arXiv subjects

Torsten Enßlin

Publications and source records attributed to Torsten Enßlin.

At least 19 recordsLinked to original sources

arXiv2026

Beyond $X_\mathrm{max}$ : Reconstructing Air Shower Profiles with Information Field Theory with SKA-Low

While radio measurements of extensive air showers have shown to achieve a high precision of $X_\mathrm{max}$ sensitivity, it has been shown that parameters beyond $X_\mathrm{max}$ can also be reconstructed. These shape parameters contain additional sensitivity to the hadronic physics in the shower as well as its mass composition. In this work, we showcase a reconstruction framework to recover the full longitudinal profile from realistic radio measurements. The framework is based on Information Field Theory that infers the full profile with a forward-based model, which uses a Gaisser-Hillas profile with weakly informative shower priors, SMIET with a template library to synthesise pulses at any event geometry, and a realistic antenna response and noise level emulating that of SKA-Low. We verify the self-consistency of our framework with $\sim 900$ events generated with SMIET with antennas placed on the $\vec{v} \times (\vec{v} \times \vec{B})$ axis. The framework recovers the full profile within uncertainty and capture correlations between shower parameters. We yield an $X_\mathrm{max}$ resolution of $< 9$ g cm$^{-2}$ as well as resolutions of the width and asymmetry with minimal bias. The profile is also recovered with a bias of $< 4$% at all atmospheric depths $< 1200$ g cm$^{-2}$. We aim to apply this framework with pulses simulated from CoREAS with measured noise, ultimately extending the framework to realistic antenna layouts such as from LOFAR or SKA-Low.

astro-ph.IM↗

arXiv2026

Milky Way Atlas: A radial-velocity-resolved, three-dimensional map of H I within 1.25 kpc

We present a velocity-resolved three-dimensional map of local atomic hydrogen (HI) within 1.25 kpc of the Sun, tackling the challenge of converting emission from position-position-velocity space into true 3D structure. Our method combines the HI4PI full-sky survey with the Edenhofer et al. (2024) 3D dust map in the framework of Information Field Theory, enabling a joint reconstruction of the local HI density, radial velocity field, and effective line width while also separating emission arising inside the mapped local volume from more distant Galactic HI. The inference is driven by morphological matching between dust and HI structures together with kinematic coherence in 3D space. Synthetic data tests show that the method recovers the local density and velocity structure, even in the presence of substantial contamination from distant emission. The resulting map reveals a smoother, more diffuse local HI distribution than the dust, a declining HI-to-dust ratio toward high dust column densities consistent with the atomic-to-molecular transition, and a velocity field that captures both large-scale Galactic rotation and local non-circular velocities. Independent comparisons with maser and young stellar cluster velocities agree with the recovered kinematics. This HI map provides a new three-dimensional, kinematically resolved view of the nearby atomic interstellar medium and a foundation for localising other velocity-resolved Galactic emission in physical space.

astro-ph.GA↗

arXiv2026

Interferometric Analysis of Air-shower Radio Emission in the Near Field with an Information Field Theory Approach

Current reconstruction techniques for air-shower radio emission generated by cosmic rays have shown great success, having been applied to several radio detectors over the last decade. Nevertheless, they are limited by their high computational cost, simplified approximations, and signal information used for reconstruction. As such, advanced analyses are required to not only be able to perform a holistic reconstruction of all parameters, but also to conduct near-field interferometry of the air shower. This can be achieved through Information Field Theory (IFT), an imaging reconstruction framework based on Bayesian inference that can extract all available information within the signal to infer distributions of field-like quantities. In this chapter, we highlight current novel approaches that use IFT for air shower reconstruction, and the potential of their applicability towards SKA-Low.

astro-ph.IM↗

arXiv2026

Nonparametric Variational Inference Reconstruction of the Cosmic Expansion History from SNe Ia -- the charm2 code

