Introduction
This multidisciplinary research project aims to take advantage of modern Machine Learning (ML) architectures for data processing tasks relevant to High-Energy Physics (HEP) experiments. One example is the task of subatomic particle trajectory reconstruction, a.k.a., tracking, for accelerator collision events. Tracking is crucial to the understanding of subatomic particle behaviour.
The approach is to perform exploration for ML-assisted data processing solution designs. These solutions are expected to deliver higher capability, ultimately replacing the currently fielded classical algorithms. The ambition is to deliver solutions that are flexible enough to be detector agnostic.
One of the adopted strategies is the systematic simplification of the problem at hand through Reduced-Order Models (ROMs) and complexity-reduced simulations. In doing so, we have developed the REDuced VIrtual Detector (REDVID) simulation framework.
Dedicated pages for our developed tools:
- REDVID
- TrueTrack (under development)
Contact
The research is being carried out as a multidisciplinary collaboration between Radboud University (RU), the Dutch National Institute for Subatomic Physics (Nikhef) and the University of Twente (UTwente).
We usually offer Master-level Student Projects in relevant topics. In case of enquiries, you can reach us at: aW5mb0BWaXJ0dWFsRGV0ZWN0b3IuY29t