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Detector Systems
Research areas mapped to detector subsystems
Machine Learning
Deep learning architectures for particle physics reconstruction. Graph neural networks, transformers, and scalable inference pipelines for HL-LHC.
Particle Physics
ATLAS experiment at CERN. Standard Model measurements, BSM searches, and detector operations with the Large Hadron Collider.
Graph Neural Networks
GNN-based pipelines for charged particle tracking (GNN4ITk), jet classification, and event reconstruction at production scale.
Physics Language Models
Foundation model paradigm for HEP. Exploring how large language model architectures can learn the language of particle physics data.
Reconstructed Events
Recent publications and key contributions
MLCharged Particle Tracking with Machine Learning on FPGAsJINST, 2024GNNGraph Neural Network Track Reconstruction for the ATLAS ITk DetectorATL-ITK-PROC, 2023PHYPerformance of the ATLAS Track Reconstruction AlgorithmsEur. Phys. J. C, 2022TRKExa.TrkX: Graph Neural Networks for Particle Track ReconstructionCHEP 2021MLTrack Seeding and Labelling with Embedded-space Graph Neural NetworksEPJ Web Conf., 2021
Luminosity Report
Integrated luminosity of research output
1,276+
Citations
Total integrated luminosity
42
Publications
Reconstructed event count
8
h-index
Signal significance
3
Runtime Environments
Dublin / Berkeley / Copenhagen