Cosmological analyses using the latest set of type Ia SNe data weakly favor an evolving dark energy (EDE) model without strongly disfavoring the standard LCDM paradigm. Nonparametric reconstructions of the expansion history may reveal signal features potentially missed by a parametric LCDM model without laying out a specific functional form for the evolution of dark energy. Information field theory (IFT) is a Bayesian framework for optimal, nonparametric reconstruction algorithms. In this work, we present charm2, the successor to charm1, a previous IFT-based code to reconstruct the cosmic energy density's redshift evolution from SNe Ia. We apply our reconstruction algorithm to the Union2.1, Pantheon+, DESY5 and DESY5-Dovekie data sets to investigate the agreement between the nonparametric reconstruction and the signal suggested by a parametric, flat LCDM model. To enable an accurate Gaussian approximation, we employ geometric variational inference, which finds a coordinate transformation through which a curved posterior gets "flattened". The redshift evolution of the energy density can then be traced on a double-logarithmic scale, which, after de-trending, is well described by a stationary Gaussian process. The nonparametric charm2 reconstructions using the Union2.1 and Pantheon+ data sets are consistent with flat LCDM signal fields. The DESY5 and DESY5-Dovekie reconstructions deviate from flat LCDM comparison fields and are compatible with an evolving dark energy signal. However, using the evidence lower bound (ELBO) measure for model selection, we find no conclusive evidence supporting a preference for non-flat-LCDM features in any of the data sets. We note that at current DESY5 noise levels, the ELBO tends to favor flat LCDM over our nonparametric model although the latter better recovers the ground truth in synthetic EDE data; a trend reversing only at ~7x lower noise covariance.

astro-ph.CO↗

arXiv2026

Shaping the Digital Future of ErUM Research: Sustainability & Ethics

This workshop report from "Shaping the Digital Future of ErUM Research: Sustainability & Ethics" (Aachen, 2025) reviews progress on sustainability measures in data-intensive ErUM-Data research since the 2023 call-to-action on resource-aware research. It evaluates short-, medium-, and long-term actions around monitoring and reducing CO2 emissions, improving data and software FAIRness, optimizing workflows and computing infrastructures, and aligning operations with low-carbon energy availability, including concepts such as "breathing" computing centers, long-term data storage strategies, and software efficiency certification. The report stresses the need for systematic teaching, training, mentoring, and new support formats to establish sustainable coding and computing practices, particularly among students and early-career researchers, and highlights the importance of dedicated steering and funding instruments to embed sustainability in project planning. Ethical discussions focus on the transformative use of AI in ErUM-Data, addressing autonomy, bias, transparency, explainability, attribution of responsibility, and the risk of deskilling, while reaffirming that accountability for scientific outcomes remains with human researchers. Finally, the report emphasizes that sustainable transformation requires not only technical measures but also targeted awareness-building, communication strategies, incentives, and community-driven initiatives to move from awareness to action and to integrate sustainability and ethics into everyday scientific practice.

physics.comp-ph↗

arXiv2026

Mass Manipulation in Simulated Social Networks: Dominating vs. Diversifying Attention

Modern information environments, especially social media, are highly complex systems that exceed individual processing capacities such as humans' limited attention. This environment/cognition mismatch can increase susceptibility to misinformation, which various actors exploit for anti-social (including anti-democratic or anti-science) aims. This raises the question of how to feasibly sustain societal resilience against misinformation, though the challenge is to find strategies that respect individuals' cognitive limitations. We investigate whether a simple behavioral rule - topic diversification - can enhance collective performance and mitigate vulnerability. In an agent-based model that includes a deceptive mass-influencing agent (MIA), we compare two attention-distribution strategies: (A) acquaintance-based topic selection, where agents return to familiar content, and (B) randomized topics, which diversify attention. We also track dynamics across different network structures. We find that under acquaintance-based topics, a central MIA advances its propaganda effectively, causing volatile and polarized opinions through repeated exposure and echo chambers. Under randomized topics, this leverage disappears: the MIA's influence collapses across all network structures, and opinions become stable and broadly aligned with reality. These results, while deriving from simple simulations, align with realistic theories of bounded rationality and collective cognition, further suggesting a cognitively feasible, easy-to-monitor and robust strategy: distribute attention to combat misinformation.

