EuCAIFCon 2026

Session

🔀 Real-Time Data Processing

26 Aug 2026, 14:00

3.404

Conveners

🔀 Real-Time Data Processing

• Wouter Verkerke

🔀 Real-Time Data Processing

• Markus Klute (Karlsruhe Institute of Technology (KIT))

160. Real-time data processing model at LHCb and ML/AI: evolution from an extension to a core functionality

Miroslav Saur (Lanzhou University)

26/08/2026, 14:00

Real-time Data Processing

The LHCb experiment has deployed machine learning and artificial intelligence models in its real-time data processing from the start of Run 1 data taking, and by Run 3 such models are an integral part of the trigger.

This talk will describe the usage of machine learning and AI models and algorithms within the LHCb real-time analysis data processing paradigm as part of both reconstruction...

94. Metric Learning for Graph Neural Network-Based Track Reconstruction at the ATLAS Event Filter

Giulia Fazzino

26/08/2026, 14:10

Real-time Data Processing

The High-Luminosity LHC (HL-LHC) will deliver proton-proton collisions at unprecedented pile-up conditions, making fast and efficient track reconstruction a key computational challenge. Graph Neural Networks (GNNs) offer a promising approach to this problem.

A critical first step in GNN-based charged-particle tracking is graph construction, which can be carried out with the Metric Learning...

27. CNN-Based Trigger for QGP-Sensitive Event Selection in High-Rate Heavy-Ion Experiments

Olga Soloveva (GSI Helmholtz Zentrum fuer Schwerionenforschung GmbH)

26/08/2026, 14:20

Real-time Data Processing

High-rate heavy-ion experiments require fast and robust event-selection strategies capable of identifying rare physics signatures in real time under severe throughput and storage constraints. We present a convolutional-neural-network-based trigger concept for the selection of events associated with quark–gluon plasma (QGP) formation. The method represents each collision event as a compact...

183. Deep Learning for detector alignment at LHCb

Manjunath Omana Kuttan (Albert-Ludwigs-Universität Freiburg)

26/08/2026, 14:40

Real-time Data Processing

The LHCb experiment at CERN's Large Hadron Collider (LHC) relies on precise alignment of its tracking detectors comprising the Vertex Locator (VELO), Upstream Tracker (UT), and Scintillating Fibre (SciFi) Tracker to achieve the momentum, mass, and vertex resolutions demanded by the LHCb physics programme. During data-taking, detector alignment at LHCb is performed on CPUs using an iterative χ²...

112. Symbolic Regression as a Model Compression Tool for Neural Networks used in Track Seeding

Urs Moritz Fischer

26/08/2026, 14:50

Real-time Data Processing

Machine learning techniques have demonstrated great potential for efficiently addressing the increasing combinatorial complexity of track finding at high-luminosity collider experiments. To achieve high computational efficiency, neural network models often require compression, a necessity that becomes increasingly important for the large models used in GNN-based tracking pipelines. This is...

123. Machine Learning Models for Anomaly Detection at HADES

Óscar Marcos Pérez Cytron (Ruhr-Universität Bochum, GSI Helmholtzzentrum für Schwerionenforschung)

26/08/2026, 15:00

Real-time Data Processing

Maintaining data quality in real-time systems remains a fundamental requirement in nuclear and particle physics experiments. At the HADES collaboration, the Quality Assessment (QA) system generates plots that visualize the real-time performance of detector subsystems. Currently this requires that operators manually inspect these visualizations to identify potential anomalies.

This...

108. EPIGRAPHY: Advancing Edge AI for Particle Physics through Community Benchmarks and Data Challenges

Sebastian Dittmeier (PI)

27/08/2026, 14:00

Real-time Data Processing

Machine learning is playing an increasingly important role in particle physics, from offline data analysis to real-time event selection at the Large Hadron Collider. The COST Action EPIGRAPHY (Edge deeP learnIng foR pArticle PHYsics) brings together researchers across Europe to advance efficient deep learning methods for resource-constrained, low-latency computing environments. This...

97. Economical Jet Taggers -- Equivariant, Slim and Quantized

Antoine Petitjean (ITP, Heidelberg University)

27/08/2026, 14:10

Real-time Data Processing

Modern machine learning is transforming jet tagging at the LHC, but the leading transformer architectures are large, not particularly fast, and training-intensive. We present a slim version of the L-GATr tagger, reduce the number of parameters of jet-tagging transformers, and quantize them. We compare different quantization methods for standard and Lorentz-equivariant transformers and estimate...

138. Enabling portable and optimized Machine Learning Inference through code generation

Sanjiban Sengupta (CERN, University of Manchester)

27/08/2026, 14:20

Real-time Data Processing

SOFIE, or the System for Optimized Fast Inference code Emit, is a tool developed by the ML4EP team at CERN that translates trained machine learning models into self-contained, low-latency C++ code. The generated code is portable, hardware-agnostic, and highly optimized while depending only on BLAS libraries.

Code generated by SOFIE achieves portability across different hardware...

172. From slow to real-time control in accelerator-based research infrastructures

Erik Bründermann (Karlsruhe Institute of Technology (KIT))

27/08/2026, 14:30

Real-time Data Processing

The KIT accelerator team has contributed significantly to the state-of-the-art of accelerator control especially in controlling electron beams near real-time in storage rings and synchrotrons. Adapted to the latency of components like magnets, slow control optimizes beam transfer and injection into a storage ring [1]. Fast control by online reinforcement learning with AI-on-hardware...

178. Real-time Gravitational Wave Parameter Estimation

James Alvey (University of Cambridge)

27/08/2026, 14:50

Real-time Data Processing

In this talk I will present a specialised GPU-native nested sampling kernel targeting rapid parameter estimation for gravitational wave inference problems. Building upon a Slice-within-Gibbs (SwiG) structure for rapid mixing, we investigated how far we can push baseline stochastic sampling techniques on modern GPU hardware. I will show that for typical long-duration binary neutron star signals...

46. Pre-localization of Massive Black Hole Binaries in the Millihertz Band

Dr Xue-Ting Zhang (Max Planck Institute for Gravitational Physics (Albert Einstein Institute))

27/08/2026, 15:00

Real-time Data Processing

The space-borne gravitational-wave (GW) detectors will open a new mass and redshift regime, allowing us to observe massive black hole binaries (MBHBs) throughout the Universe. A subset of these systems is expected to produce electromagnetic (EM) counterparts, offering a unique opportunity to follow the continuous evolution of massive black holes through joint GW and EM observations. Realizing...

157. Is Data Compression an Anomaly Detector? Applications to High-Energy Physics Datasets

Prof. Caterina Doglioni

27/08/2026, 15:10

Patterns & Anomalies

The ATLAS experiment at the CERN Large Hadron Collider (LHC) records and processes vast amounts of data from proton-proton collisions. With the High-Luminosity LHC (HL-LHC), the expected increase in data volume by more than an order of magnitude will place unprecedented demands on storage, data throughput, and analysis. In this contribution, we will start with the comparison of two novel...