BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Lindsey Gray (Fermi National Accelerator Laboratory) DTSTART:20201014T170000Z DTEND:20201014T180000Z DTSTAMP:20260423T020954Z UID:MPML/15 DESCRIPTION:Title: Gr aph Neural Networks for Pattern Recognition in Particle Physics\nby Li ndsey Gray (Fermi National Accelerator Laboratory) as part of Mathematics\ , Physics and Machine Learning (IST\, Lisbon)\n\n\nAbstract\nModern partic le physics detectors generate copious amounts of data packed with meaning that provides the means for high-quality measurements in demanding experim ental environments. To achieve these measurements there is a trend towards finer granularity in these detectors and that implies the data read out h as less intrinsic structure. Accurate pattern recognition is required to d efine the signatures of particles within those detectors and simultaneousl y extract physical parameters for the particles. Typically\, algorithms to achieve these goals are written using well known unsupervised algorithms\ , but recent advances in machine learning on graph structures\, "Graph Neu ral Networks" (GNNs)\, provide powerful new methodologies for designing pa ttern recognition algorithms. In particular\, methodologies for predicting the link structure between pieces of data from detectors are well suited to the particle physics pattern recognition task. Furthermore\, there are interesting avenues for enforcing known symmetries of the data into the ou tput of such networks and there is ongoing research in this direction. Thi s talk will discuss the challenges of pattern recognition\, the advent of GNNs and the connections to particle physics\, and the paths of research a head for fully utilizing this powerful new tool.\n LOCATION:https://researchseminars.org/talk/MPML/15/ END:VEVENT END:VCALENDAR