arXiv subjects

Andreas Wagner

Publications and source records attributed to Andreas Wagner.

At least 37 records · Page 2Linked to original sources

arXiv2023

Photometry of Type II Supernova SN 2023ixf with a Worldwide Citizen Science Network

We present highly sampled photometry of the supernova (SN) 2023ixf, a Type II SN in M101, beginning 2 days before its first known detection. To gather these data, we enlisted the global Unistellar Network of citizen scientists. These 252 observations from 115 telescopes show the SN's rising brightness associated with shock emergence followed by gradual decay. We measure a peak $M_{V}$ = -18.18 $\pm$ 0.09 mag at 2023-05-25 21:37 UTC in agreement with previously published analyses.

astro-ph.HE↗

arXiv2023

The Automatic Identification and Tracking of Coronal Flux Ropes -- Part I: Footpoints and Fluxes

Investigating the early-stage evolution of an erupting flux rope from the Sun is important to understand the mechanisms of how it looses its stability and its space weather impacts. Our aim is to develop an efficient scheme for tracking the early dynamics of erupting solar flux ropes and use the algorithm to analyse its early-stage properties. The algorithm is tested on a data-driven simulation of an eruption that took place in active region AR12473. We investigate the modelled flux rope's footpoint movement and magnetic flux evolution and compare with observational data from the Solar Dynamics Observatory's Atmospheric Imaging Assembly in the 211 $\unicode{x212B}$ and 1600 $\unicode{x212B}$ channels. To carry out our analysis, we use the time-dependent data-driven magnetofrictional model (TMFM). We also perform another modelling run, where we stop the driving of the TMFM midway through the flux rope's rise through the simulation domain and evolve it instead with a zero-beta magnetohydrodynamic (MHD) approach. The developed algorithm successfully extracts a flux rope and its ascend through the simulation domain. We find that the movement of the modelled flux rope footpoints showcases similar trends in both TMFM and relaxation MHD run: they recede from their respective central location as the eruption progresses and the positive polarity footpoint region exhibits a more dynamic behaviour. The ultraviolet brightenings and extreme ultraviolet dimmings agree well with the models in terms of their dynamics. According to our modelling results, the toroidal magnetic flux in the flux rope first rises and then decreases. In our observational analysis, we capture the descending phase of toroidal flux. In conclusion, the extraction algorithm enables us to effectively study the flux rope's early dynamics and derive some of its key properties such as footpoint movement and toroidal magnetic flux.

astro-ph.SR↗

arXiv2023

Ion Intercalation in Lanthanum Strontium Ferrite for Aqueous Electrochemical Energy Storage Devices

Ion intercalation of perovskite oxides in liquid electrolytes is a very promising method for controlling their functional properties while storing charge, which opens the potential application in different energy and information technologies. Although the role of defect chemistry in the oxygen intercalation in a gaseous environment is well established, the mechanism of ion intercalation in liquid electrolytes at room temperature is poorly understood. In this study, the defect chemistry during ion intercalation of La0.5Sr0.5FeO3-δ thin films in alkaline electrolytes is studied. Oxygen and proton intercalation into the LSF perovskite structure is observed at moderate electrochemical potentials (0.5 V to -0.4 V), giving rise to a change in the oxidation state of Fe (as a charge compensation mechanism). The variation of the concentration of holes as a function of the intercalation potential was characterized by in-situ ellipsometry and the concentration of electron holes was indirectly quantified for different electrochemical potentials. Finally, a dilute defect chemistry model that describes the variation of defect species during ionic intercalation was developed.

cond-mat.mtrl-sci↗

arXiv2023

A phase-field model for non-small cell lung cancer under the effects of immunotherapy

