IRIS-HEP Fellow: Dominika Maria Krawiec

Fellowship dates: Feb – May, 2022

Home Institution: University of Warwick, UK

Project: Conformal Mappings for Particle Track Reconstruction

Particle detectors like the HL-LHC record point clouds of hits; to reconstruct the trajectories of particles, it is necessary to determine which hits belong to these individual trajectories. This, along with the sparse nature of the detector data, makes graph neural networks (GNNs) a promising tool for particle tracking, given their strong performance on instance segmentation tasks. The GNN pipeline (Interaction Network) built by Dr. Thais' team extracts track parameters in addition to grouping hits belonging to the trajectories of individual particles. A conformal space mapping can be used for track fitting and consequently parameter extraction, and has the advantage of being computationally efficient compared to a helical fit. The goal of this project is to further improve the conformal space fit and its stability, and optimise certain aspects of the Interaction Network's architecture for this purpose.

More information: My project proposal

  • Savannah Thais (Princeton University)

Presentations and Publications

Current Status
March 2022 - Undergraduate student of Physics at the University of Warwick, UK.

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