physics.soc-ph↗

arXiv2026

Collisionless relaxation as the origin of the anisotropic, non-thermal, and multi-temperature momentum distributions observed in space plasmas

Anisotropic, non-thermal, and multi-temperature distributed particle momenta are commonly observed in collisionless space plasmas, such as the solar wind. Using Liouville's theorem, we argue that anisotropic compression or expansion of the plasma, followed by a relaxation of the resulting anisotropic stress must lead to non-equilibrium states that exhibit either anisotropic, non-thermal distribution functions, different electron and ion temperatures, or a combination of these effects. We present arguments showing that a plasma in thermal equilibrium undergoing anisotropic compression or expansion cannot return to thermal equilibrium in the absence of particle collisions. Since most astrophysical plasmas are practically collisionless and experience significant anisotropic compression or expansion, we expect anisotropic, non-thermal, and multi-temperature particle distributions to be ubiquitous, in agreement with solar wind measurements.

physics.plasm-ph↗

arXiv2026

The Self-Limiting Nature of Jet-Modulated Thermal Conduction in Cool Core Clusters

Conduction as a mechanism for explaining the disrupted cooling-flow in galaxy clusters has been mostly discounted, as the process is inefficient at transporting heat all the way from the cluster into the core. However, thermal conduction can be strongly enhanced when materials of significantly different temperature are brought into proximity, and thus into close thermal contact. Jets of active galactic nuclei may act as heat pumps by bringing low-entropy gas from the cluster core into thermal contact with the hot outer atmosphere of the cluster, significantly increasing the feedback efficiency of active galactic nuclei. We test this hypothesis by running a suite of 3D magnetohydrodynamic simulations of active galactic nuclei jets in a Perseus-like cluster, including anisotropic conduction. We find that the heat pump efficiency $η$ can reach up to 50\% of the maximum possible efficiency $η_{\rm max}$ if conduction operates near the Spitzer-Braginskii limit, while $η\approx f_{\rm sp}η_{\rm max}$ if conduction along the field lines is substantially suppressed below the Spitzer-Braginskii value by a factor $f_{\rm sp}$ by kinetic effects, as recently suggested. We further find that jet-induced thermal conduction is self-limiting: Magnetic draping during the uplift results in a magnetic field orientation close to perpendicular to the induced temperature gradients, significantly reducing conduction along the ideal conductive pathways. Thus, for conservative assumptions about thermal conduction suppression by $f_{\rm sp} \lesssim 0.1$, the heat pump effect leads to only marginal heat transfer and, correspondingly, to immaterial changes in the overall thermal evolution of cool core clusters beyond the isolated effects of conduction and jet-induced heating alone.

astro-ph.HE↗

arXiv2026

DeepLight: A Sobolev-trained Image-to-Image Surrogate Model for Light Transport in Tissue

In optoacoustic imaging, recovering the absorption coefficients of tissue by inverting the light transport remains a challenging problem. Improvements in solving this problem can greatly benefit the clinical value of optoacoustic imaging. Existing variational inversion methods require an accurate and differentiable model of this light transport. As neural surrogate models allow fast and differentiable simulations of complex physical processes, they are considered promising candidates to be used in solving such inverse problems. However, there are in general no guarantees that the derivatives of these surrogate models accurately match those of the underlying physical operator. As accurate derivatives are central to solving inverse problems, errors in the model derivative can considerably hinder high fidelity reconstructions. To overcome this limitation, we present a surrogate model for light transport in tissue that uses Sobolev training to improve the accuracy of the model derivatives. Additionally, the form of Sobolev training we used is suitable for high-dimensional models in general. Our results demonstrate that Sobolev training for a light transport surrogate model not only improves derivative accuracy but also reduces generalization error for in-distribution and out-of-distribution samples. These improvements promise to considerably enhance the utility of the surrogate model in downstream tasks, especially in solving inverse problems.

cs.LG↗

arXiv2025

The ORCA-TWIN qCMOS Experiment I. Science case and commissioning at Calar Alto Observatory