Formulating tumor models that predict growth under therapy is vital for improving patient-specific treatment plans. In this context, we present our recent work on simulating non-small-scale cell lung cancer (NSCLC) in a simple, deterministic setting for two different patients receiving an immunotherapeutic treatment. At its core, our model consists of a Cahn-Hilliard-based phase-field model describing the evolution of proliferative and necrotic tumor cells. These are coupled to a simplified nutrient model that drives the growth of the proliferative cells and their decay into necrotic cells. The applied immunotherapy decreases the proliferative cell concentration. Here, we model the immunotherapeutic agent concentration in the entire lung over time by an ordinary differential equation (ODE). Finally, reaction terms provide a coupling between all these equations. By assuming spherical, symmetric tumor growth and constant nutrient inflow, we simplify this full 3D cancer simulation model to a reduced 1D model. We can then resort to patient data gathered from computed tomography (CT) scans over several years to calibrate our model. For the reduced 1D model, we show that our model can qualitatively describe observations during immunotherapy by fitting our model parameters to existing patient data. Our model covers cases in which the immunotherapy is successful and limits the tumor size, as well as cases predicting a sudden relapse, leading to exponential tumor growth. Finally, we move from the reduced model back to the full 3D cancer simulation in the lung tissue. Thereby, we show the predictive benefits a more detailed patient-specific simulation including spatial information could yield in the future.

math.NA↗

arXiv2022

Study of a possible silicon photomultiplier based readout of the large plastic scintillator neutron detector NeuLAND

The NeuLAND (New Large-Area Neutron Detector) plastic-scintillator-based time-of-flight detector for 0.1-1.6 GeV neutrons is currently under construction at the Facility for Antiproton and Ion Research (FAIR), Darmstadt, Germany. In its final configuration, NeuLAND will consist of 3000 2.7 m $\times$ 5 cm $\times$ 5 cm big plastic scintillator bars that are read out on each end by fast timing photomultipliers. Here, data from a comprehensive study of an alternative light readout scheme using silicon photomultipliers (SiPM) are reported. For this purpose, a NeuLAND bar was instrumented on each end with a SiPM-based prototype of the same geometry as a 1'' photomultiplier tube, including four 6 $\times$ 6 mm$^2$ SiPMs, amplifiers, high voltage supply, and microcontroller. Tests were carried out using the 35 MeV electron beam from the superconducting Electron Linac for beams with high Brilliance and low Emittance (ELBE) with its picosecond-level time jitter in two different modes of operation, namely parasitic mode with one electron per bunch and single-user mode with 1-60 electrons per bunch. Acqiris fast digitisers were used for data acquisition. In addition, off-beam tests using cosmic rays and the NeuLAND data acquisition scheme have been carried out. Typical time resolutions of $σ_t\leq$ 120 ps were found for $\geq$95% efficiency for minimum ionising particles, improving on previous work at ELBE and exceeding the NeuLAND timing goal of $σ_t$ < 150 ps. Over a range of 10-300 MeV deposited energy in the NeuLAND bar, the gain was found to deviate by $\leq$10% ($\leq$20%) from linearity for 35 mm (75 mm) SiPM pitch, respectively, satisfactory for calorimetric use of the full NeuLAND detector. The dark rate of the prototype studied was found to be lower than the expected cosmic-ray induced background in NeuLAND.

physics.ins-det↗

arXiv2022

A matrix-free ILU realization based on surrogates

Matrix-free techniques play an increasingly important role in large-scale simulations. Schur complement techniques and massively parallel multigrid solvers for second-order elliptic partial differential equations can significantly benefit from reduced memory traffic and consumption. The matrix-free approach often restricts solver components to purely local operations, for instance, the Jacobi- or Gauss--Seidel-Smoothers in multigrid methods. An incomplete LU (ILU) decomposition cannot be calculated from local information and is therefore not amenable to an on-the-fly computation which is typically needed for matrix-free calculations. It generally requires the storage and factorization of a sparse matrix which contradicts the low memory requirements in large scale scenarios. In this work, we propose a matrix-free ILU realization. More precisely, we introduce a memory-efficient, matrix-free ILU(0)-Smoother component for low-order conforming finite elements on tetrahedral hybrid grids. Hybrid grids consist of an unstructured macro-mesh which is subdivided into a structured micro-mesh. The ILU(0) is used for degrees-of-freedom assigned to the interior of macro-tetrahedra. This ILU(0)-Smoother can be used for the efficient matrix-free evaluation of the Steklov-Poincare operator from domain-decomposition methods. After introducing and formally defining our smoother, we investigate its performance on refined macro-tetrahedra. Secondly, the ILU(0)-Smoother on the macro-tetrahedrons is implemented via surrogate matrix polynomials in conjunction with a fast on-the-fly evaluation scheme resulting in an efficient matrix-free algorithm. The polynomial coefficients are obtained by solving a least-squares problem on a small part of the factorized ILU(0) matrices to stay memory efficient. The convergence rates of this smoother with respect to the polynomial order are thoroughly studied.

