Below is a copy of a speed talk that I completed at TRIUMF in summer 2024 as a part of the TRIUMF Science Week. If I get a video of the presentation I will be sure to upload it!
Deep Learning at TRIUMF
Summary Particle reconstruction in the ATLAS detector is the practice of associating calorimeter and tracker signals particle and determining which particles caused them, with how much energy, and through what process. One crucial step in reconstruction is cell segmentation where we determine which calorimeter signals belong to which tracks from the inner tracker. Current methods to perform segmentation rely on algorithms that grow with quadratic complexity, posing a challenge for segmentation after the High-Luminosity LHC (HL-LHC) upgrade, which promises a significant increase in the number of collisions per event and, therefore, the complexity of each event.
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