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Thomas Ertl

Publications and source records attributed to Thomas Ertl.

At least 19 recordsLinked to original sources

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

Enhancing Single-Frame Supervision for Better Temporal Action Localization

Temporal action localization aims to identify the boundaries and categories of actions in videos, such as scoring a goal in a football match. Single-frame supervision has emerged as a labor-efficient way to train action localizers as it requires only one annotated frame per action. However, it often suffers from poor performance due to the lack of precise boundary annotations. To address this issue, we propose a visual analysis method that aligns similar actions and then propagates a few user-provided annotations (e.g. , boundaries, category labels) to similar actions via the generated alignments. Our method models the alignment between actions as a heaviest path problem and the annotation propagation as a quadratic optimization problem. As the automatically generated alignments may not accurately match the associated actions and could produce inaccurate localization results, we develop a storyline visualization to explain the localization results of actions and their alignments. This visualization facilitates users in correcting wrong localization results and misalignments. The corrections are then used to improve the localization results of other actions. The effectiveness of our method in improving localization performance is demonstrated through quantitative evaluation and a case study.

cs.HC

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

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

Droplet-Local Line Integration for Multiphase Flow

Line integration of stream-, streak-, and pathlines is widely used and popular for visualizing single-phase flow. In multiphase flow, i.e., where the fluid consists, e.g., of a liquid and a gaseous phase, these techniques could also provide valuable insights into the internal flow of droplets and ligaments and thus into their dynamics. However, since such structures tend to act as entities, high translational and rotational velocities often obfuscate their detail. As a remedy, we present a method for deriving a droplet-local velocity field, using a decomposition of the original velocity field removing translational and rotational velocity parts, and adapt path- and streaklines. Generally, the resulting integral lines are thus shorter and less tangled, which simplifies their analysis. We demonstrate and discuss the utility of our approach on droplets in two-phase flow data and visualize the removed velocity parts employing glyphs for context.

physics.flu-dyn

Real-Time Visual Analysis of High-Volume Social Media Posts

Breaking news and first-hand reports often trend on social media platforms before traditional news outlets cover them. The real-time analysis of posts on such platforms can reveal valuable and timely insights for journalists, politicians, business analysts, and first responders, but the high number and diversity of new posts pose a challenge. In this work, we present an interactive system that enables the visual analysis of streaming social media data on a large scale in real-time. We propose an efficient and explainable dynamic clustering algorithm that powers a continuously updated visualization of the current thematic landscape as well as detailed visual summaries of specific topics of interest. Our parallel clustering strategy provides an adaptive stream with a digestible but diverse selection of recent posts related to relevant topics. We also integrate familiar visual metaphors that are highly interlinked for enabling both explorative and more focused monitoring tasks. Analysts can gradually increase the resolution to dive deeper into particular topics. In contrast to previous work, our system also works with non-geolocated posts and avoids extensive preprocessing such as detecting events. We evaluated our dynamic clustering algorithm and discuss several use cases that show the utility of our system.

cs.HC

Efficient Sparse Spherical k-Means for Document Clustering

Spherical k-Means is frequently used to cluster document collections because it performs reasonably well in many settings and is computationally efficient. However, the time complexity increases linearly with the number of clusters k, which limits the suitability of the algorithm for larger values of k depending on the size of the collection. Optimizations targeted at the Euclidean k-Means algorithm largely do not apply because the cosine distance is not a metric. We therefore propose an efficient indexing structure to improve the scalability of Spherical k-Means with respect to k. Our approach exploits the sparsity of the input vectors and the convergence behavior of k-Means to reduce the number of comparisons on each iteration significantly.

cs.LG

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

ELSKE: Efficient Large-Scale Keyphrase Extraction

Keyphrase extraction methods can provide insights into large collections of documents such as social media posts. Existing methods, however, are less suited for the real-time analysis of streaming data, because they are computationally too expensive or require restrictive constraints regarding the structure of keyphrases. We propose an efficient approach to extract keyphrases from large document collections and show that the method also performs competitively on individual documents.

cs.IR

Stellar Collapse Diversity and the Diffuse Supernova Neutrino Background

The diffuse cosmic supernova neutrino background (DSNB) is observational target of the gadolinium-loaded Super-Kamiokande (SK) detector and the forthcoming JUNO and Hyper-Kamiokande detectors. Current predictions are hampered by our still incomplete understanding of the supernova (SN) explosion mechanism and of the neutron star (NS) equation of state and maximum mass. In our comprehensive study we revisit this problem on grounds of the landscapes of successful and failed SN explosions obtained by Sukhbold et al. and Ertl et al. with parametrized one-dimensional neutrino engines for large sets of single-star and helium-star progenitors, with the latter serving as proxy of binary evolution effects. Besides considering engines of different strengths, leading to different fractions of failed SNe with black-hole (BH) formation, we also vary the NS mass limit, the spectral shape of the neutrino emission, and include contributions from poorly understood alternative NS-formation channels such as accretion-induced or merger-induced collapse events. Since the neutrino signals of our large model sets are approximate, we calibrate the associated degrees of freedom by using state-of-the-art simulations of proto-neutron star cooling. Our predictions are higher than other recent ones because of a large fraction of failed SNe with long delay to BH formation. Our best-guess model predicts a DSNB electron-antineutrino-flux of 28.8^{+24.6}_{-10.9} cm^{-2}s^{-1} with 6.0^{+5.1}_{-2.1} cm^{-2}s^{-1} in the favorable measurement interval of [10,30] MeV, and 1.3^{+1.1}_{-0.4} cm^{-2}s^{-1} with electron-antineutrino energies > 17.3 MeV, which is roughly a factor of two below the current SK limit. The uncertainty range is dominated by the still insufficiently constrained cosmic rate of stellar core-collapse events.

