Abstract
Autoencoders as tools behind anomaly searches at the LHC have the
structural problem that they only work in one direction, extracting
jets with higher complexity but not the other way around. To
address this, we derive classifiers from the latent space of
(variational) autoencoders, specifically in Gaussian mixture and
Dirichlet latent spaces. In particular, the Dirichlet setup solves the
problem and improves both the performance and the interpretability
of the networks.
Funder
Bundesministerium für Bildung und Forschung
Deutsche Forschungsgemeinschaft
Max Planck Society
Subject
General Physics and Astronomy
Cited by
42 articles.
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