Climate research often requires substantial technical expertise. This involves managing data standards, various file formats, software engineering, and high-performance computing. Translating scientific questions into code that can answer them demands significant effort. The question is, why? Data analysis platforms like Freva (Kadow et al. 2021, e.g., gems.dkrz.de ) aim to enhance user...
More and more AI tools written by non-physicists such as ChatGBT, CoPilot and Cursor are becoming available. These tools allow researchers to perform their work more efficiently. Yet, many are not using them (properly), though they can be very useful for literature research, writing texts, coding, etc.
To ensure that scientists at DESY have a basic understanding of what is possible and...
As part of an effort to integrate generative AI into operation, a project focusing on establishing an AI Copilot to help guide experiments at the European XFEL has been initiated. Initially, a survey was designed and implemented to investigate which issues beamline staff face during operation, and that they think could benefit from an AI co-pilot. The results of the survey indicated that...
Ensuring 24/7 operation of the laser systems for DESY's free-electron lasers requires detailed analysis of operator logbooks and RT-tickets, a task currently limited by labor-intensive manual methods that lack statistical granularity. As part of a DASHH collaboration with the University of Hamburg, we investigate the application of Large Language Models (LLMs) to automate this analysis. We...
The Helmholtz Model Zoo (HMZ) is a cloud-based platform providing remote access to deep learning models within the Helmholtz Association via web interface and REST API. Scientists from all 18 Helmholtz centers can contribute models through a streamlined GitLab submission process, with HMZ automatically generating the interface, testing, and deploying the model.
HMZ runs on GPU nodes at DESY...
Generative AI is increasingly playing a role in supporting the operation of light sources, e.g. through support assistants or tools that cover certain recurring tasks. While LLM-based coding assistants are somewhat established, and RAG-based knowledge assistants are reaching production-readiness levels in well-defined domains, agentic generative AI, to which operational tasks are handed off,...
Historic data of settings and operational conditions within the Karabo SCADA system at the European XFEL is stored in an InfluxDB time-series database and facilitates trend analysis, anomaly detection and performance optimization. Frequently, the data serves as a foundation for data-driven decision-making, allowing researchers and engineers to analyze past events, validate system behavior, and...
Large-scale facilities like European XFEL consist of numerous subsystems, where precise real-time tuning is essential for maintaining stable and optimal performance. Automation techniques can be leveraged to reduce operators' time investment and potentially increase the exploitation of allotted beamtime, both in quantity and quality. In this talk, we will discuss our ongoing efforts to...
Newly available soft X-ray two-color FEL pulse mode at European XFEL opens a new way to the structural and plasma studies of the nano-scaled object. The first pulse, designated as the pump, characterizes the initial state of the object, whereas the second probe pulse captures the system’s evolution following its interaction with the pump. The pulses are separated by an extremely short time...
Designing complex optical coatings, such as dispersion-managed mirrors for ultrafast lasers, is a high-dimensional inverse problem traditionally relying on iterative, expert-guided methods. We present a machine learning framework that automates this process by employing an autoencoder with a differentiable, physics-based decoder. This decoder, which analytically solves Maxwell's equations via...
Precise control of the temporal profile of laser pulses is essential for optimizing beam emittance in photoinjectors. Achieving this through spectral shaping is particularly challenging due to nonlinear effects, temporal drifts, and experimental nonidealities. To address this, a two-step feedback mechanism based on differentiable physics modeling is developed for adaptive experimental control....
Presenting a generative model for detector simulation, that utilities normalising flows for macro-information, and iid diffusion models for fast details.
This is the third iteration of the CaloClouds series, which surpasses previous limits in inference speed. New, upgraded, capacities allow simulation of photons throughout the barrel of the detector. By integrating the model into the...
Simulating showers of particles in highly-granular calorimeters is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models can enable them to augment traditional simulations and alleviate a major computing constraint. Recent developments have shown how diffusion based generative shower simulation...
Accurate and efficient detector simulation is essential for modern collider experiments. To reduce the high computational cost, various fast machine learning surrogate models have been proposed. Traditional surrogate models for calorimeter shower modeling train separate networks for each particle species, limiting scalability and reuse. We introduce PanShower, a unified generative model that...
We present a second-generation transformer for simulating calorimeter showers as point clouds in high-granularity detectors. As in OmniJet-α, showers are modeled as variable-length sequences, capturing realistic shower development without conditioning on the number of hits. New in this version, we remove the VQ-VAE entirely and directly tokenize the detector geometry. This geometry-aware...
Accurate particle shower simulation remains a critical computational bottleneck in high-energy physics. Traditional Monte Carlo methods, such as Geant4, are computationally prohibitive, while existing machine learning surrogates are often tied to specific detector geometries and require full retraining for each design change or alternative detector configuration.
We present a transfer...
The practice of collider physics typically involves the marginalization of multi-dimensional collider data to uni-dimensional observables relevant for some physics task. In any cases, such as classification or anomaly detection, the observable can be arbitrarily complicated, such as the output of a neural network. However, for precision measurements, the observable must correspond to something...
Efficient capture of final state radiation (FSR) is crucial for a high mass resolution in BSM QCD searches. Using kinematic observables of dijet processes, we want to develop a GNN-based supervised tagger that improves the FSR resolution and is able to distinguish BSM processes from a dijet background.