astro-ph.HE

Visual Neural Decomposition to Explain Multivariate Data Sets

Investigating relationships between variables in multi-dimensional data sets is a common task for data analysts and engineers. More specifically, it is often valuable to understand which ranges of which input variables lead to particular values of a given target variable. Unfortunately, with an increasing number of independent variables, this process may become cumbersome and time-consuming due to the many possible combinations that have to be explored. In this paper, we propose a novel approach to visualize correlations between input variables and a target output variable that scales to hundreds of variables. We developed a visual model based on neural networks that can be explored in a guided way to help analysts find and understand such correlations. First, we train a neural network to predict the target from the input variables. Then, we visualize the inner workings of the resulting model to help understand relations within the data set. We further introduce a new regularization term for the backpropagation algorithm that encourages the neural network to learn representations that are easier to interpret visually. We apply our method to artificial and real-world data sets to show its utility.

cs.LG

PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning

Storyline visualizations are an effective means to present the evolution of plots and reveal the scenic interactions among characters. However, the design of storyline visualizations is a difficult task as users need to balance between aesthetic goals and narrative constraints. Despite that the optimization-based methods have been improved significantly in terms of producing aesthetic and legible layouts, the existing (semi-) automatic methods are still limited regarding 1) efficient exploration of the storyline design space and 2) flexible customization of storyline layouts. In this work, we propose a reinforcement learning framework to train an AI agent that assists users in exploring the design space efficiently and generating well-optimized storylines. Based on the framework, we introduce PlotThread, an authoring tool that integrates a set of flexible interactions to support easy customization of storyline visualizations. To seamlessly integrate the AI agent into the authoring process, we employ a mixed-initiative approach where both the agent and designers work on the same canvas to boost the collaborative design of storylines. We evaluate the reinforcement learning model through qualitative and quantitative experiments and demonstrate the usage of PlotThread using a collection of use cases.

cs.HC

How much H and He is "hidden" in SNe Ib/c? -- II. Intermediate-mass objects: a 22 M$_{\odot}$ progenitor case study

Stripped envelope supernovae are a sub-class of core collapse supernovae showing several stages of H/He shell stripping that determines the class: H-free/He-poor SNe are classified as Type Ic, H-poor/He-rich are Type Ib, and H/He-rich are Type IIb. Stripping H/He with only stellar wind requires significantly higher mass loss rates than observed while binary-involved mass transfer may usually not strip enough to produce H/He free SNe. Type Ib/c SNe are sometimes found to include weak H/He transient lines as a product of a trace amount of H/He left over from stripping processes. The extent and mass of the H/He required to produce these lines is not well known. In this work, a 22 M$_{\odot}$ progenitor model is stripped of the H/He shells in five steps prior to collapse and then exploded at four explosion energies. Requiring both optical and NIR He I lines for helium identification does not allow much He mass to be hidden in SE--SNE. Increasing the mass of He above the CO core delays the visibility of O I 7774 in early spectra. Our SN Ib-like models are capable of reproducing the spectral evolution of a set of observed SNe with reasonable estimated $E_\mathrm{k}$ accuracy. Our SN\,IIb-like models can partially reproduce low energy observed SN IIb, but we find no observed comparison for the SN IIb-like models with high $E_\mathrm{k}$.

astro-ph.HE

Type Ic Supernova of a 22 $\text{M}_{\odot}$ Progenitor

Type Ic supernovae (SNe Ic) are a sub-class of core-collapse supernovae that exhibit no helium or hydrogen lines in their spectra. Their progenitors are thought to be bare carbon-oxygen cores formed during the evolution of massive stars that are stripped of their hydrogen and helium envelopes sometime before collapse. SNe Ic present a range of luminosities and spectral properties, from luminous GRB-SNe with broad-lined spectra to less luminous events with narrow-line spectra. Modelling SNe\,Ic reveals a wide range of both kinetic energies, ejecta masses, and $^{56}\text{Ni}$ masses. To explore this diversity and how it comes about, light curves and spectra are computed from the ejecta following the explosion of an initially 22 $\text{M}_{\odot}$ progenitor that was artificially stripped of its hydrogen and helium shells, producing a bare CO core of $\sim$ 5 $\text{M}_{\odot}$, resulting in an ejected mass of $\sim$ 4 $\text{M}_{\odot}$, which is an average value for SNe Ic. Four different explosion energies are used that cover a range of observed SNe. Finally, $^{56}\text{Ni}$ and other elements are artificially mixed in the ejecta using two approximations to determine how element distribution affects light curves and spectra. The combination of different explosion energy and degree of mixing produces spectra that roughly replicate the distribution of near-peak spectroscopic features of SNe Ic. High explosion energies combined with extensive mixing can produce red, broad-lined spectra, while minimal mixing and a lower explosion energy produce bluer, narrow-lined spectra.