math.NA↗

arXiv2022

Short- and long-term forecasting of electricity prices using embedding of calendar information in neural networks

Electricity prices strongly depend on seasonality of different time scales, therefore any forecasting of electricity prices has to account for it. Neural networks have proven successful in short-term price-forecasting, but complicated architectures like LSTM are used to integrate the seasonal behaviour. This paper shows that simple neural network architectures like DNNs with an embedding layer for seasonality information can generate a competitive forecast. The embedding-based processing of calendar information additionally opens up new applications for neural networks in electricity trading, such as the generation of price forward curves. Besides the theoretical foundation, this paper also provides an empirical multi-year study on the German electricity market for both applications and derives economical insights from the embedding layer. The study shows that in short-term price-forecasting the mean absolute error of the proposed neural networks with an embedding layer is better than the LSTM and time-series benchmark models and even slightly better as our best benchmark model with a sophisticated hyperparameter optimization. The results are supported by a statistical analysis using Friedman and Holm's tests.

stat.AP↗

arXiv2022

A 1D-0D-3D coupled model for simulating blood flow and transport processes in breast tissue

In this work, we present mixed dimensional models for simulating blood flow and transport processes in breast tissue and the vascular tree supplying it. These processes are considered, to start from the aortic inlet to the capillaries and tissue of the breast. Large variations in biophysical properties and flow conditions exist in this system necessitating the use of different flow models for different geometries and flow regimes. Large variations in biophysical properties and flow conditions exist in this system necessitating the use of different flow models for different geometries and flow regimes. In total, we consider four different model types. First, a system of 1D nonlinear hyperbolic PDEs is considered to simulate blood flow in larger arteries with highly elastic vessel walls. Second, we assign 1D linearized hyperbolic PDEs to model the smaller arteries with stiffer vessel walls. The third model type consists of ODE systems (0D models). It is used to model the arterioles and peripheral circulation. Finally, homogenized 3D porous media models are considered to simulate flow and transport in capillaries and tissue within the breast volume. Sink terms are used to account for the influence of the venous and lymphatic systems. Combining the four model types, we obtain two different 1D-0D-3D coupled models for simulating blood flow and transport processes: The first 1D-0D-3D model covers the whole path from the aorta to the breast, while the second model is a sub-model obtained by restriction to breast vasculature and tissue making possible a significant reduction in computational cost. Several numerical experiments are conducted that demonstrate realistic flow simulations compared to existing data on blood flow in human breast and vascular system.

q-bio.TO↗

arXiv2021

Modeling and simulation of vascular tumors embedded in evolving capillary networks

In this work, we present a coupled 3D-1D model of solid tumor growth within a dynamically changing vascular network to facilitate realistic simulations of angiogenesis. Additionally, the model includes erosion of the extracellular matrix, interstitial flow, and coupled flow in blood vessels and tissue. We employ continuum mixture theory with stochastic Cahn--Hilliard type phase-field models of tumor growth. The interstitial flow is governed by a mesoscale version of Darcy's law. The flow in the blood vessels is controlled by Poiseuille flow, and Starling's law is applied to model the mass transfer in and out of blood vessels. The evolution of the network of blood vessels is orchestrated by the concentration of the tumor angiogenesis factors (TAFs); blood vessels grow towards the increasing TAFs concentrations. This process is not deterministic, allowing random growth of blood vessels and, therefore, due to the coupling of nutrients in tissue and vessels, makes the growth of tumors stochastic. We demonstrate the performance of the model by applying it to a variety of scenarios. Numerical experiments illustrate the flexibility of the model and its ability to generate satellite tumors. Simulations of the effects of angiogenesis on tumor growth are presented as well as sample-independent features of cancer.

q-bio.TO↗

arXiv2021

A matrix math facility for Power ISA(TM) processors

Power ISA(TM) Version 3.1 has introduced a new family of matrix math instructions, collectively known as the Matrix-Multiply Assist (MMA) facility. The instructions in this facility implement numerical linear algebra operations on small matrices and are meant to accelerate computation-intensive kernels, such as matrix multiplication, convolution and discrete Fourier transform. These instructions have led to a power- and area-efficient implementation of a high throughput math engine in the future POWER10 processor. Performance per core is 4 times better, at constant frequency, than the previous generation POWER9 processor. We also advocate the use of compiler built-ins as the preferred way of leveraging these instructions, which we illustrate through case studies covering matrix multiplication and convolution.

