arXiv subjects

Torsten Enßlin

Publications and source records attributed to Torsten Enßlin.

At least 37 records · Page 2Linked to original sources

arXiv2025

Quantifying imperfect cognition via achieved information gain

Cognition, information processing in form of inference, communication, and memorization, is the central activity of any intelligence. Its physical realization in a brain, computer, or in any other intelligent system requires resources like time, energy, memory, bandwidth, money, and others. Due to limited resources, many real world intelligent systems perform only imperfect cognition. To understand the trade-off between accuracy and resource investments in existing systems, e.g. in biology, as well as for the resource-aware optimal design of information processing systems, like computer algorithms and artificial neural networks, a quantification of information obtained in an imperfect cognitive operation is desirable. To this end, we propose the concept of the achieved information gain (AIG) of a belief update, which is given by the amount of information obtained by updating from the initial state of knowledge to the ideal state, minus the amount that a change from the imperfect to the ideal state would yield. AIG has many desirable properties for quantifying imperfect cognition. The ratio of achieved to ideally obtainable information measures cognitive fidelity and that of AIG to the necessary cognitive effort measures cognitive efficiency. We provide an axiomatic derivation of AIG, relate it to other information measures, illustrate its application to common scenarios of posterior inaccuracies, and discuss the implication of cognitive efficiency for sustainable resource allocation in computational inference.

cs.IT↗

arXiv2025

The Polarisation Sky Survey of the Universe's Magnetism (POSSUM): Science Goals and Survey Description

The Australian SKA Pathfinder (ASKAP) offers powerful new capabilities for studying the polarised and magnetised Universe at radio wavelengths. In this paper, we introduce the Polarisation Sky Survey of the Universe's Magnetism (POSSUM), a groundbreaking survey with three primary objectives: (1) to create a comprehensive Faraday rotation measure (RM) grid of up to one million compact extragalactic sources across the southern ~50 per cent of the sky (20,630 deg$^2$); (2) to map the intrinsic polarisation and RM properties of a wide range of discrete extragalactic and Galactic objects over the same area; and (3) to contribute interferometric data with excellent surface brightness sensitivity, which can be combined with single-dish data to study the diffuse Galactic interstellar medium. Observations for the full POSSUM survey commenced in May 2023 and are expected to conclude by mid-2028. POSSUM will achieve an RM grid density of around 30-50 RMs per square degree with a median measurement uncertainty of ~1 rad m$^{-2}$. The survey operates primarily over a frequency range of 800-1088 MHz, with an angular resolution of 20'' and a typical RMS sensitivity in Stokes $Q$ or $U$ of 18 $μ$Jy beam$^{-1}$. Additionally, the survey will be supplemented by similar observations covering 1296-1440 MHz over 38 per cent of the sky. POSSUM will enable the discovery and detailed investigation of magnetised phenomena in a wide range of cosmic environments, as well as the interplay between these components. This paper reviews the current science case developed by the POSSUM Collaboration and provides an overview of POSSUM's observations, data processing, outputs, and its complementarity with other radio and multi-wavelength surveys, including future work with the SKA. [Abstract abridged]

astro-ph.GA↗

arXiv2025

Direction-Dependent Faraday Synthesis

Modern radio interferometers enable high-resolution polarization imaging, offering insights into cosmic magnetism through Rotation Measure (RM) synthesis. Traditional 2+1D RM synthesis treats the 2D spatial and 1D spectral transforms separately. A fully 3D approach transforms data directly from visibility-frequency space to sky-Faraday depth space using a 3D Fourier transform. Faraday synthesis uses the full dataset for improved reconstruction but requires a 3D deconvolution algorithm to subtract artifacts from the residual image. Applying this method to modern interferometers also requires corrections for direction-dependent effects (DDEs). We extend Faraday synthesis by incorporating DDE corrections, enabling accurate polarized imaging in the presence of instrumental and ionospheric effects. We implement this method within DDFACET, introducing a direction-dependent deconvolution algorithm (DDFSCLEAN) that applies DDE corrections in a faceted framework. Additionally, we parameterize the CLEAN components and evaluate the model over a larger set of frequency channels, naturally correcting for bandwidth depolarization. The method is tested on both synthetic and real data. Our results show that Faraday synthesis enables deeper deconvolution, reduces artifacts, and increases dynamic range. The depolarization correction improves recovery of polarized flux, allowing coarser frequency resolution without loss of sensitivity at high Faraday depths. From the 3D reconstruction, we identify a polarized source in a LOFAR Surveys pointing not detected by earlier RM surveys. Faraday synthesis is memory-intensive due to the large transforms between the visibility domain and the Faraday cube, and is only now becoming practical. Nevertheless, our implementation achieves comparable or faster runtimes than the 2+1D approach, making it a competitive alternative for polarization imaging.