astro-ph.HE

The Explosion of Helium Stars Evolved With Mass Loss

Light curves, explosion energies, and remnant masses are calculated for a grid of supernovae resulting from massive helium stars that have been evolved including mass loss. These presupernova stars should approximate the results of binary evolution for stars in interacting systems that lose their envelopes close to the time of helium core ignition. Initial helium star masses are in the range 2.5 to 40\,\Msun, which correspond to main sequence masses of about 13 to 90\,\Msun. Common Type Ib and Ic supernovae result from stars whose final masses are approximately 2.5 to 5.6\,\Msun. For heavier stars, a large fraction of collapses lead to black holes, though there is an island of explodability for presupernova masses near 10\,\Msun. The median neutron star mass in binaries is 1.35--1.38\,\Msun \ and the median black hole mass is between 9 and 11\,\Msun. Even though black holes less massive than 5 \Msun\ are rare, they are predicted down to the maximum neutron star mass. There is no empty ``gap'', only a less populated mass range. For standard assumptions regarding the explosions and nucleosynthesis, the models predict light curves that are fainter than the brighter common Type Ib and Ic supernovae. Even with a very liberal, but physically plausible increase in $^{56}$Ni production, the highest energy models are fainter, at peak, than 10$^{42.6}$\,erg\,s$^{-1}$, and very few approach that limit. The median peak luminosity ranges from 10$^{42.0}$ to 10$^{42.3}$\,erg\,s$^{-1}$. Possible alternatives to the standard neutrino-powered and radioactive-illuminated models are explored. Magnetars are a promising alternative. Several other unusual varieties of Type I supernovae at both high and low mass are explored.

astro-ph.HE

Towards realistic HPC models of the neuromuscular system

Realistic simulations of detailed, biophysics-based, multi-scale models require very high resolution and, thus, large-scale compute facilities. Existing simulation environments, especially for biomedical applications, are designed to allow for a high flexibility and generality in model development. Flexibility and model development, however, are often a limiting factor for large-scale simulations. Therefore, new models are typically tested and run on small-scale compute facilities. By using a detailed biophysics-based, chemo-electromechanical skeletal muscle model and the international open-source software library OpenCMISS as an example, we present an approach to upgrade an existing muscle simulation framework from a moderately parallel version towards a massively parallel one that scales both in terms of problem size and in terms of the number of parallel processes. For this purpose, we investigate different modeling, algorithmic and implementational aspects. We present improvements addressing both numerical and parallel scalability. In addition, our approach includes a novel visualization environment, which is based on the MegaMol environment capable of handling large amounts of simulated data. It offers a platform for fast visualization prototyping, distributed rendering, and advanced visualization techniques. We present results of a variety of scaling studies at the Tier-1 supercomputer HazelHen at the High Performance Computing Center Stuttgart (HLRS). We improve the overall runtime by a factor of up to 2.6 and achieved good scalability on up to 768 cores, where the previous implementation used only 4 cores.

cs.CE

Visualization of Feature Separation in Advected Scalar Fields

Scalar features in time-dependent fluid flow are traditionally visualized using 3D representation, and their topology changes over time are often conveyed with abstract graphs. Using such techniques, however, the structural details of feature separation and the temporal evolution of features undergoing topological changes are difficult to analyze. In this paper, we propose a novel approach for the spatio-temporal visualization of feature separation that segments feature volumes into regions with respect to their contribution to distinct features after separation. To this end, we employ particle-based feature tracking to find volumetric correspondences between features at two different instants of time. We visualize this segmentation by constructing mesh boundaries around each volume segment of a feature at the initial time that correspond to the separated features at the later time. To convey temporal evolution of the partitioning within the investigated time interval, we complement our approach with spatio-temporal separation surfaces. For the application of our approach to multiphase flow, we additionally present a feature-based corrector method to ensure phase-consistent particle trajectories. The utility of our technique is demonstrated by application to two-phase (liquid-gas) and multi-component (liquid-liquid) flows where the scalar field represents the fraction of one of the phases.

cs.GR

Erratum: Progenitor-explosion connection and remnant birth masses for neutrino-driven supernovae of iron-core progenitors (2012, ApJ, 757, 69)

An erroneous interpretation of the hydrodynamical results led to an incorrect determination of the fallback masses in Ugliano et al. (2012), which also (on a smaller level) affects the neutron star masses provided in that paper. This problem was already addressed and corrected in the follow-up works by Ertl et al. (2015) and Sukhbold et al. (2015). Therefore, the reader is advised to use the new data of the latter two publications. In the remaining text of this Erratum we present the differences of the old and new fallback results in detail and explain the origin of the mistake in the original analysis by Ugliano et al. (2012).

astro-ph.HE