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The EDR and eRASS1 data have already revealed a remarkable number of undiscovered X-ray sources. Using Bayesian inference and generative modeling techniques for X-ray imaging, we aim to increase the sensitivity and scientific value of these observations by denoising, deconvolving, and decomposing the X-ray sky. Leveraging information field theory, we can exploit the spatial and spectral correlation structures of the different physical components of the sky with non-parametric priors to enhance the image reconstruction. By incorporating instrumental effects into the forward model, we develop a comprehensive Bayesian imaging algorithm for eROSITA pointing observations. Finally, we apply the developed algorithm to EDR data of the LMC SN1987A, fusing data sets from observations made by five different telescope modules. The final result is a denoised, deconvolved, and decomposed view of the LMC, which enables the analysis of its fine-scale structures, the creation of point source catalogues of this region, and enhanced calibration for future work.

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., Comment: Accepted for publication on A&A, 12 pages

Reconstructing the electric field from the measured voltages in an antenna, unfolding the antenna response, comes with several problems. Due to the noisiness of the signal it is often necessary to disregard part of the bandwidth of the antenna. It is also not guaranteed, that this system of equations can be inverted at all. In any case, the noise of the measurement will be converted into noise on the electric field. This could be solved by Bayesian inference, however, the electric field is continuous, which would lead to an infinite-dimensional latent space. Information field theory (IFT) has been developed to deal with this problem and allow for Bayesian reasoning on fields. It provides a theoretical backbone and effective tools to approach the inference as a discrete problem in the continuum limit, taking the continuous nature of fields into account. We will present a first working signal model that can be used with IFT-based inference algorithms, which can successfully reconstruct the electric field. The model is based on the current understanding of air shower emission physics, modelling geomagnetic and charge-excess emission and their respective polarisation and spectra separately. Since Bayesian inference provides the posterior distribution, this method also gives an estimate on the uncertainty of the measured field. The performance of this method will be demonstrated with Monte-Carlo simulations of air shower radio signals., Comment: PoS(ARENA2024)

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.

The galaxy M87 is one of the prime targets for high resolution radio imaging pursuing the ringlike shadow of its supermassive black hole, the innermost regions of accretion flow, and the formation of the relativistic jet. However, it remains challenging to observe both jointly. Only recently, global mm-VLBI array (GMVA)+ALMA observations at 86 GHz in 2018 were able to reconstruct the M87 black hole shadow and the extended jet emission simultaneously. In order to analyze the ring and jet of M87, conventional CLEAN algorithms were mainly employed alongside the RML method SMILI in the previous work. To test the robustness of the reconstructed structures of M87 GMVA+ALMA observations at 86GHz, we estimate the ring diameter, width, and the extended jet emission with the possible central spine by two different novel imaging algorithms: resolve and DoG-HiT. Overall reconstructions are consistent with the results reported in the previous paper. The ring structure of the M87 is resolved at higher resolution and the posterior distribution of M87 ring features is explored. The resolve images show that the ring diameter is 60.9 +- 2.2 muas and width is 16.0 +- 0.9 muas. The ring diameter is 61.0 muas and width is 20.6 muas by DoG-HiT. The ring diameter is therefore in agreement with the estimation (64+4-8 muas) by SMILI and the geometrical modeling. Two bright spots in the ring are reconstructed by four independent imaging methods, the substructure in the ring is therefore most likely originated from the data. A consistent limb-brightened jet structure is reconstructed by resolve and DoG-HiT, albeit with a less pronounced central spine. Modern data-driven imaging methods confirm the ring and jet structure in M87, complementing traditional VLBI methods with novel perspectives on the significance of recovered features. They confirm the result of the previous report., Comment: submitted to A&A

Nested sampling (NS) is a stochastic method for computing the log-evidence of a Bayesian problem. It relies on stochastic estimates of prior volumes enclosed by likelihood contours, which limits the accuracy of the log-evidence calculation. We propose to transform the prior volume estimation into a Bayesian inference problem, which allows us to incorporate a smoothness assumption for likelihood-prior volume relations. As a result, we aim to increase the accuracy of the volume estimates and thus improve the overall log-evidence calculation using NS. The method presented works as a post-processing step for NS and provides posterior samples of the likelihood-prior-volume relation, from which the log-evidence can be calculated. We demonstrate an implementation of the algorithm and compare its results with plain NS on two synthetic datasets for which the underlying evidence is known. We find a significant improvement in accuracy for runs with less than one hundred active samples in NS, but are prone to numerical problems beyond this point.

