2nd GSI/FAIR AI Workshop

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...