In search for increasingly rarer signatures of “new physics”, today’s particle detectors are streaming raw data with increasingly higher resolution, volume, and complexity, posing growing challenges to data processing and physics data analysis. At the same time, recent advances in machine learning and artificial intelligence provide an unprecedented opportunity to dig deeper and extract more information out of large data sets. The talk will present the case of a particular detector technology commonly used in neutrino physics, characterized by exorbitant amounts of image-like raw data and unprecedented demands for real-time data processing, where "accelerated machine learning" can be used to extract rare signatures, enabling fundamental physics discoveries.
Agenda: indico.cern.ch...
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