFTy framework. We validate the approach on a 3D inverse problem involving diffuse radiative transfer in tissue, reconstructing an effective radiative source from Monte Carlo-simulated data. The reconstruction reproduces the data at a reduced chi-squared of 1.1 and generalizes to 32 independent validation datasets. Benchmarks against a JAX-native multigrid implementation show comparable runtimes and consistently lower peak memory for the hand-derived adjoint, with modest reverse-mode overhead. These results establish differentiable multigrid solvers as practical building blocks for variational inference in PDE-constrained problems.

math.NA↗

arXiv2026

Bayesian Rate Inference for Sequence Motif Dynamics in Systems of Reactive Nucleic Acids

The RNA world hypothesis suggests a pathway of how life emerged on early earth. It assumes that life started with RNA based systems, capable of storing, transmitting and replicating information, envisioning that monomers and short RNA oligomers interact to form longer strands, eventually becoming catalytically active ribozymes. Key reactions in RNA pools are hybridization, dehybridization, templated ligation, and cleavage. Those reactions depend on many environmental parameters and the wide range of possible configurations among interacting strands. In order to scan such high dimensional parameter spaces, efficient descriptions are needed. Motif rate equations project complex strand reactor dynamics onto sequence motif space. Here we present a Bayesian inference framework to infer their parameters from ligation count data produced by strand reactor simulations. This provides a framework to match the simpler motif rate equations to more complex simulations. Additionally, it is a step towards inferring reaction rate constants directly from experimental data, including rigorous uncertainty estimation. This could be an essential procedure to connect theory and experiment, and deepen our understanding of the essential features necessary for life to emerge.

physics.bio-ph↗

arXiv2026

A Three-Dimensional Tomographic Reconstruction of the Galactic Cosmic-Ray Proton Density

Cosmic rays (CRs) are a ubiquitous non-thermal component of the interstellar medium (ISM). A data-driven three-dimensional (3D) map of their distribution is essential for understanding CR transport and constraining the spatial distribution of their sources. In this work, we reconstructed the 3D spatial distribution of the Galactic cosmic-ray proton (CRp) density. We model the diffuse gamma-ray emission arising from inelastic hadronic interactions between CRps and interstellar gas. Using a map of dust-correlated diffuse gamma-ray emission based on ten years of Fermi-LAT observations together with a three-dimensional gas density model, we infer the spatial CRp distribution through a morphological matching approach. The logarithmic CRp density field is described by a Gaussian process defined on a spherical-times-radial grid, while both the field and its correlation structure are inferred simultaneously using Iterative Charted Refinement. The posterior distribution of the reconstructed 3D CRp density field is approximated using geometric variational inference. The reconstructed CRp density exhibits a smooth but spatially structured distribution with a limited dynamical range across the Galactic disk. We find a moderate enhancement of the CRp density toward the inner Galaxy. The inferred normalization at the Solar position is consistent with local CR measurements by the AMS-02 instrument.

astro-ph.HE↗

arXiv2026

A Bayesian Method for Air-Shower Reconstruction using Information Field Theory

The radio detection of extensive air showers provides a powerful method for studying the origin of high-energy cosmic rays. The Low-Frequency Array (LOFAR) offers unprecedentedly detailed measurements of the radio emission footprint. However, fully exploiting this information requires advanced reconstruction techniques. In this paper, we introduce a novel framework for air shower reconstruction based on Bayesian inference and Information Field Theory (IFT). Our method is built on a fully differentiable forward model of the radio signal, which incorporates a physical emission parameterization and a precise wavefront model. Additionally, we augment this physical model with Gaussian processes to account for systematic uncertainties in both the signal fluence and arrival timing. By leveraging gradient information, our approach enables efficient (three orders of magnitude acceleration w.r.t.\ the legacy method) and robust inference of the underlying physical shower parameters, such as primary energy and the depth of shower maximum, $X_\text{max}$. This work provides not only point estimates but also a rigorous quantification of uncertainties. We achieve a resolution in $X_\text{max}$ of $25\,\mathrm{g/cm^2}$ and a radiation energy resolution of $12\%$ on simulations for LOFAR.