We describe a pilot study to explore a new generation of fast and low noise CMOS image sensors for time domain astronomy, using two remote telescopes with a baseline of 1635 km. The experiment involves direct imaging with novel qCMOS image sensor technology that combines fast readout with sub-electron readout noise. Moreover, synchronized observations from two remote telescope sites will be used to explore new approaches for measuring Solar System bodies, precision stellar photometry, and speckle imaging. A fast-track installation of an ORCA-Quest2 camera at the Calar Alto Observatory 1.23m telescope has demonstrated the potential of the qCMOS technology for time domain astronomy. Numerical simulations suggest that owing to sub-electron readout noise, qCMOS sensors outperform classical CCDs for high-cadence imaging on 1m-class telescopes. The small penalty for post-readout binning, that is almost insignificant in comparison to higher readout noise detectors, opens interesting applications for scene-dependent data processing in direct imaging, and potentially even for spectroscopy.

astro-ph.IM↗

arXiv2025

The Universal Bayesian Imaging Kit

Bayesian imaging of astrophysical measurement data shares universal properties across the electromagnetic spectrum: it requires probabilistic descriptions of possible images and spectra, and instrument responses. To unify Bayesian imaging, we present the Universal Bayesian Imaging Kit (UBIK). Currently, UBIK images data from Chandra, eROSITA, JWST, and ALMA. UBIK is based on information field theory (IFT), the mathematical theory of field inference, and on NIFTy, a package for numerical IFT. UBIK provides sky models that are instrument independent and instrument interfaces that share common parts of their response representations. It is open source, can provide spatio-spectral image cubes, jointly analyses data from several instruments, and separates diffuse emission, point sources, and extended emission regions.

astro-ph.IM↗

arXiv2025

Correlation as a Resource in Unitary Quantum Measurements

Quantum measurement is a physical process. What physical resources and constraints does quantum mechanics require for measurement to produce the classical world we observe? Treating measurement as a fully unitary quantum process, our goal is to show that objective, redundant, and correctly aligned outcomes are possible iff the environment begins in a specially structured, correlated subspace. We start with a minimal set of assumptions: unitarity, orthogonality of conditional environment branches, and finite-dimensional Hilbert spaces. Using these, we demonstrate that generic environmental states cannot support redundant and mutually consistent records of the signal, the measured quantum system. The admissible initial states form a subspace on which the measurement maps obey the Knill-Laflamme error-correction conditions, revealing that the emergence of classical objectivity relies on the environment behaving like a quantum error-correcting code. The post-measurement subspace naturally factorizes into a ``pointer'' to hold measurement outcomes and ``memory'' to retain pre-measurement quantum information about the environment's state, thereby respecting the no-deletion theorem. This further allows the identification of correlation as a finite resource consumed during measurement. Through an explicit qudit model with local interactions, we demonstrate how correlated environments yield redundant observer networks. Simulations show that record fidelity and redundancy depend on the initial correlations in the environment. This perspective links quantum Darwinism to error correction and raises the possibility that natural processes may prepare and evolutionarily favour environments capable of supporting reliable measurement.

quant-ph↗

arXiv2025

Information Field Theory -- Concepts, Applications, and AI-Perspective

Information field theory (IFT) is the application of probabilistic reasoning to fields. Physical fields are mathematical functions over continuous spaces that exhibit certain properties of regularity, such as limited variance and finite gradients. Inferring a field from an observational dataset should exploit these regularities. However, the finite number of constraints that the data provides is insufficient to determine the infinite number of degrees of freedom of a field. IFT enables us to derive optimal field inference algorithms that explicitly exploit domain knowledge. These algorithms can be implemented via Numerical Information Field Theory (NIFTy). In NIFTy, neural operator forward models can be written and inverted probabilistically. NIFTy thereby infers fields and their remaining uncertainties. This is achieved using novel variational inference schemes that scale quasi-linearly, even for ultra-high dimensional problems. This paper introduces the basic concepts of IFT and NIFTy, highlights a few of their astrophysical applications, and discusses their artificial intelligence (AI) perspective. Finally, UBIK (the Universal Bayesian Imaging Kit), an emerging customisation of NIFTy for a suite of astrophysical telescopes, is presented as a central tool to the topic of the UniversAI conference.