astro-ph.IM↗

arXiv2025

Exploring magnetised galactic outflows in starburst dwarf galaxies NGC 3125 and IC 4662

The study of radio emission in starburst dwarf galaxies provides a unique opportunity to investigate the mechanisms responsible for the amplification and transport of magnetic fields. Local dwarfs are often considered proxies for early-Universe galaxies, so this study may provide insights into the role of non-thermal components in the formation and evolution of larger galaxies. By investigating the radio continuum spectra and maps of the starburst dwarf galaxies, we aim to draw conclusions on their magnetic field strengths and configurations, as well as the dynamics of cosmic ray (CR) transport. We perform a radio continuum polarimetry study of two of the brightest starburst IRAS Revised Bright Galaxy Sample (RBGS) dwarf galaxies, NGC 3125 and IC 4662. By combining data of the Australian Telescope Compact Array (2.1 GHz) and MeerKAT (1.28 GHz), we analyse the underlying emission mechanism and the CR transport in these systems. We find flat spectra in those dwarf galaxies over the entire investigated frequency range, which sharply contrasts with observations of massive spiral galaxies. Because the expected cooling time of CR electrons is much shorter than their escape time, we would expect a steepened steady-state CR electron spectrum. The flat observed spectra suggest a substantial contribution from free-free emission at high frequencies and absorption at low frequencies, which may solve this puzzle. For NGC 3125, we measure a degree of polarisation between 0.75% and 2.6%, implying a turbulent field and supporting the picture of a comparably large thermal emission component that could be sourced by stellar radiation feedback and supernovae.

astro-ph.GA↗

arXiv2024

Electric Field Reconstruction with Information Field Theory

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.

physics.data-an↗

arXiv2024

Towards a Field Based Bayesian Evidence Inference from Nested Sampling Data

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.

physics.comp-ph↗

arXiv2024

Semi-Supervised Rotation Measure Deconvolution and its application to MeerKAT observations of galaxy clusters

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.

astro-ph.CO↗

arXiv2024

Non-parametric Bayesian reconstruction of Galactic magnetic fields using Information Field Theory: The inclusion of line-of-sight information in ultra-high energy cosmic ray backtracking

(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.

astro-ph.HE↗

arXiv2024

A parsec-scale Galactic 3D dust map out to 1.25 kpc from the Sun

High-resolution 3D maps of interstellar dust are critical for probing the underlying physics shaping the structure of the interstellar medium, and for foreground correction of astrophysical observations affected by dust. We aim to construct a new 3D map of the spatial distribution of interstellar dust extinction out to a distance of 1.25 kpc from the Sun. We leveraged distance and extinction estimates to 54 million nearby stars derived from the Gaia BP/RP spectra. Using the stellar distance and extinction information, we inferred the spatial distribution of dust extinction. We modeled the logarithmic dust extinction with a Gaussian process in a spherical coordinate system via iterative charted refinement and a correlation kernel inferred in previous work. In total, our posterior has over 661 million degrees of freedom. We probed the posterior distribution using the variational inference method MGVI. Our 3D dust map has an angular resolution of up to 14' (Nside = 256), and we achieve parsec-scale distance resolution, sampling the dust in 516 logarithmically spaced distance bins spanning 69 pc to 1250 pc. We generated 12 samples from the variational posterior of the 3D dust distribution and release the samples alongside the mean 3D dust map and its corresponding uncertainty. Our map resolves the internal structure of hundreds of molecular clouds in the solar neighborhood and will be broadly useful for studies of star formation, Galactic structure, and young stellar populations. It is available for download in a variety of coordinate systems online and can also be queried via the publicly available dustmaps Python package.