astro-ph.HE↗

arXiv2025

The superclouds of the local Milky Way

Recent 3D dust maps of the local Milky Way are revolutionizing our understanding of the Sun's Galactic neighborhood, providing much needed insight into the large-scale organization of the interstellar medium. Focusing on the largest scales in $\textit{Gaia}$-based 3D dust maps, we find a pattern of seven highly elongated, mostly parallel structures in the local $\sim 5\,\mathrm{kpc}^2$, five of which were previously unknown. These structures show pitch angles of $33.5 \pm 4.0 ^\circ$ and masses ranging from $10^5$ to $10^6$ $\mathrm{M}_\odot$. We refer to these structures as superclouds. Nearly all known star-forming regions in the solar neighborhood lie within the superclouds, primarily along their central axes, supporting the idea that they act as gas reservoirs for the formation of giant molecular clouds. All but one of the seven superclouds show an underlying undulation, indicating that this is not a property unique to the Radcliffe Wave. We find that while the superclouds have linear masses that vary by about a factor of 4, their volume densities only vary by about 10$\%$. This suggests that superclouds self-regulate their physical sizes and internal structure to maintain pressure equilibrium with their environment. These findings establish a new framework for understanding how large-scale Galactic structures shape the conditions for star formation in the solar vicinity, and likely in galaxies like the Milky Way.

astro-ph.GA↗

arXiv2025

aim-resolve: Automatic Identification and Modeling for Bayesian Radio Interferometric Imaging

Modern radio interferometers deliver large volumes of data containing high-sensitivity sky maps over wide fields-of-view. These large area observations can contain various and superposed structures such as point sources, extended objects, and large-scale diffuse emission. To fully realize the potential of these observations, it is crucial to build appropriate sky emission models which separate and reconstruct the underlying astrophysical components. We introduce aim-resolve, an automatic and iterative method that combines the Bayesian imaging algorithm resolve with deep learning and clustering algorithms in order to jointly solve the reconstruction and source extraction problem. The method identifies and models different astrophysical components in radio observations while providing uncertainty quantification of the results. By using different model descriptions for point sources, extended objects, and diffuse background emission, the method efficiently separates the individual components and improves the overall reconstruction. We demonstrate the effectiveness of this method on synthetic image data containing multiple different sources. We further show the application of aim-resolve to an L-band (856 - 1712 MHz) MeerKAT observation of the radio galaxy ESO 137-006 and other radio galaxies in that environment. We observe a reasonable object identification for both applications, yielding a clean separation of the individual components and precise reconstructions of point sources and extended objects along with detailed uncertainty quantification. In particular, the method enables the creation of catalogs containing source positions and brightnesses and the corresponding uncertainties. The full decoupling of sky emission model and instrument response makes the method applicable to a wide variety of instruments or wavelength bands.

astro-ph.IM↗

arXiv2025

MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python

Origins of life research investigates how life could emerge from prebiotic chemistry only. One possible explanation provides the RNA world hypothesis. It states that life could emerge from RNA strands only, storing and transferring biological information, as well as catalyzing reactions as ribozymes. Before this state could have emerged, however, the prebiotic world was probably a purely chemical pool of short RNA strands with random sequences and without biological function performing hybridization and dehybridization, as well as ligation and cleavage. In this context relevant questions are what are the conditions that allow longer RNA strands to be built and how can information carrying in RNA sequence emerge? In order to investigate such RNA reactors, efficient simulations are needed because the space of possible RNA sequences increases exponentially with the length of the strands, as well as the number of reactions between two strands. In addition, simulations have to be compared to experimental data for validation and parameter calibration. Here, we present the MoRSAIK python package for sequence motif (or k-mer) reactor simulation, analysis and inference. It enables users to simulate RNA sequence motif dynamics in the mean field approximation as well as to infer the reaction parameters from data with Bayesian methods and to analyze results by computing observables and plotting. MoRSAIK simulates an RNA reactor by following the reactions and the concentrations of all strands inside up to a certain length (of four nucleotides by default). Longer strands are followed indirectly, by tracking the concentrations of their containing sequence motifs of that maximum length.