astro-ph.IM↗

arXiv2025

Bayesian Multi-wavelength Imaging of the LMC SN1987A with SRG/eROSITA

The eROSITA Early Data Release (EDR) and eROSITA All-Sky Survey (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 Large Magellanic Cloud (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 identification of point sources in this region, and enhanced calibration for future work.

astro-ph.IM↗

arXiv2025

Information Field Theory with JAX infers Air Shower Electric Currents from Antenna Signal Traces

Direct imaging of cosmic-ray-induced particle showers during daylight is a long-standing challenge in astroparticle physics. A promising avenue for capturing images of these showers is through the radio emissions generated by their electrically charged particles. Their corresponding current vectors evolve over time as the particle shower propagates through the Earth's atmosphere leading to a characteristic time-dependent electric field in an antenna array. In this work, we harness modern Bayesian inference techniques within the Python toolkit for numerical information field theory NIFTy, coupled with the high-performance numerical computing capabilities of the Python library JAX. This innovative combination enables us to reconstruct the particle shower and its temporal development from data collected by a ground-based antenna array. Our approach opens an initial pathway for detailed imaging of cosmic-ray showers, potentially advancing our understanding of high-energy astrophysical processes.

astro-ph.IM↗

arXiv2025

Information Field Theory based Event Reconstruction for Cosmic Ray Radio Detectors

Detection of extensive air showers with radio antennas is an appealing technique in cosmic ray physics. However, because of the high level of measurement noise, current reconstruction methods still leave room for improvement. Furthermore, reconstruction efforts typically focus only on a single aspect of the signal, such as the energy fluence or arrival time. Bayesian inference is then a natural choice for a holistic approach to reconstruction, yet, this problem would be ill-posed, since the electric field is a continuous quantity. Information Field Theory provides the solution for this by providing a statistical framework to deal with discretised fields in the continuum limit. We are currently developing models for this novel approach to reconstructing extensive air showers. The model described here is based on the best current understanding of the emission mechanisms: It uses parametrisations of the lateral signal strength distribution, charge-excess contribution and spectral shape. Shower-to-shower fluctuations and narrowband RFI are modelled using Gaussian processes. Combined with a detailed detector description, this model can infer not only the electric field, but also the shower geometry, electromagnetic energy and position of shower maximum. Another big achievement of this approach is its ability to naturally provide uncertainties for the reconstruction, which has been shown to be difficult in more traditional methods. With such an open framework and robust computational methods based in Information Field Theory, it will also be easy to incorporate new insights and additional data, such as timing distributions or particle detector data, in the future. This approach has a high potential to exploit the full information content of a complex detector with rigorous statistical methods, in a way that directly includes domain knowledge.

astro-ph.IM↗

arXiv2025

Latent-space Field Tension for Astrophysical Component Detection An application to X-ray imaging

Modern observatories are designed to deliver increasingly detailed views of astrophysical signals. To fully realize the potential of these observations, principled data-analysis methods are required to effectively separate and reconstruct the underlying astrophysical components from data corrupted by noise and instrumental effects. In this work, we introduce a novel multi-frequency Bayesian model of the sky emission field that leverages latent-space tension as an indicator of model misspecification, enabling automated separation of diffuse, point-like, and extended astrophysical emission components across wavelength bands. Deviations from latent-space prior expectations are used as diagnostics for model misspecification, thus systematically guiding the introduction of new sky components, such as point-like and extended sources. We demonstrate the effectiveness of this method on synthetic multi-frequency imaging data and apply it to observational X-ray data from the eROSITA Early Data Release (EDR) of the SN1987A region in the Large Magellanic Cloud (LMC). Our results highlight the method's capability to reconstruct astrophysical components with high accuracy, achieving sub-pixel localization of point sources, robust separation of extended emission, and detailed uncertainty quantification. The developed methodology offers a general and well-founded framework applicable to a wide variety of astronomical datasets, and is therefore well suited to support the analysis needs of next-generation multi-wavelength and multi-messenger surveys.

astro-ph.IM↗

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↗