astro-ph.GA↗

arXiv2024

Attention to Entropic Communication

The concept of attention, numerical weights that emphasize the importance of particular data, has proven to be very relevant in artificial intelligence. Relative entropy (RE, aka Kullback-Leibler divergence) plays a central role in communication theory. Here we combine these concepts, attention and RE. RE guides optimal encoding of messages in bandwidth-limited communication as well as optimal message decoding via the maximum entropy principle (MEP). In the coding scenario, RE can be derived from four requirements, namely being analytical, local, proper, and calibrated. Weighted RE, used for attention steering in communications, turns out to be improper. To see how proper attention communication can emerge, we analyze a scenario of a message sender who wants to ensure that the receiver of the message can perform well-informed actions. If the receiver decodes the message using the MEP, the sender only needs to know the receiver's utility function to inform optimally, but not the receiver's initial knowledge state. In case only the curvature of the utility function maxima are known, it becomes desirable to accurately communicate an attention function, in this case a by this curvature weighted and re-normalized probability function. Entropic attention communication is here proposed as the desired generalization of entropic communication that permits weighting while being proper, thereby aiding the design of optimal communication protocols in technical applications and helping to understand human communication. For example, our analysis shows how to derive the level of cooperation expected under misaligned interests of otherwise honest communication partners.

cs.IT↗

arXiv2023

Inferring Evidence from Nested Sampling Data via Information Field Theory

Nested sampling provides an estimate of the evidence of a Bayesian inference problem via probing the likelihood as a function of the enclosed prior volume. However, the lack of precise values of the enclosed prior mass of the samples introduces probing noise, which can hamper high-accuracy determinations of the evidence values as estimated from the likelihood-prior-volume function. We introduce an approach based on information field theory, a framework for non-parametric function reconstruction from data, that infers the likelihood-prior-volume function by exploiting its smoothness and thereby aims to improve the evidence calculation. Our method provides posterior samples of the likelihood-prior-volume function that translate into a quantification of the remaining sampling noise for the evidence estimate, or for any other quantity derived from the likelihood-prior-volume function.

physics.comp-ph↗

arXiv2023

First spatio-spectral Bayesian imaging of SN1006 in X-ray

Supernovae are an important source of energy in the interstellar medium. Young remnants of supernovae have a peak emission in the X-ray region, making them interesting objects for X-ray observations. In particular, the supernova remnant SN1006 is of great interest due to its historical record, proximity and brightness. It has therefore been studied by several X-ray telescopes. Improving the X-ray imaging of this and other remnants is important but challenging as it requires to address a spatially varying instrument response in order to achieve a high signal-to-noise ratio. Here, we use Chandra observations to demonstrate the capabilities of Bayesian image reconstruction using information field theory. Our objective is to reconstruct denoised, deconvolved and spatio-spectral resolved images from X-ray observations and to decompose the emission into different morphologies, namely diffuse and point-like. Further, we aim to fuse data from different detectors and pointings into a mosaic and quantify the uncertainty of our result. Utilizing prior knowledge on the spatial and spectral correlation structure of the two components, diffuse emission and point sources, the presented method allows the effective decomposition of the signal into these. In order to accelerate the imaging process, we introduce a multi-step approach, in which the spatial reconstruction obtained for a single energy range is used to derive an informed starting point for the full spatio-spectral reconstruction. The method is applied to 11 Chandra observations of SN1006 from 2008 and 2012, providing a detailed, denoised and decomposed view of the remnant. In particular, the separated view of the diffuse emission should provide new insights into its complex small-scale structures in the center of the remnant and at the shock front profiles.

astro-ph.HE↗

arXiv2023

A BRAIN study to tackle image analysis with artificial intelligence in the ALMA 2030 era

An ESO internal ALMA development study, BRAIN, is addressing the ill-posed inverse problem of synthesis image analysis employing astrostatistics and astroinformatics. These emerging fields of research offer interdisciplinary approaches at the intersection of observational astronomy, statistics, algorithm development, and data science. In this study, we provide evidence of the benefits of employing these approaches to ALMA imaging for operational and scientific purposes. We show the potential of two techniques, RESOLVE and DeepFocus, applied to ALMA calibrated science data. Significant advantages are provided with the prospect to improve the quality and completeness of the data products stored in the science archive and overall processing time for operations. Both approaches evidence the logical pathway to address the incoming revolution in data rates dictated by the planned electronic upgrades. Moreover, we bring to the community additional products through a new package, ALMASim, to promote advancements in these fields, providing a refined ALMA simulator usable by a large community for training and/or testing new algorithms.

astro-ph.IM↗

arXiv2023

Resource-aware Research on Universe and Matter: Call-to-Action in Digital Transformation

