Speaker
Description
From particle identification to the discovery of the Higgs boson, neural network algorithms have become an increasingly important tool for data analysis at the Large Hadron Collider. We present a novel approach to event and particle identification, called end-to-end deep learning, that combines deep learning image classification algorithms with low-level detector representation. Using two physics examples as references: quark and gluon discrimination and top quark jet tagging, we demonstrate the performance of the end-to-end approach using high-fidelity detector simulations from the CMS Open Data. Additionally, we explore the relevance of the information collected from various sub-detectors and describe how end-to-end techniques can be useful for full-event interpretation.