Faraday rotation contains information about the magnetic field structure along the line of sight and is an important instrument in the study of cosmic magnetism. Traditional Faraday spectrum deconvolution methods such as RMCLEAN face challenges in resolving complex Faraday dispersion functions and handling large datasets. We develop a deep learning deconvolution model to enhance the accuracy and efficiency of extracting Faraday rotation measures from radio astronomical data, specifically targeting data from the MeerKAT Galaxy Cluster Legacy Survey (MGCLS). We use semi-supervised learning, where the model simultaneously recreates the data and minimizes the difference between the output and the true signal of synthetic data. Performance comparisons with RMCLEAN were conducted on simulated as well as real data for the galaxy cluster Abell 3376. Our semi-supervised model is able to recover the Faraday dispersion with great accuracy, particularly for complex or high-RM signals, maintaining sensitivity across a broad RM range. The computational efficiency of this method is significantly improved over traditional methods. Applied to observations of Abell 3376, we find detailed magnetic field structures in the radio relics, and several AGN. We also apply our model to MeerKAT data of Abell 85, Abell 168, Abell 194, Abell 3186 and Abell 3667., Comment: 20 pages, 20 figures, submitted to Astronomy & Astrophysics

Self-calibration methods with the CLEAN algorithm have been widely employed in Very Long Baseline Interferometry (VLBI) data processing in order to correct antenna-based amplitude and phase corruptions present in the data. However, human interaction during the conventional CLEAN self-calibration process can impose a strong effective prior, which in turn may produce artifacts within the final image and hinder the reproducibility of final results. In this work, we aim to demonstrate a combined self-calibration and imaging method for VLBI data in a Bayesian inference framework. The method corrects for amplitude and phase gains for each antenna and polarization mode by inferring the temporal correlation of the gain solutions. We use Stokes I data of M87 taken with the Very Long Baseline Array (VLBA) at 43GHz, pre-calibrated using the rPICARD CASA-based pipeline. For antenna-based gain calibration and imaging, we use the Bayesian imaging software resolve. To estimate gain and image uncertainties, we use a Variational Inference method. We obtain a high-resolution M87 Stokes I image at 43GHz in conjunction with antenna-based gain solutions using our Bayesian self-calibration and imaging method. The core with counter-jet structure is better resolved, and extended jet emission is better described compared to the CLEAN reconstruction. Furthermore, uncertainty estimation of the image and antenna-based gains allows us to quantify the reliability of the result. Our Bayesian self-calibration and imaging method is able to reconstruct robust and reproducible Stokes I images and gain solutions with uncertainty estimation by taking into account the uncertainty information in the data., Comment: accepted for publication in A&A

The spatial distribution of the gaseous components of the Milky Way is of great importance for a number of different fields, e.g. 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 unique vantage point in the Galaxy. It requires precise knowledge of gas velocities and generally suffers from distance ambiguities. Previous works often assumed the optically thin limit (no absorption), a fixed velocity field, and lack resolution overall. We aim to overcome these issues and improve previous reconstructions of the gaseous constituents of the interstellar medium of the Galaxy. We use 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 take absorption effects into account. A special numerical grid ensures high resolution nearby. We infer the spatial distributions of HI, CO, their emission line-widths, and the Galactic velocity field in a joint Bayesian inference. We further constrain 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., Comment: 21 pages, 22 figures

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 interacts closely with CRs, influencing CR fluxes and gamma -ray emission. We illustrate the influence of 3D gas structures on CR transport and gamma -ray emission. We use the PICARD code and multiple samples of recent 3D reconstructions of the HI and H$_2$ Galactic gas constituents to investigate the impact on the transport of CRs and emission of gamma -rays. We find the necessary transport parameters to reproduce local measurements of CR fluxes, and see that they depend on the local distribution of gas density and structure. The distribution of CR fluxes exhibits 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. We observe a high sensitivity of the gamma -ray emission on the contrast of gas structures, as those determine the 3D spatial distributions of hadronic interactions and bremsstrahlung. We find that corresponding gas-induced structures in the distribution of CR electrons are also visible in Inverse Compton (IC) emission. Due to the aforementioned sensitivity, the analysis of CR data for CR sources and transport parameters requires the usage of accurate 3D gas maps.

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., Comment: 24 pages, 14 figures

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., Comment: 6 pages, 3 figures, to be published in Astronomy & Astrophysics (A&A)

(abridged) Ultra-high energy cosmic rays (UHECRs) are extremely energetic charged particles with energies surpassing $10^{18}$ eV. Their sources remain elusive, obscured by deflections caused by the Galactic magnetic field (GMF). This challenge is further complicated by our limited understanding of the three-dimensional structure of the GMF, as current GMF observations consist primarily of quantities integrated along the line-of-sight (LOS). Nevertheless, data from upcoming stellar polarisation surveys along with Gaia's stellar parallax data are expected to yield local GMF measurements.. In this work, we employ methods of Bayesian statistical inference in order to sample the posterior distribution of the GMF within part of the Galaxy. By assuming a known rigidity and arrival direction of an UHECR, we backtrack its trajectory through various GMF configurations drawn from the posterior distribution. Our objective is to rigorously evaluate our algorithm's performance in scenarios that closely mirror the setting of expected future applications. In pursuit of this, we condition the posterior to synthetic integrated LOS measurements of the GMF, in addition to synthetic local POS-component measurements. In this proof of concept work, we assume the ground truth to be a magnetic field produced by a dynamo simulation of the Galactic ISM. Our results demonstrate that for all locations of the observed arrival direction on the POS, our algorithm is able to substantially update our knowledge on the original arrival direction of UHECRs with rigidity $E/Z = 5 \times 10^{19}$ eV, even in the case of complete absence of LOS information. If integrated data is included in the inference, then the regions of the celestial sphere where the maximum error occurs diminishes greatly. Even in those regions the maximum error is diminished by a factor of about $3$ in the specific setting studied., Comment: 11 pages, 7 figures

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., Comment: 10 pages, 2 figures, published in Journal of Open Source Software (JOSS)