Current research projects

Multi-messenger X-ray Science – electron densities from a combined analysis of elastic x-ray scattering and x-ray emission data

Supervisors: Prof. Nina Rohringer (DESY, UHH, Fachbereich Physik), Jun.-Prof. Christina Brandt (UHH, Fachbereich Mathematik)

We propose an interdisciplinary project between the natural and mathematical sciences to develop novel x-ray data analysis methods for combined x-ray diffraction and x-ray emission spectroscopy measurements at high-brilliance x-ray sources. While coherent xray diffraction gives access to the structure (position of atoms) of a chemical compound or reaction unit, x-ray emission spectroscopy is a complementary and sensitive probe to the changes of the chemical bonds. The interpretation of the latter strongly relies on comparison with theoretically calculated spectra (electronic structure calculations). Pioneering experiments at storage-ring based x-ray sources and x-ray free electron lasers (XFELs) have shown the possibility for combining x-ray diffraction, x-ray scattering and x-ray emission spectroscopy in one high-quality experiment. Currently, the data analysis and reconstruction of the underlying physical quantities of interest (electron density, oxidation state, electron affinity, etc.) are undertaken independently from the two techniques. In the spirit of quantum crystallography, we propose to refine electronic structure (quantum chemistry) calculations by constraining the minimization problem of the total energy by measured x-ray structure factors and x-ray emission spectra. The goal is thus to predict the valence electron wave function by a  combined theoretical and experimental method.

Next Generation Integrative Modeling for Cryo-Electron Microscopy

Supervisors: Prof. Michael Kolbe (HZI, CSSB, UHH, Fachbereich Chemie), Prof. Matthias Rarey (ZBH, UHH, Fachbereich Informatik)

Cryo-electron microscopy (cryo-EM) is an emerging technology in the field of structural biology. Especially for larger biomolecular assemblies (hundreds of kDa to mDa) it is becoming the method of choice and might play a crucial role in the understanding of cellular processes at the molecular level. Although cryo-EM structures with near to atomic resolution exist, the most frequent scenario is models at slightly lower resolutions of 4-8 Å. Moreover, the resolution can vary considerably for different volumes of a given cryo-EM map which makes structural interpretation sometimes challenging. To overcome the information gap, various different data sources are usually combined into an integrative modelling approach. Highly sophisticated computational techniques combining spatial pattern recognition with precise molecular modelling are required to create overall models with atomic resolution. The project focuses on two challenging problems that researchers often face when working with EM. We want to develop better tools for the interpretation of mid resolution electron density maps by integrating atomic structures from other biophysical methods under consideration of conformational flexibility. Second, we want to improve the detection of self-similarity and local symmetry in density maps and consider this information upon model building

High Performance Simulations of Next Generation Light Sources

Supervisors: Prof. Franz Kärtner (DESY, UHH, Fachbereich Physik), Dr. Jens Osterhoff (DESY), Prof. Sabine Le Borne (TUHH, Mathematics), Dr. Jens-Peter Zemke (TUHH, Mathematics)

Next generation light sources, X-ray Free-Electron Lasers (XFELs) driven by compact electron accelerators and using short period undulators show highly complex behavior and are extremely challenging to simulate. Some 10 million electrons are involved in a highly nonlinear motion with collective effects driven by electromagnetic fields of a wide range of wavelengths. Understanding and optimization of X-ray production with these sources requires the simultaneous computation of the emission and motion of electrons and electromagnetic fields on multiple time and spatial scales.
In this project, modern data science techniques/algorithms will be developed to process the enormous amounts of data in these challenging simulations. The focus will lie on PDE modeling and the development of efficient solvers based on data-driven data-sparse preconditioners for the linear systems arising in space-time finite-element  discretisations. The advanced techniques will be implemented to enable the analysis and design of novel FEL schemes via manipulation of the electron phase space starting from the photo cathode in the electron gun to novel undulator configurations and implementations such as optical undulators.