Given the urgency to reduce fossil fuel energy production to make climate tipping points less likely, we call for resource-aware knowledge gain in the research areas on Universe and Matter with emphasis on the digital transformation. A portfolio of measures is described in detail and then summarized according to the timescales required for their implementation. The measures will both contribute to sustainable research and accelerate scientific progress through increased awareness of resource usage. This work is based on a three-days workshop on sustainability in digital transformation held in May 2023.

physics.comp-ph↗

arXiv2023

Resolving Horizon-Scale Dynamics of Sagittarius A*

Sagittarius A* (Sgr A*), the supermassive black hole at the heart of our galaxy, provides unique opportunities to study black hole accretion, jet formation, and gravitational physics. The rapid structural changes in Sgr A*'s emission pose a significant challenge for traditional imaging techniques. We present dynamic reconstructions of Sgr A* using Event Horizon Telescope (EHT) data from April 6th and 7th, 2017, analyzed with a one-minute temporal resolution with the Resolve framework. This Bayesian approach employs adaptive Gaussian Processes and Variational Inference for data-driven self-regularization. Our results not only fully confirm the initial findings by the EHT Collaboration for a time-averaged source but also reveal intricate details about the temporal dynamics within the black hole environment. We find an intriguing dynamic feature on April 6th that propagates in a clock-wise direction. Geometric modelling with ray-tracing, although not fully conclusive, indicates compatibility with high-inclination configurations of about $θ_o = 160^\circ$, as seen in other studies.

astro-ph.HE↗

arXiv2023

Reconstructing Galactic magnetic fields from local measurements for backtracking ultra-high-energy cosmic rays

(abridged) Ultra-high energy cosmic rays (UHECRs) are highly energetic charged particles with energies exceeding $10^{18}$ eV. Identifying their sources and production mechanism can provide insight into many open questions in astrophysics and high energy physics. However, the Galactic magnetic field (GMF) deflects UHECRs, and the high uncertainties in our current understanding of the $3$-dimensional structure of the GMF does not permit us to accurately determine their true arrival direction on the plane of the sky (PoS). This difficulty arises from the fact that currently all GMF observations are integrated along the line-of-sight (LoS). Upcoming stellar optopolarimetric surveys as well as Gaia data on stellar parallaxes, are expected to provide local measurements of the GMF in the near future. In this paper, we evaluate the reconstruction of the GMF in a limited region of the Galaxy given sparse and local GMF measurements within that region, through Bayesian inference using principles of Information Field Theory. We backtrack UHECRs through GMF configurations drawn from the posterior to improve our knowledge of their true arrival directions. We show that, for a weakly turbulent GMF, it is possible to correct for its effect on the observed arrival direction of UHECRs to within $\sim 3^\circ$. For completely turbulent fields, we show that our procedure can still be used to significantly improve our knowledge on the true arrival direction of UHECRs.

astro-ph.HE↗

arXiv2023

Quantum Measurement and Objective Classical Reality

We explore quantum measurement in the context of Everettian unitary quantum mechanics and construct an explicit unitary measurement procedure. We propose the existence of prior correlated states that enable this procedure to work and therefore argue that correlation is a resource that is consumed when measurements take place. It is also argued that a network of such measurements establishes a stable objective classical reality.

quant-ph↗

arXiv2022

Causal, Bayesian, & Non-parametric Modeling of the SARS-CoV-2 Viral Load Distribution vs. Patient's Age

The viral load of patients infected with SARS-CoV-2 varies on logarithmic scales and possibly with age. Controversial claims have been made in the literature regarding whether the viral load distribution actually depends on the age of the patients. Such a dependence would have implications for the COVID-19 spreading mechanism, the age-dependent immune system reaction, and thus for policymaking. We hereby develop a method to analyze viral-load distribution data as a function of the patients' age within a flexible, non-parametric, hierarchical, Bayesian, and causal model. The causal nature of the developed reconstruction additionally allows to test for bias in the data. This could be due to, e.g., bias in patient-testing and data collection or systematic errors in the measurement of the viral load. We perform these tests by calculating the Bayesian evidence for each implied possible causal direction. The possibility of testing for bias in data collection and identifying causal directions can be very useful in other contexts as well. For this reason we make our model freely available. When applied to publicly available age and SARS-CoV-2 viral load data, we find a statistically significant increase in the viral load with age, but only for one of the two analyzed datasets. If we consider this dataset, and based on the current understanding of viral load's impact on patients' infectivity, we expect a non-negligible difference in the infectivity of different age groups. This difference is nonetheless too small to justify considering any age group as noninfectious.

stat.AP↗