physics.bio-ph↗

arXiv2025

Spatially Coherent 3D Distributions of HI and CO in the Milky Way

The spatial distribution of the gaseous components of the Milky Way is of great importance for a number of different fields, for example, Galactic structure, star formation, and cosmic rays. However, obtaining distance information to gaseous clouds in the interstellar medium from Doppler-shifted line emission is notoriously difficult given our vantage point in the Galaxy. It requires spatial knowledge of gas velocities and generally suffers from distance ambiguities. Previous works often assumed the optically thin limit, had a fixed velocity field, and lacked resolution overall. We aim to overcome these issues and improve previous reconstructions of the gaseous constituents of the interstellar medium of the Galaxy. We used 3D Gaussian processes to model correlations in the interstellar medium, including correlations between different lines of sight, and enforce a spatially coherent structure in the prior. For modelling the transport of radiation from the emitting gas to us as observers, we took absorption effects into account. A special numerical grid ensures that there is high resolution nearby. We inferred the spatial distributions of atomic hydrogen (HI), carbon monoxide (CO), their emission line widths, and the Galactic velocity field in a joint Bayesian inference. We further constrained these fields with complementary data from Galactic masers and young stellar object clusters. Our main result consists of a set of samples that implicitly contain statistical uncertainties. The resulting maps are spatially coherent and reproduce the data with high fidelity. We confirm previous findings regarding the warping and flaring of the Galactic disc. A comparison with 3D dust maps reveals a good agreement on scales larger than approximately 400 pc. While our results are not free of artefacts, they present a big step forward in obtaining high-quality 3D maps of the interstellar medium.

astro-ph.GA↗

arXiv2025

The influence of the 3D Galactic gas structure on cosmic-ray transport and $γ$-ray emission

Cosmic rays (CRs) play a major role in the dynamics of the interstellar medium (ISM). Their interactions and transport ionize, heat, and push the ISM thereby coupling different regions of it. The spatial distribution of CRs depends on the distribution of their sources as well as the ISM constituents they interact with, such as gas, starlight, and magnetic fields. Particularly, gas influences CR fluxes and $γ$-ray emission. We illustrate the influence of realistic and largely structured 3D gas distributions on CR transport and $γ$-ray emission, by studying their correlation using the PICARD code and multiple samples of recent 3D reconstructions of the HI and H2 Galactic gas constituents. We adjust the diffusion coefficient $D_{xx}$ and Alfvén speed $v_{\text{A}}$ to reproduce local measurements of B/C abundances and find that these parameters depend non-linearly on the local distribution of gas. When simulating CR transport, the distributions of CR fluxes exhibit energy-dependent structures that vary for all CR species due to their corresponding loss processes. Regions of enhanced secondary (primary) species are spatially correlated (anti-correlated) with the gas density. Furthermore, we show that the morphology of gas clouds alone impacts CR flux predictions. For $γ$-ray emission, we observe a high sensitivity of the $γ$-ray emissivities to gas structures, as these determine the spatial distributions of hadronic interactions and bremsstrahlung. This way, we have for the first time calculated how well-defined uncertainties in a structured gas model propagate to CR transport and $γ$-ray emission.

astro-ph.HE↗

arXiv2025

Bayesian Imaging of Interferometric Data from Polarized Electromagnetic Signals

We present an imaging algorithm for polarimetric interferometric data from radio telescopes. It is based on Bayesian statistics and thereby able to provide uncertainties and to incorporate prior information such as positivity of the total emission (Stokes I) or consistency constraints (polarized fraction can only be between 0% and 100%). By comparing our results to the output of the de-facto standard algorithm called CLEAN, we show that these constraints paired with a consistent treatment of measurement uncertainties throughout the algorithm significantly improve image quality. In particular, our method reveals that depolarization canals in CLEAN images do not necessarily indicate a true absence of polarized emission, e.g., after frequency averaging, but can also stem from uncertainty in the polarization direction. This demonstrates that our Bayesian approach can distinguish between true depolarization and mere uncertainty, providing a more informative representation of polarization structures.

astro-ph.IM↗

arXiv2024

Filaments in and between galaxy clusters at low and mid-frequency with the SKA telescope

