Point cloud applications to collider physics - Livestream

At the LHC Experiment, each proton collision creates thousands of particles. Extracting information from a high dimensional space such as the space of collisions requires algorithms that take advantage of particle symmetries while being capable of handling high dimensional inputs. Point cloud processing methods, often applied to robotics and self-driving cars, are able to handle such datasets and exploit the geometrical relationship between points. In this talk, I will present the application of this concept to different problems in collider physics, comparing the results with other well established algorithms.

Speaker: Vinicius Mikuni, NERSC

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