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Description
Finding the tracks that particles make through detectors is a critical component of identifying new physics and phenomena, but is very a
challenging combinatorial problem. Traditionally, track finding codes assume that tracks must be helical, which simplifies the task but also restricts power to discover new physics which might produce non-helical tracks, effectively ignoring some potentially striking signatures. However, recent advances in ML-based tracking allow for new inroads into previously inaccessible territory, such as efficient reconstruction of tracks that do not follow helical trajectories. I will present a demonstration of training a network to reconstruct a particular type of non-helical tracks, quirks, as well as a generalization to a wider class of non-helical tracks, enabling a search for overlooked anomalous tracks and fast track parameter fitting. I’ll end by talking briefly about my experience in science communication.
https://cern.zoom.us/j/61585612649?pwd=FoLJQF7A3uVaG1ss9JoguyXrFeYHYY.1
passcode 60292128
