arXiv subjects

Steffen Frey

Publications and source records attributed to Steffen Frey.

12 recordsLinked to original sources

arXiv2026

On-sky Fibre-Target-Alignment of the 4MOST instrument: calibration and performance

The 4-metre Multi-Object Spectroscopic Telescope (4MOST) is a new wide-field, fibre-fed spectroscopic survey facility for the VISTA telescope at ESOs Paranal Observatory. The instrument enables the simultaneous acquisition of 2436 spectra across a 4.2 square deg field of view, using a tilting spine fibre positioner feeding three dedicated spectrographs. In this paper, we describe the calibration process, and performance verification of the Fibre-Target-Alignment (FTA) process for 4MOST. We show the complete FTA process, including calibration of the individual hardware- and software components. Namely the Metrology camera system, the Fibre Positioner AESOP, a spine based Secondary Guiding System, the sky to focal surface projection software, and residual minimization via raster scans. In total, the FTA system required one special tool, a large calibration target for the focal surface, and approximately 1 month of accumulated calibration work on the telescope. The FTA process reached approx. 24 um (0.4 arc sec) RMS distance between fibres and targets on sky about 3 weeks after installation of the final hardware components of 4MOST, which is when 4MOST had its first light event. By the time of writing this paper, i.e. 6 months later, we reach approx. 16 um (0.27 arc sec) RMS. Currently, we far exceed our requirements in terms of accuracy, and are doing trade-off studies to maximize scientific returns.

astro-ph.IM↗

arXiv2026

TRACE: Reconstruction-Based Anomaly Detection in Ensemble and Time-Dependent Simulations

Detecting anomalies in high-dimensional, time-dependent simulation data is challenging due to complex spatial and temporal dynamics. We study reconstruction-based anomaly detection for ensemble data from parameterized K\'arm\'an vortex street simulations using convolutional autoencoders. We compare a 2D autoencoder operating on individual frames with a 3D autoencoder that processes short temporal stacks. The 2D model identifies localized spatial irregularities in single time steps, while the 3D model exploits spatio-temporal context to detect anomalous motion patterns and reduces redundant detections across time. We further evaluate volumetric time-dependent data and find that reconstruction errors are strongly influenced by the spatial distribution of mass, with highly concentrated regions yielding larger errors than dispersed configurations. Our results highlight the importance of temporal context for robust anomaly detection in dynamic simulations.

cs.LG↗

arXiv2025

SENSE: Self-Supervised Neural Embeddings for Spatial Ensembles

Analyzing and visualizing scientific ensemble datasets with high dimensionality and complexity poses significant challenges. Dimensionality reduction techniques and autoencoders are powerful tools for extracting features, but they often struggle with such high-dimensional data. This paper presents an enhanced autoencoder framework that incorporates a clustering loss, based on the soft silhouette score, alongside a contrastive loss to improve the visualization and interpretability of ensemble datasets. First, EfficientNetV2 is used to generate pseudo-labels for the unlabeled portions of the scientific ensemble datasets. By jointly optimizing the reconstruction, clustering, and contrastive objectives, our method encourages similar data points to group together while separating distinct clusters in the latent space. UMAP is subsequently applied to this latent representation to produce 2D projections, which are evaluated using the silhouette score. Multiple types of autoencoders are evaluated and compared based on their ability to extract meaningful features. Experiments on two scientific ensemble datasets - channel structures in soil derived from Markov chain Monte Carlo, and droplet-on-film impact dynamics - show that models incorporating clustering or contrastive loss marginally outperform the baseline approaches.

cs.LG↗

arXiv2025

Visualizing the mass transfer flow in direct-impact accretion

We use a variety of visualization techniques to display the interior and surface flows in a double white dwarf binary undergoing direct-impact mass transfer and evolving dynamically to a merger. The structure of the flow can be interpreted in terms of standard dynamical, cyclostrophic and geostrophic arguments. We describe and showcase some visualization and analysis techniques of potential interest for astrophysical hydrodynamics. In the context of R Coronae Borealis stars, we find that mixing of accretor material with donor material at the shear layer between the fast accretion belt and the slower rotating accretor body will always result in some dredge-up. We also discuss briefly some potential applications to other types of binaries.

