Machine learning is emerging as vital tool in many sciences. In quantum physics, notable examples are neural networks for the efficient representation of quantum many-body states and reinforcement learning of preparation and read-out routines. In this talk, I will present our results on machine learning of quantum phase transitions using classification techniques. This approach works very well...
Generative machine learning models allow fast event generation, yet are so far primarily constrained to fixed data and detector geometries.
We introduce a Deep Sets-based permutation equivariant generative adversarial network (GAN) for generating point clouds with variable cardinality - a flexible data structure optimal for collider events such as jets. The generator utilizes an interpretable...
The simulation of calorimeters using standard Monte Carlo based methods is expected to create a major computational bottleneck at the next generation of collider experiments, motivating the development of alternative approaches. Surrogate simulators based on deep generative models could potentially provide significant computational speedups- however the highly granular nature of many future...
Simulation in High Energy Physics places a heavy burden on the available computing resources and is expected to become a major bottleneck for the upcoming high luminosity phase of the LHC and future Higgs factories, motivating a concerted effort to develop computationally efficient solutions. Generative machine learning methods hold promise to alleviate the computational strain produced by...
We present techniques to perform model-agnostic searches for new physics at the LHC, both at the trigger and analysis level. We highlight some of the recently uncovered practical challenges and discuss proposed solutions.
Particle colliders such as the LHC produce data at an unprecedented rate and volume. To overcome bandwidth constraints, event filtering systems are employed, with the first stage usually implemented in hardware using FPGAs. We present the studies on real-time event filtering using machine learning for the Level-1 Trigger of the CMS experiment.
In high energy physics, detailed and time-consuming simulations are used for particle interactions with detectors. For the upcoming High-Luminosity phase of the Large Hadron Collider (HL-LHC), the computational costs of conventional simulation tools exceeds the projected computational resources. Generative machine learning is expected to provide a fast and accurate alternative. The CMS...
The solution of the time-dependent Schrödinger equation completely characterizes the quantum dynamics of molecular systems. Because this equation is analytically solvable only for toy problems, for real-life simulations, a variety of numerical approximations have been developed. The most accurate methodologies employ the variational approach based on the linear expansion of the solutions in a...
The House of Computing & Data Science is a central unit of U Hamburg for the digital transformation of research; the KIEZ of Computing and Data Science is a Hamburg-wide network to connect computation-oriented people in research and development. After motivating the need of such units and networks for all research disciplines, the current level of their implementation is described.
Machine learning techniques have left the drawing board and are currently industrialized in the aerospace sector.
First an overview of the wide range of these applications at Airbus is given, followed by a more detailed description of the research on how an LSTM and AutoEncoder-based dimensionality reduction approach is used to calculate the loads on an aircraft during landing. Finally, an...
Proteins are important biomolecules of life. While many proteins can function independently, most proteins interact with other proteins to control and mediate their biological activities. Hence, studying protein-protein interactions (PPIs) is important to better understand biological functions. There are several biological factors that can influence the presence or absence of a PPI. In our...
Single particle imaging (SPI) with X-ray free-electron lasers (XFELs) sources can empower visualization of structural heterogeneity in biological entities such as viruses and complex proteins. We have developed a CNN-based generative network approach for mapping and reconstruction of the 3D structure of a target object at any point of its structural variation landscape. We discuss...
Serial femtosecond crystallography produces large data volumes however, only a small percentage of the data is useful for downstream analysis. In this work, we handle serial crystallography data with deep learning for various challenges including classification, Bragg peak detection, domain adaptation, explainability.
Hard X-ray nanotomography is a commonly used tool in many research areas such as material science, biology and medicine. However, the quality of the reconstructed tomogram is often obscured by noise, especially for in situ experiments when a high time resolution is required. Machine learning (ML) techniques offer a powerful alternative to conventional filtering methods. Here, we present a ML...
I am developing a data analysis workflow involving machine learning technologies like recurrent and convolutional neural networks as well as classification systems, for the rapid and reliable identification and quantification of viral proteins in mass spectrometry data from human saliva samples. The goal is to implement this workflow in high-throughput quantitative testing for respiratory...
With the increasing success of deep learning techniques in many applications, researchers also use it for segmentation of biodegradable implants in high resolution synchrotron radiation microtomograms (SRμCT). Deep learning models, however, require lots of annotated training examples in order to generalize and perform well on new data. Manual annotation of high resolution SRμCT is a very...
The abundance of data currently stored for heterogeneous subsystems represents an excellent opportunity for data analysis. Data-driven approaches, relying solely on data can significantly benefit from having massive streams of data available. So far, frequently the modeling activities focused on model-based analyses which relied on human expertise. In this talk, we provide examples of...
Virtual diagnostics involves using fast computational tools that can predict the output of a diagnostic when it is unavailable. One work in this direction proposes the use of ML learning methods at the European XFEL's SASE1 beamline to predict X-ray properties such as beam pointing using undulator electron properties. Such an approach is promising for providing accurate knowledge on X-ray...
High-fidelity particle-in-cell simulations are an essential tool for the modeling and optimization of laser-plasma accelerators. However, the high computational cost associated with them severely limits the possibility of broad parameter exploration. Here, we show that a multitask Bayesian optimization algorithm can be used to mitigate the need for high-fidelity simulations by incorporating...
In this work we present a model-based approach for fault detection for the superconducting cavities at the European XFEL. With the help of a classification algorithm, a special class faults, the quenches, can be distinguished from others. Application results are presented.
Laser-plasma accelerators are promising candidates for driving compact undulator radiation sources. Future applications would greatly benefit from optimization by data-driven modelling of the acceleration process, but intrinsic noise and large parameter spaces poses a problem for conventional modelling methods. At LUX beamline we use an ensemble of neural networks and bootstrap aggregation to...
Reinforcement learning (RL) has enabled the development of intelligent controllers for complex tasks that previously required human intuition to solve. In the context of particle accelerators, there exist many such tasks and solving them with conventional methods takes away from scarce experiment time and limits the operability of accelerators. We demonstrate how to successfully apply RL to...