cs.AR↗

arXiv2021

Mapping the Structure of Oxygen-Doped Wurtzite Aluminum Nitride Coatings From Ab Initio Random Structure Search and Experiments

Machine learning is changing how we design and interpret experiments in materials science. In this work, we show how unsupervised learning, combined with ab initio modeling, improves our understanding of structural metastability in multicomponent alloys. We use the example case of Al-O-N alloys where the formation of aluminum vacancies in wurtzite AlN upon the incorporation of substitutional oxygen can be seen as a general mechanism of solids where crystal symmetry is reduced to stabilize defects. The ideal AlN wurtzite crystal structure occupation cannot be matched due to the presence of an aliovalent hetero-element into the structure. The traditional interpretation of the c-lattice shrinkage in sputter-deposited Al-O-N films from X-ray diffraction (XRD) experiments suggests the existence of a solubility limit at 8at.% oxygen content. Here we show that such naive interpretation is misleading. We support XRD data with a machine learning analysis of ab initio simulations and positron annihilation lifetime spectroscopy data, revealing no signs of a possible solubility limit. Instead, the presence of a wide range of non-equilibrium oxygen-rich defective structures emerging at increasing oxygen contents suggests that the formation of grain boundaries is the most plausible mechanism responsible for the lattice shrinkage measured in Al-O-N sputtered films.

cond-mat.mtrl-sci↗

arXiv2020

Unifying the theory of storage and the risk premium by an unobservable intrinsic electricity price

In this paper we introduce a new concept for modelling electricity prices through the introduction of an unobservable intrinsic electricity price $p(τ)$. We use it to connect the classical theory of storage with the concept of a risk premium. We derive prices for all common contracts such as the intraday spot price, the day-ahead spot price, and futures prices. Finally, we propose an explicit model from the class of structural models and conduct an empirical analysis, where we find an overall negative risk premium.

q-fin.MF↗

arXiv2020

Dissolution of donor-vacancy clusters in heavily doped n-type germanium

The n-type doping of Ge is a self-limiting process due to the formation of vacancy-donor complexes (DnV with n <= 4) that deactivate the donors. This work unambiguously demonstrates that the dissolution of the dominating P4V clusters in heavily phosphorus-doped Ge epilayers can be achieved by millisecond-flash lamp annealing at about 1050 K. The P4V cluster dissolution increases the carrier concentration by more than three-fold together with a suppression of phosphorus diffusion. Electrochemical capacitance-voltage measurements in conjunction with secondary ion mass spectrometry, positron annihilation lifetime spectroscopy and theoretical calculations enabled us to address and understand a fundamental problem that has hindered so far the full integration of Ge with complementary-metal-oxide-semiconductor technology.

cond-mat.mtrl-sci↗

arXiv2020

Resiliency in Numerical Algorithm Design for Extreme Scale Simulations

This work is based on the seminar titled ``Resiliency in Numerical Algorithm Design for Extreme Scale Simulations'' held March 1-6, 2020 at Schloss Dagstuhl, that was attended by all the authors. Naive versions of conventional resilience techniques will not scale to the exascale regime: with a main memory footprint of tens of Petabytes, synchronously writing checkpoint data all the way to background storage at frequent intervals will create intolerable overheads in runtime and energy consumption. Forecasts show that the mean time between failures could be lower than the time to recover from such a checkpoint, so that large calculations at scale might not make any progress if robust alternatives are not investigated. More advanced resilience techniques must be devised. The key may lie in exploiting both advanced system features as well as specific application knowledge. Research will face two essential questions: (1) what are the reliability requirements for a particular computation and (2) how do we best design the algorithms and software to meet these requirements? One avenue would be to refine and improve on system- or application-level checkpointing and rollback strategies in the case an error is detected. Developers might use fault notification interfaces and flexible runtime systems to respond to node failures in an application-dependent fashion. Novel numerical algorithms or more stochastic computational approaches may be required to meet accuracy requirements in the face of undetectable soft errors. The goal of this Dagstuhl Seminar was to bring together a diverse group of scientists with expertise in exascale computing to discuss novel ways to make applications resilient against detected and undetected faults. In particular, participants explored the role that algorithms and applications play in the holistic approach needed to tackle this challenge.