astro-ph.SR↗

arXiv2025

ENTIRE: Learning-based Volume Rendering Time Prediction

We introduce ENTIRE, a novel deep learning-based approach for fast and accurate volume rendering time prediction. Predicting rendering time is inherently challenging due to its dependence on multiple factors, including volume data characteristics, image resolution, camera configuration, and transfer function settings. Our method addresses this by first extracting a feature vector that encodes structural volume properties relevant to rendering performance. This feature vector is then integrated with additional rendering parameters, such as image resolution, camera setup, and transfer function settings, to produce the final prediction. We evaluate ENTIRE across multiple rendering frameworks (CPU- and GPU-based) and configurations (with and without single-scattering) on diverse datasets. The results demonstrate that our model achieves high prediction accuracy with fast inference speed and can be efficiently adapted to new scenarios by fine-tuning the pretrained model with few samples. Furthermore, we showcase ENTIRE's effectiveness in two case studies, where it enables dynamic parameter adaptation for stable frame rates and load balancing.

cs.GR↗

arXiv2024

HyperFLINT: Hypernetwork-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization

We present HyperFLINT (Hypernetwork-based FLow estimation and temporal INTerpolation), a novel deep learning-based approach for estimating flow fields, temporally interpolating scalar fields, and facilitating parameter space exploration in spatio-temporal scientific ensemble data. This work addresses the critical need to explicitly incorporate ensemble parameters into the learning process, as traditional methods often neglect these, limiting their ability to adapt to diverse simulation settings and provide meaningful insights into the data dynamics. HyperFLINT introduces a hypernetwork to account for simulation parameters, enabling it to generate accurate interpolations and flow fields for each timestep by dynamically adapting to varying conditions, thereby outperforming existing parameter-agnostic approaches. The architecture features modular neural blocks with convolutional and deconvolutional layers, supported by a hypernetwork that generates weights for the main network, allowing the model to better capture intricate simulation dynamics. A series of experiments demonstrates HyperFLINT's significantly improved performance in flow field estimation and temporal interpolation, as well as its potential in enabling parameter space exploration, offering valuable insights into complex scientific ensembles.

cs.CV↗

arXiv2024

FLINT: Learning-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization

We present FLINT (learning-based FLow estimation and temporal INTerpolation), a novel deep learning-based approach to estimate flow fields for 2D+time and 3D+time scientific ensemble data. FLINT can flexibly handle different types of scenarios with (1) a flow field being partially available for some members (e.g., omitted due to space constraints) or (2) no flow field being available at all (e.g., because it could not be acquired during an experiment). The design of our architecture allows to flexibly cater to both cases simply by adapting our modular loss functions, effectively treating the different scenarios as flow-supervised and flow-unsupervised problems, respectively (with respect to the presence or absence of ground-truth flow). To the best of our knowledge, FLINT is the first approach to perform flow estimation from scientific ensembles, generating a corresponding flow field for each discrete timestep, even in the absence of original flow information. Additionally, FLINT produces high-quality temporal interpolants between scalar fields. FLINT employs several neural blocks, each featuring several convolutional and deconvolutional layers. We demonstrate performance and accuracy for different usage scenarios with scientific ensembles from both simulations and experiments.

cs.CV↗

arXiv2024

Parallel Compositing of Volumetric Depth Images for Interactive Visualization of Distributed Volumes at High Frame Rates

We present a parallel compositing algorithm for Volumetric Depth Images (VDIs) of large three-dimensional volume data. Large distributed volume data are routinely produced in both numerical simulations and experiments, yet it remains challenging to visualize them at smooth, interactive frame rates. VDIs are view-dependent piecewise constant representations of volume data that offer a potential solution. They are more compact and less expensive to render than the original data. So far, however, there is no method for generating VDIs from distributed data. We propose an algorithm that enables this by sort-last parallel generation and compositing of VDIs with automatically chosen content-adaptive parameters. The resulting composited VDI can then be streamed for remote display, providing responsive visualization of large, distributed volume data.

