A more dedicated study on the information flow in DNNs will help us understand their behaviour and the deep connection between DNN models and the corresponding tasks. Taking into account our well-established physics analysis framework (observable-based), we present a novel way to interpret DNNs results for HEP, which not only gives a clear physics picture but also inspires interfaces with the theoretical foundation. Information captured by DNNs can thus be used as a fine-tailored general-purpose encoder. As a concrete example, we showcase using encoded information to help with physics searches at the LHC.
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