Understanding the magnetised Universe is a major challenge in modern astrophysics, and cosmic magnetism has been acknowledged as one of the science key drivers of the most ambitious radio instrument ever planned, the SKA telescope. With this work, we aim to investigate the potential of the SKA telescope and its precursors and pathfinders in the study of magnetic fields in galaxy clusters and filaments through diffuse synchrotron radio emission. Galaxy clusters and filaments of the cosmic web are indeed unique laboratories to investigate turbulent fluid motions and large-scale magnetic fields in action and much of what is known about magnetic fields in galaxy clusters comes from sensitive radio observations. Based on cosmological MHD simulations, we predict radio properties (total intensity and polarisation) of a pair of galaxy clusters connected by a cosmic-web filament. We use our theoretical expectations to explore the potential of polarimetric observations to study large-scale structure magnetic fields in the frequency ranges 50-350MHz and 950-1760MHz. We also present predictions for galaxy cluster polarimetric observations with the SKA precursors and pathfinders (LOFAR2.0 and MeerKAT+). Our findings point out that polarisation observations are particularly powerful for the study of large-scale magnetic fields, since they are not significantly affected by confusion noise. The unprecedented sensitivity and spatial resolution of the intermediate frequency radio telescopes make them the favourite instruments for the study of these sources through polarimetric data, potentially allowing us to understand if the energy density of relativistic electrons is in equipartition with the magnetic field or coupled with the thermal gas density. Our results show that low frequency instruments represent as well a precious tool to study diffuse synchrotron emission in total intensity and polarisation.

astro-ph.CO↗

arXiv2024

J-UBIK: The JAX-accelerated Universal Bayesian Imaging Kit

Many advances in astronomy and astrophysics originate from accurate images of the sky emission across multiple wavelengths. This often requires reconstructing spatially and spectrally correlated signals detected from multiple instruments. To facilitate the high-fidelity imaging of these signals, we introduce the universal Bayesian imaging kit (UBIK). Specifically, we present J-UBIK, a flexible and modular implementation leveraging the JAX-accelerated NIFTy.re software as its backend. J-UBIK streamlines the implementation of the key Bayesian inference components, providing for all the necessary steps of Bayesian imaging pipelines. First, it provides adaptable prior models for different sky realizations. Second, it includes likelihood models tailored to specific instruments. So far, the package includes three instruments: Chandra and eROSITA for X-ray observations, and the James Webb Space Telescope (JWST) for the near- and mid-infrared. The aim is to expand this set in the future. Third, these models can be integrated with various inference and optimization schemes, such as maximum a posteriori estimation and variational inference. Explicit demos show how to integrate the individual modules into a full analysis pipeline. Overall, J-UBIK enables efficient generation of high-fidelity images via Bayesian pipelines that can be tailored to specific research objectives.

astro-ph.IM↗

arXiv2024

Disentangling the Faraday rotation sky

Magnetic fields permeate the diffuse interstellar medium (ISM) of the Milky Way, and are essential to explain the dynamical evolution and current shape of the Galaxy. Magnetic fields reveal themselves via their influence on the surrounding matter, and as such are notoriously hard to measure independently of other tracers. In this work, we attempt to disentangle an all sky map of the line-of-sight parallel component of the Galactic magnetic field from the Faraday effect, utilizing several tracers of the Galactic thermal electron density. Additionally, we aim to produce a Galactic electron dispersion measure map and quantify several tracers of the structure of the ionized medium of the Milky Way. We rely on compiled catalogs of extragalactic Faraday rotation measures and Galactic pulsar dispersion measures, a well as data on bremsstrahlung and the hydrogen $α$ spectral line to trace the ionized medium of the Milky Way. We present the first full sky map of the line-of-sight averaged Galactic magnetic field. Within this map, we find LoS parallel and LoS-averaged magnetic field strengths of up to 4 $μ$G, with an all-sky root-mean-square of 1.1 $μ$G, which is consistent with previous local measurements and global magnetic field models. Additionally, we produce a detailed electron dispersion measure map, which agrees with already existing parametric models at high latitudes, but suffers from systematic effects in the disk. Further analysis of our results with regard to the 3D structure of $n_{th}$ reveals that it follows a Kolmogorov-type turbulence for most of the sky. From the reconstructed dispersion measure and emission measure maps we construct several tracers of variability of $n_{th}$ along the LoS.