cs.DC↗

arXiv2020

Bremsstrahlung emission and plasma characterization driven by moderately relativistic laser-plasma interactions

Relativistic electrons generated by the interaction of petawatt-class short laser pulses with solid targets can be used to generate bright X-rays via bremsstrahlung. The efficiency of laser energy transfer into these electrons depends on multiple parameters including the focused intensity and pre-plasma level. This paper reports experimental results from the interaction of a high intensity petawatt-class glass laser pulses with solid targets at a maximum intensity of $10^{19}$ W/cm$^2$. In-situ measurements of specularly reflected light are used to provide an upper bound of laser absorption and to characterize focused laser intensity, the pre-plasma level and the generation mechanism of second harmonic light. The measured spectrum of electrons and bremsstrahlung radiation provide information about the efficiency of laser energy transfer.

physics.plasm-ph↗

arXiv2020

Vacancy-Hydrogen Interaction in Niobium during Low-Temperature Baking

A recently discovered modified low-temperature baking leads to reduced surface losses and an increase of the accelerating gradient of superconducting TESLA shape cavities. We will show that the dynamics of vacancy-hydrogen complexes at low-temperature baking lead to a suppression of lossy nanohydrides at 2\,K and thus a significant enhancement of accelerator performance. Utilizing Doppler broadening Positron Annihilation Spectroscopy, Positron Annihilation Lifetime Spectroscopy and instrumented nanoindentation, samples made from European XFEL niobium sheets were investigated. We studied the evolution of vacancies in bulk samples and in the sub-surface region and their interaction with hydrogen at different temperature levels during {\it in-situ} and {\it ex-situ} annealing.

cond-mat.supr-con↗

arXiv2020

Nitrogen magneto-ionics

So far, magneto-ionics, understood as voltage-driven ion transport in magnetic materials, has largely relied on controlled migration of oxygen ion/vacancy and, to a lesser extent, lithium and hydrogen. Here, we demonstrate efficient, room-temperature, voltage-driven nitrogen transport (i.e., nitrogen magneto-ionics) by electrolyte-gating of a single CoN film (without an ion-reservoir layer). Nitrogen magneto-ionics in CoN is compared to oxygen magneto-ionics in Co3O4, both layers showing a nanocrystalline face-centered-cubic structure and reversible voltage-driven ON-OFF ferromagnetism. In contrast to oxygen, nitrogen transport occurs uniformly creating a plane-wave-like migration front, without assistance of diffusion channels. Nitrogen magneto-ionics requires lower threshold voltages and exhibits enhanced rates and cyclability. This is due to the lower activation energy for ion diffusion and the lower electronegativity of nitrogen compared to oxygen. These results are appealing for the use of magneto-ionics in nitride semiconductor devices, in applications requiring endurance and moderate speeds of operation, such as brain-inspired computing.

cond-mat.mtrl-sci↗

arXiv2020

Boosting room temperature magneto-ionics in Co3O4

Voltage control of magnetism through electric field-induced oxygen motion (magneto-ionics) could represent a significant breakthrough in the pursuit for new strategies to enhance energy efficiency in a large variety of magnetic devices, such as magnetic micro-electro-mechanical systems (MEMS), magnetic logics, spin electronics, or neuromorphic computing, i.e., envisaging ultra-low power emulation of the biological synapse. Boosting the induced changes in magnetization, magneto-ionic motion and cyclability (endurance) continue to be key challenges to turn magneto-ionic phenomena into real applications. Here, we demonstrate that, without degrading cyclability, room temperature magneto-ionic motion in electrolyte-gated paramagnetic and fairly thick (> 100 nm) Co3O4 films largely depends on the configuration used to apply the electric field. In particular, magneto-ionic effects are significantly increased both in terms of generated magnetization (6 times larger: from 118.5 to 699.2 emu cm-3) and speed (35 times faster: from 33.1 to 1170.8 emu cm-3 h-1) if the electric field is applied across a conducting buffer layer (grown underneath the Co3O4 films), instead of directly contacting Co3O4. This is attributed to a greater uniformity and strength of the applied electric field when using the conducting layer. These results may trigger the use of oxygen magneto-ionics into promising new technologies, such as magnetic MEMS or brain-inspired computing, which require endurance and moderate speeds of operation.

cond-mat.mtrl-sci↗