Graph Neural Network-based Top Quark Reconstruction
The Large Hadron Collider collects a large amount of data through collisions between photons, which produce and decay into various particles such as top quarks. Top quarks are the elementary particle with the most mass and could help in the search for new physics. Due to their mass, they decay quickly and are not detected directly. Instead, top quarks can be reconstructed using other observable information with machine learning. Graph neural networks (GNN) are useful for top quark reconstruction because of its ability to represent particles and their relationships as graphs. I will continue to develop GNNs, understand its performance, and explore the flexibility it provides in the representation of data.
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- Major: Computer Science & Statistics
- Mentor: Haichen Wang, Physics