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Decoding Black Box Models to Find New Physics at the LHC

Creative Commons 'BY' version 4.0 license

This work presents techniques for addressing the black box problem for deep learning in high-energy physics applications at the LHC. In an initial group of studies, a method is presented for translating a black box classifier using high-dimensional detector data into a minimal set of simple physics motivated features with equivalent classification performance. The strategy is first applied to a benchmark discrimination task for jets from a boosted W boson decay. The algorithm is then used on two active areas of standard model research at the LHC: electron identification and prompt muon isolation. Finally, the technique is applied to a beyond the standard model study of semi-visible jets produced via a theoretical dark quark hadronization process.

A second technique is presented, providing a method for the explicit embedding of physics parameters alongside measured features in a deep learning model. This architecture yields a parameterized classifier that can smoothly interpolate between physical features. The result is a simpler and more powerful machine learning classifier that can seamlessly incorporate expert physics knowledge into its learned solution. The parameterized network is applied to a benchmark classification task for tt ̄ decays.

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