Laser-Cooled Ion Beams and Strongly Coupled Plasmas for Precision Experiments

The first part of this thesis summarizes the results of laser-cooling of relativistic C3+ ion beams at the ESR/GSI. It is shown that laser cooling at high beam energies is feasible and that momentum spreads much smaller than those observed for electron cooling can be achieved. Resulty indicate that space-charge dominated beams have been observed, reaching the regime of strong coupling which is an essential prerequisite for beam crystallization. Moderate electron cooling was employed to create three-dimensionally cold beams. With the laser cooled beams it was possible to perform precision VUV spectroscopy of the cooling transition. In the second part results on large-scale realistic simulations on the stopping of highly charged ions in a laser-cooled one-component plasma of 24Mg+ ions confined in a harmonic potential are presented. It is shown that cooling times short enough for cooling unstable nuclei can be achieved and fast recooling of the plasma is possible. With this cooling scheme highly charged ions for precision experiments such as mass spectrometry in Penning traps at millikelvin temperatures can be delivered

Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library

We present an analysis on optimizing performance of a single C++11 source code using the Alpaka hardware abstraction library. For this we use the general matrix multiplication (GEMM) algorithm in order to show that compilers can optimize Alpaka code effectively when tuning key parameters of the algorithm. We do not intend to rival existing, highly optimized DGEMM versions, but merely choose this example to prove that Alpaka allows for platform-specific tuning with a single source code. In addition we analyze the optimization potential available with vendor-specific compilers when confronted with the heavily templated abstractions of Alpaka. We specifically test the code for bleeding edge architectures such as Nvidia's Tesla P100, Intel's Knights Landing (KNL) and Haswell architecture as well as IBM's Power8 system. On some of these we are able to reach almost 50\% of the peak floating point operation performance using the aforementioned means. When adding compiler-specific #pragmas we are able to reach 5 TFLOPS/s on a P100 and over 1 TFLOPS/s on a KNL system.Comment: Accepted paper for the P\^{}3MA workshop at the ISC 2017 in Frankfur

Quantitatively consistent computation of coherent and incoherent radiation in particle-in-cell codes - a general form factor formalism for macro-particles

Quantitative predictions from synthetic radiation diagnostics often have to consider all accelerated particles. For particle-in-cell (PIC) codes, this not only means including all macro-particles but also taking into account the discrete electron distribution associated with them. This paper presents a general form factor formalism that allows to determine the radiation from this discrete electron distribution in order to compute the coherent and incoherent radiation self-consistently. Furthermore, we discuss a memory-efficient implementation that allows PIC simulations with billions of macro-particles. The impact on the radiation spectra is demonstrated on a large scale LWFA simulation.Comment: Proceedings of the EAAC 2017, This manuscript version is made available under the CC-BY-NC-ND 4.0 licens

Dosimetric evidence confirms computational model for magnetic field induced dose distortions of therapeutic proton beams

Given the sensitivity of proton therapy to anatomical variations, this cancer treatment modality is expected to benefit greatly from integration with magnetic resonance (MR) imaging. One of the obstacles hindering such an integration are strong magnetic field induced dose distortions. These have been predicted in simulation studies, but no experimental validation has been performed so far. Here we show the first measurement of planar distributions of dose deposited by therapeutic proton pencil beams traversing a one-Tesla transversal magnetic field while depositing energy in a tissue-like phantom using film dosimetry. The lateral beam deflection ranges from one millimeter to one centimeter for 80 to 180 MeV beams. Simulated and measured deflection agree within one millimeter for all studied energies. These results proof that the magnetic field induced proton beam deflection is both measurable and accurately predictable. This demonstrates the feasibility of accurate dose measurement and hence validates dose predictions for the framework of MR-integrated proton therapy

On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective

We implement and benchmark parallel I/O methods for the fully-manycore driven particle-in-cell code PIConGPU. Identifying throughput and overall I/O size as a major challenge for applications on today's and future HPC systems, we present a scaling law characterizing performance bottlenecks in state-of-the-art approaches for data reduction. Consequently, we propose, implement and verify multi-threaded data-transformations for the I/O library ADIOS as a feasible way to trade underutilized host-side compute potential on heterogeneous systems for reduced I/O latency.Comment: 15 pages, 5 figures, accepted for DRBSD-1 in conjunction with ISC'1

A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry

With the growth of computational resources, the scope of electronic structure simulations has increased greatly. Artificial intelligence and robust data analysis hold the promise to accelerate large-scale simulations and their analysis to hitherto unattainable scales. Machine learning is a rapidly growing field for the processing of such complex datasets. It has recently gained traction in the domain of electronic structure simulations, where density functional theory takes the prominent role of the most widely used electronic structure method. Thus, DFT calculations represent one of the largest loads on academic high-performance computing systems across the world. Accelerating these with machine learning can reduce the resources required and enables simulations of larger systems. Hence, the combination of density functional theory and machine learning has the potential to rapidly advance electronic structure applications such as in-silico materials discovery and the search for new chemical reaction pathways. We provide the theoretical background of both density functional theory and machine learning on a generally accessible level. This serves as the basis of our comprehensive review including research articles up to December 2020 in chemistry and materials science that employ machine-learning techniques. In our analysis, we categorize the body of research into main threads and extract impactful results. We conclude our review with an outlook on exciting research directions in terms of a citation analysis

Flying Adversarial Patches: Manipulating the Behavior of Deep Learning-based Autonomous Multirotors

Autonomous flying robots, e.g. multirotors, often rely on a neural network that makes predictions based on a camera image. These deep learning (DL) models can compute surprising results if applied to input images outside the training domain. Adversarial attacks exploit this fault, for example, by computing small images, so-called adversarial patches, that can be placed in the environment to manipulate the neural network's prediction. We introduce flying adversarial patches, where an image is mounted on another flying robot and therefore can be placed anywhere in the field of view of a victim multirotor. For an effective attack, we compare three methods that simultaneously optimize the adversarial patch and its position in the input image. We perform an empirical validation on a publicly available DL model and dataset for autonomous multirotors. Ultimately, our attacking multirotor would be able to gain full control over the motions of the victim multirotor.Comment: 6 pages, 5 figures, Workshop on Multi-Robot Learning, International Conference on Robotics and Automation (ICRA