Web: http://arxiv.org/abs/2104.14659

Jan. 24, 2022, 2:10 a.m. | Michael Andrews, Bjorn Burkle, Yi-fan Chen, Davide DiCroce, Sergei Gleyzer, Ulrich Heintz, Meenakshi Narain, Manfred Paulini, Nikolas Pervan, Yusef Sh

cs.LG updates on arXiv.org arxiv.org

We describe a novel application of the end-to-end deep learning technique to
the task of discriminating top quark-initiated jets from those originating from
the hadronization of a light quark or a gluon. The end-to-end deep learning
technique combines deep learning algorithms and low-level detector
representation of the high-energy collision event. In this study, we use
low-level detector information from the simulated CMS Open Data samples to
construct the top jet classifiers. To optimize classifier performance we
progressively add low-level information …

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