2nd GSI/FAIR AI Workshop

This talk gives a brief overview of the IT infrastructure supporting AI activities at GSI/FAIR, with a particular focus on emerging inference and LLM services. It will also present the GSI strategy for the professional use of AI-related tools, including considerations around data protection, trade secrets and information security, and outline how infrastructure and institutional guidance can...

We present a versatile production-grade LLM inference and document intelligence server designed for GSI/FAIR computing infrastructure.

The system employs a modern containerized architecture built on vLLM for GPU-accelerated inference, OpenWebUI for user interaction, and Milvus vector database for Retrieval-Augmented Generation (RAG) capabilities.
The deployment utilizes several specialized...

PowerSched presents a proof of concept for deep reinforcement learning-based, price-aware capacity control in high-performance computing clusters, dynamically adjusting powered resources in response to fluctuating electricity prices and workload demand. Evaluations using realistic price and workload traces demonstrate that measurable cost savings can be achieved while maintaining complete job...

The configuration parameters of the SLURM guide, the working of the scheduler. The efficiency of the cluster where SLURM is run can be optimised with the help of SLURM simulator adapted and tailored to the work load of the HPC cluster.

We investigate fast methods for evaluating particle hypotheses by combining signals from multiple detectors during the early readout stage of the data-acquisition (DAQ) chain. As a first use case, we consider straight particle trajectories reconstructed from straw-tube drift-time measurements and time-of-flight (TOF) wall responses, initially neglecting energy loss. A compact multilayer...

Artificial intelligence methods developed for one scientific domain often struggle to generalize to other fields that produce irregular and high dimensional detector data, creating a need for domain agnostic reconstruction approaches.
BRAID is a research consortium developing machine learning models particularly graph neural network and attention-based architectures, that can work across this...

We present HEIDI, a deep learning-based conditional diffusion model for the ultra-fast generation of event-by-event heavy-ion collision output. Trained on UrQMD simulations, HEIDI learns to generate point clouds of final-state particles and accurately reproduces the multiplicity and momentum distributions of 26 different hadron species produced in UrQMD. Compared to standard UrQMD cascade...

We present an unsupervised machine learning approach to automate quality 
monitoring in the HADES experiment, combining a Variational Autoencoder 
with HDBSCAN clustering (VAE-HDBSCAN) to detect anomalies in detector 
performance plots without needing to hand-label data. Our method 
outperforms non-latent clustering and approaches the performance of 
supervised CNN baselines, and is being...

The CBM experiment is a complex detector system composed of numerous subdetectors operating simultaneously and generating thousands of monitoring plots to ensure stable and reliable performance. Given the massive data volumes expected during data taking, continuous verification of data quality and the prompt identification of technical faults are essential. This project proposes the adoption...

A unified framework based on machine learning for the identification of hadrons by combining responses from different sub-detectors is developed for the CBM experiment. In the first iteration, gradient-boosted decision trees (xGBOOST) are used as base models. This contribution focuses on the implementation of the models and the recent results achieved through their application. Furthermore, a...

Abstract: AI-assisted operation of the Super-FRS will be tested during the commissioning activities in 2027. The approach treats the language model as a reasoning engine rather than a knowledge base: it retrieves from the facility's own data, computes with tested tools, and cites what it used. The data itself already exists — S-FRSRoot delivers physics-grade output from the DAQ streams, and...

An AI chat assistant based on open-source large language models (LLMs) to provide context-aware guidance and answer questions related to the GSI/FAIR accelerators and their operation. The model runs locally, using prompt engineering and retrieval-augmented generation (RAG) to gain access to shift data from operations and domain knowledge spread across the various GSI wikis.

A fully on-premise hybrid RAG system that makes electronic logbook entries searchable in natural language, surfacing expert knowledge that keyword search misses.

SIS-SYS develops beam-based feedback, automated control and expert-diagnostic systems for SIS18 and SIS100, combining classical DSP and control methods with numerical models and, increasingly, ML/AI-based approaches.

A central focus is how beam-data-driven algorithms are qualified and maintained over the long term through physics-informed digital twins that provide a reproducible...

Since 10 years we are maintaining a Java application "DeviceAutomator" ready for use in the control room. Step by step this application is enhanced by AI-powered functionality - classical optimizers until now. We want to report on the latest developments, application scenarios and how we use DeviceAutomator in our daily work.

This contribution presents the ideas for an AI-assisted beamline optimisation framework for the FRS/Super-FRS Ion Catcher. The project will explore the use of machine-learning algorithms together with ion-optical simulations and offline measurements to automate tuning of the low-energy beamline and reduce reliance on manual optimisation.

The GSI/FAIR facility’s complexity demands a high degree of automation for future operations. As part of this effort, we developed Geoff, a framework for real-time optimization of beam parameters and experimental setups designed for fast deployment and control room integration. Geoff significantly improves operational performance, having reduced SIS18 synchrotron injection losses from 45% to...

At the GSI Helmholtz Centre, civil engineering, research, plant engineering and operations work in separate data and tool environments. "Twin+" does not replace these systems but places an integration layer on top of them. The existing building and asset data remain in their respective domain systems and are made accessible across disciplines through an access layer based on the Model Context...

The Helmholtz Information & Data Science Academy (HIDA) is dedicated to the development of information and data science skills within the Helmholtz Association. HIDA offers a comprehensive range of training opportunities and promotes the exchange of knowledge between scientists through funding, mobility programs, and networking events. HIDA also serves as the umbrella organization for seven...

AI agents are getting good at doing pieces of research, such as searching literature, writing code, working with data, and making plots. But a useful research agent needs more: it needs to learn how a community actually works.

In this talk, I’ll introduce Ori (Open Research Intern), our AI research assistant at AIP, and the Skill Commons, a database where researchers can share practical...