Introduction
The High-Energy Physics (HEP) research area investigates computational methods for particle-physics simulation, data generation, and track reconstruction, with particular emphasis on machine-learning-assisted approaches.
A recurring theme is the controlled management of complexity: from reduced-order simulations and synthetic data, through scalable ML-based reconstruction, to efficient deployment on specialised hardware. The aim is to support systematic experimentation while bridging the gap between simplified research environments and increasingly realistic detector and event conditions.
Research Directions
Reduced-order simulation and synthetic data generation
Development of controllable simulation and data-generation methods for particle-physics research, with an emphasis on Reduced-Order Models (ROM) that make detector and event complexity explicit. REDuced VIrtual Detector (REDVID) supports controlled studies across different particle-propagation, detector, and data-complexity settings, while REDVID-Gen explores generative methods for producing event and hit data from compact user specifications.
Machine-learning-assisted track reconstruction
Design and evaluation of machine-learning methods for reconstructing particle trajectories from detector hits. This direction includes detector-aware data representations, relational and Transformer-based models, scalable event processing, and reconstruction-oriented evaluation under realistic event complexity.
Low-latency hardware deployment
Exploration of hardware-aware implementations for ML-assisted particle tracking, including deployment on FPGAs and, in future work, ASICs. The work considers latency, resource use, model partitioning, and the broader feasibility of low-latency or online reconstruction workflows.
Related projects
- REDVID
- REDVID-Gen (future work)
- TrueTrack (under development)
- TrackCore-F
- TrackCore-A (future work)
Selected Artefacts
Resources
For background information, foundational knowledge, and detailed context related to this research, the following resources may be helpful:
- Wikipedia: Particle Physics – General introduction to particle physics and its core concepts.
- ATLAS Inner Detector – A brief overview of the inner detector installed within the ATLAS detector.
- Phase 2 Upgrade of the ATLAS Inner Tracker – A detailed overview of the ATLAS experiment Inner Tracking upgrades intended for High-Luminosity LHC operation.
- A Living Review of Machine Learning for Particle Physics - A categorised collection of references, listing modern machine learning applications, intended for particle physics.
Student Projects
Student work in this area includes particle-physics simulation, synthetic-data generation, track reconstruction, ML-assisted tracking, and FPGA deployment. See the Student Projects page for current opportunities and completed research.