Search for Dark Matter with the CMS experiment via Neural Networks with Multi-Task Learning

Supervisors: Prof. Christian Schwanenberger (DESY, UHH, Fachbereich Physik), Dr. Christian Seifert (TUHH, Institute of Mathematics)

The big unknown in particle physics is Dark Matter. In order to detect the presence of Dark Matter, we use data coming from the CMS experiment at the Large Hadron Collider at CERN. To classify the data into potential Dark Matter candidates and known elementary particles, a deep neural network will be implemented, trained, and fine-tuned. To obtain further characteristic properties of Dark Matter particles, such as the nature of the interaction with known particles, an attribute learning model combined with multi-task learning techniques will be exploited. A stable classification with characteristic meta-information of Dark Matter will be a huge step forward for new physics searches since to our knowledge such an approach had not been applied ever in particle physics.

Distributed Self-Healing Infrastructure for High-Speed Scientific Data Processing

Supervisors: Dr. Holger Schlarb (DESY), Prof. Görschwin Fey (TUHH, Department of Computer Engineering)

Most advanced physics meets highly dependable high performance embedded computing precisely at the infrastructure of large scale research facilities like the European XFEL or PETRAIII/IV. The physics of the experiment defines requirements and functionality for high-speed high-performance real time processing. Reliable operation requires a dependable distributed infrastructure composed of thousands of custom computing nodes for data taking, processing, storage and transfer. Thus, the key question arises: How to facilitate self-diagnosis and self-healing under tight real-time and high-performance processing demands? Guided by the physicists and engineers at DESY and supervised at TUHH, the prospective PhD student will devise new concepts for self-aware distributed computing to identify and heal faults autonomously at run time. The scientific challenges are in the automated localization of potential sources for failures may these be due to hardware defects, radiation or even software bugs and their mitigation. Deep understanding of the computing infrastructure as well as the experiment physics are mandatory to identify feasible solutions. Model-based approaches joined with online formal reasoning will be the method of choice for advanced self-awareness. Empirical studies will implement and verify developed concepts on the most recent devices deployed at DESY. This exactly matches future needs in wider application areas relying on a myriad of devices combined into virtually autonomous distributed computing infrastructures.

A multi-purpose framework for efficient parallelized execution of charged particle tracking

Supervisors:  Dr. Krisztian Peters (DESY), Prof. Thomas Ludwig (UHH, Fachbereich Informatik)

Charged particle track reconstruction in an environment with extremely high particle multiplicities and measurement densities is a significant computing challenge for future high-energy physics experiments, such as the planned upgrade of the ATLAS experiment at CERN for the High-Luminosity Large Hadron Collider (HL-LHC). In order to make best use of modern developments in processor technologies (such as wide registers, GPUs, and other accelerators), the ability to run efficiently in a parallelized, multi-threaded framework is key. The application of such paradigms to track reconstruction is currently limited by the highly serial procedures used to mitigate the intrinsic combinatorial challenges of the application, and the necessity to run on distributed, heterogeneous computing resources. This project will address these issues by studying methods to apply intra- algorithm parallelism to common track reconstruction tasks, and developing a framework to ensure that such tasks can be efficiently assigned to resources with appropriate architectures.

New Computational Methods for Serial Crystallography at X-ray Free Electron Lasers

Supervisors: Prof. Henry Chapman (CFEL, DESY, UHH Fachbereich Physik, Dr. Anton Barty (CFEL, DESY), Prof. Matthias Rarey (ZBH, UHH, Fachbereich Informatik)

Serial crystallography experiments run X-ray cameras continuously at their maximum frame rate resulting in the collection of large volumes of data. Current experiments generate over 20,000,000 frames which need to be analyzed with raw data sets of over 50TB in size per experiment. Experiments in the next few years at the European XFEL in Hamburg will be able to collect over 3500 diffraction patterns per second, or over 12 million measurements per hour. Efficient and automated data analysis is essential to obtaining results. The aim of this project is to develop novel computational approaches for the most pressing data processing tasks along the serial crystallography pipeline. The focus will be on two problems. The first major challenge is the development of efficient methods for data reduction. Poor quality frames have to be filtered out efficiently, in the ideal case online during data generation. The second challenge lies in how the collected frames in total can be optimally exploited for atomistic model generation. Novel strategies taking all data into account and making use of modern numerical optimization schemes will be combined with interactive visualization components to generate tailored model building solutions for serial crystallography and the generation of time-resolved macromolecular movies.