astro-ph.GA↗

arXiv2024

The "C": The large Chameleon-Musca-Coalsack cloud

Recent advancements in 3D dust mapping have transformed our understanding of the Milky Way's local interstellar medium, enabling us to explore its structure in three spatial dimensions for the first time. In this Letter, we use the most recent 3D dust map by Edenhofer et al. to study the well-known Chameleon, Musca, and Coalsack cloud complexes, located about 200 pc from the Sun. We find that these three complexes are not isolated but rather connect to form a surprisingly well-defined half-ring, constituting a single C-shaped cloud with a radius of about 50 pc, a thickness of about 45 pc, and a total mass of about $5 \times 10^{4} \mathrm{M}_{\odot}$, or $9 \times 10^{4} \mathrm{M}_{\odot}$ if including everything in the vicinity of the C-shaped cloud. Despite the absence of an evident feedback source at its center, the dynamics of young stellar clusters associated with the C structure suggest that a single supernova explosion about 4 Myr to 10 Myr ago likely shaped this structure. Our findings support a single origin story for these cloud complexes, suggesting that they were formed by feedback-driven gas compression, and offer new insights into the processes that govern the birth of star-forming clouds in feedback-dominated regions, such as the Scorpius-Centaurus association.

astro-ph.GA↗

arXiv2024

Re-Envisioning Numerical Information Field Theory (NIFTy.re): A Library for Gaussian Processes and Variational Inference

Imaging is the process of transforming noisy, incomplete data into a space that humans can interpret. NIFTy is a Bayesian framework for imaging and has already successfully been applied to many fields in astrophysics. Previous design decisions held the performance and the development of methods in NIFTy back. We present a rewrite of NIFTy, coined NIFTy.re, which reworks the modeling principle, extends the inference strategies, and outsources much of the heavy lifting to JAX. The rewrite dramatically accelerates models written in NIFTy, lays the foundation for new types of inference machineries, improves maintainability, and enables interoperability between NIFTy and the JAX machine learning ecosystem.

astro-ph.IM↗

arXiv2024

fast-resolve: Fast Bayesian Radio Interferometric Imaging

Context: Interferometric imaging is algorithmically and computationally challenging as there is no unique inversion from the measurement data back to the sky maps, and the datasets can be very large. Many imaging methods already exist, but most of them focus either on the accuracy or the computational aspect. Aims: This paper aims to reduce the computational complexity of the Bayesian imaging algorithm resolve, enabling the application of Bayesian imaging for larger datasets. Methods: By combining computational shortcuts of the CLEAN algorithm with the Bayesian imaging algorithm resolve we developed an accurate and fast imaging algorithm which we name fast-resolve. Results: We validate the accuracy of the presented fast-resolve algorithm by comparing it with results from resolve on VLA Cygnus A data. Furthermore, we demonstrate the computational advantages of fast-resolve on a large MeerKAT ESO 137-006 dataset which is computationally out of reach for resolve. Conclusions: The presented algorithm is significantly faster than previous Bayesian imaging algorithms, broadening the applicability of Bayesian interferometric imaging. Specifically for the single channel VLA Cygnus A datasets fast-resolve is about $144$ times faster than resolve. For the MeerKAT dataset with multiple channels the computational speedup of fast-resolve is even larger.

astro-ph.IM↗

arXiv2023

Multi-Component Imaging of the Fermi Gamma-ray Sky in the Spatio-spectral Domain

We perform two distinct spatio-spectral reconstructions of the gamma-ray sky in the range of 0.56-316 GeV based on Fermi Large Area Telescope (LAT) data. Both describe the sky brightness to be composed of a diffuse-emission and a point-source component. The first model requires minimal assumptions and provides a template-free reconstruction as a reference. It makes use of spatial and spectral correlations to distinguish between the different components. The second model is physics-informed and further differentiates between diffuse emission of hadronic and leptonic origin. For this, we assume parametric, but spatially varying energy spectra to distinguish between the processes and use thermal Galactic dust observations to indicate the preferred sites of hadronic interactions. To account for instrumental effects we model the point-spread, the energy dispersion, and the exposure of the telescope throughout the observation. The reconstruction problem is formulated as a Bayesian inference task, that is solved by variational inference. We show decompositions of the Gamma-ray flux into diffuse and point-like emissions, and of the diffuse emissions into multiple physically motivated components. The diffuse decomposition provides an unprecedented view of the Galactic leptonic diffuse emission. It shows the Fermi bubbles and their spectral variations in high fidelity and other areas exhibiting strong cosmic ray electron contents, such as a thick disk in the inner Galaxy and outflow regions. Furthermore, we report a hard spectrum gamma ray arc in the northern outer bubble co-spatial with the reported X-ray arc by the eROSITA collaboration. All our spatio-spectral sky reconstructions and their uncertainty quantification are publicly available.

astro-ph.HE↗