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.