The phase retrieval problem is a non-linear, ill-posed inverse problem. It is also an important step in X-ray imaging, a precursor to the tomographic reconstruction stage. Experiments involving micro and nanometer-sized objects usually have weak absorption and contrast. This is usually the case in most experiments taking place at high-energy big Synchrotron centres like DESY. Hence, retrieving...
Large language models (LLMs) have demonstrated formidable capabilities in analyzing and synthesizing natural language text. This technology presents an opportunity to extract and connect knowledge from the expansive corpus of textual and visual artifacts accumulated over decades of research at the German Electron Synchrotron (DESY).
This talk will detail current efforts within the DESY MCS4...
The CMS Generative Machine Learning Group, at the DESY round table, will showcase three distinct projects, each utilizing point cloud-based generative models to advance particle physics research. The first project, "Attention to Mean Fields for Particle Cloud Generation," features an attention-based generative model that adeptly processes complex collider data represented as point clouds,...
The evaluation of plaque assays is a crucial step when studying viruses, as they are used to determine viral reproduction. This is done via a dilution series of the virus, which is applied to gel plates containing a confluent layer of host cells. Infected cells are killed by the virus and the number of empty patches ("plaques") will therefore indicate the viral load of the original...
In a wide range of high-energy particle physics analyses, machine learning methods have proven as powerful tools to enhance analysis sensitivity.
In the past years, various machine learning applications were also integrated in central CMS workflows, leading to great improvements in reconstruction and object identification efficiencies.
However, the continuation of successful deployments...
We introduce a method for efficiently generating jets in the
field of High Energy Physics. Our model is designed to generate ten different
types of jets, expanding the versatility of jet generation techniques. Beyond
the kinematic features of the jet constituents, our model also excels in
generating informative features that provide insight into the types of jet
constituents, such as...
Research on the application of small-angle x-ray scattering (SAXS) method, using x-ray free-electron laser (XFEL) images, utilizes normalizing flows for the inversion of experimental X-ray scattering images. One of the main challenges lies in the inversion of such experimental scattering images, which contain various artifacts such as parasitic scattering, slit scattering, beamstop, and...
Large-scale scientific facilities like European XFEL are complex and include multiple subsystems that work in coordination to generate high-quality scientific output. Any fault within such a subsystem can result into downtime for the entire facility, with a significant impact in the scientific output. It is therefore, fundamental to detect problems or unexpected behaviour in components well in...
A brief overview of a selection of HIFIS services that can be useful for Deep Learning applications, like the GPU Compute Service.
HIFIS provides and brokers digital services for everyone in Helmholtz and collaboration partners.
Large industrial facilities are complex systems that not only require regular maintenance and upgrades but are often inaccessible to humans due to various safety hazards. Therefore, a virtual reality (VR) system that can quickly replicate real-world remote environments to provide users with a high level of spatial and situational awareness is crucial for facility maintenance planning and robot...
I will present applications of Deep Learning-based protein structure prediction tools, such as AlphaFold, for interpreting experimental data in macromolecular crystallography and electron cryomicroscopy. I will also show examples of my own Deep Learning tools trained to complement the use of predicted models for macromolecular structure determination.
(1) Chojnowski NAR 2023 51(15)...
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 first hardware demonstration of a real-time event filtering algorithm using machine learning for the Level-1 Trigger of the CMS experiment.
We present a case study of machine learning (ML) integrated into beamline control to drive autonomous x-ray reflectivity (XRR) measurements [1], which can be seen as a prototypical implementation to serve as an example for other in-situ and in-operando synchrotron and neutron experiments. ML strategies have significantly improved in the analysis of reflectometry data in recent years [2],...
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics.
Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a significant computing constraint.
This contribution marks a significant breakthrough in this task by directly...
We present a model-agnostic search for new physics in the dijet final state using five different novel machine-learning techniques. Other than the requirement of a narrow dijet resonance, minimal additional assumptions are placed on the signal hypothesis. Signal regions are obtained utilizing multivariate machine learning methods to select jets with anomalous substructure. A collection of...