cs.GR↗

arXiv2023

Visual Ensemble Analysis of Fluid Flow in Porous Media across Simulation Codes and Experiment

We study the question of how visual analysis can support the comparison of spatio-temporal ensemble data of liquid and gas flow in porous media. To this end, we focus on a case study, in which nine different research groups concurrently simulated the process of injecting CO2 into the subsurface. We explore different data aggregation and interactive visualization approaches to compare and analyze these nine simulations. In terms of data aggregation, one key component is the choice of similarity metrics that define the relation between the different simulations. We test different metrics and find that a fine-tuned machine-learning based metric provides the best visualization results. Based on that, we propose different visualization methods. For overviewing the data, we use dimensionality reduction methods that allow us to plot and compare the different simulations in a scatterplot. To show details about the spatio-temporal data of each individual simulation, we employ a space-time cube volume rendering. We use the resulting interactive, multi-view visual analysis tool to explore the nine simulations and also to compare them to data from experimental setups. Our main findings include new insights into ranking of simulation results with respect to experimental data, and the development of gravity fingers in simulations.

cs.HC↗

arXiv2023

Visual Analysis of Displacement Processes in Porous Media using Spatio-Temporal Flow Graphs

We developed a new approach comprised of different visualizations for the comparative spatio-temporal analysis of displacement processes in porous media. We aim to analyze and compare ensemble datasets from experiments to gain insight into the influence of different parameters on fluid flow. To capture the displacement of a defending fluid by an invading fluid, we first condense an input image series to a single time map. From this map, we generate a spatio-temporal flow graph covering the whole process. This graph is further simplified to only reflect topological changes in the movement of the invading fluid. Our interactive tools allow the visual analysis of these processes by visualizing the graph structure and the context of the experimental setup, as well as by providing charts for multiple metrics. We apply our approach to analyze and compare ensemble datasets jointly with domain experts, where we vary either fluid properties or the solid structure of the porous medium. We finally report the generated insights from the domain experts and discuss our contribution's advantages, generality, and limitations.

cs.GR↗

arXiv2023

Efficient Raycasting of Volumetric Depth Images for Remote Visualization of Large Volumes at High Frame Rates

We present an efficient raycasting algorithm for rendering Volumetric Depth Images (VDIs), and we show how it can be used in a remote visualization setting with VDIs generated and streamed from a remote server. VDIs are compact view-dependent volume representations that enable interactive visualization of large volumes at high frame rates by decoupling viewpoint changes from expensive rendering calculations. However, current rendering approaches for VDIs struggle with achieving interactive frame rates at high image resolutions. Here, we exploit the properties of perspective projection to simplify intersections of rays with the view-dependent frustums in a VDI and leverage spatial smoothness in the volume data to minimize memory accesses. Benchmarks show that responsive frame rates can be achieved close to the viewpoint of generation for HD display resolutions, providing high-fidelity approximate renderings of Gigabyte-sized volumes. We also propose a method to subsample the VDI for preview rendering, maintaining high frame rates even for large viewpoint deviations. We provide our implementation as an extension of an established open-source visualization library.

cs.GR↗

arXiv2021

Visual Analysis of Two-Phase Flow Displacement Processes in Porous Media

We present the visual analysis of our novel parameter study of porous media experiments, focusing on gaining a better understanding of drainage processes on the micro-scale. We analyze the temporal evolution of extracted characteristic values, and discuss how to directly compare experiments that exhibit processes at different temporal scales due to varying boundary and physical conditions. To enable spatio-temporal analysis, we introduce a new abstract visual representation showing which paths through the porous media were occupied to what extent, e.g., allowing for classification into viscous and capillary regimes. This joint work of porous media experts and visualization researchers yields new insights regarding immiscible two-phase flow on the micro-scale toward the overarching goal of characterizing flow based on boundary conditions and physical fluid properties.

physics.flu-dyn↗