Speaker
Description
Deep learning (DL) approaches to high-energy jet tagging achieve state-of-the-art performance over classical methods, but lack human interpretability. We propose a framework for constructing post-hoc interpretable symbolic surrogates of state-of-the-art DL jet taggers trained on particle cloud input representations. A Deep Sets variational autoencoder first learns fixed-size tabular representations of the input data. These latent variables then serve as inputs to a black-box symbolic distillation step that models a DL model's decision boundary. Finally, a separate latent-space symbolic distillation step then interprets the latent space itself using known jet observables. We evaluate the framework on three benchmark jet tagging tasks found in the JetClass dataset across three state-of-the-art DL architectures. We find that surrogates tend to trade classification performance for interpretability in comparison with DL models. Additionally, we identify several functional similarities between surrogates that separate jets via N-subjettiness variables and terms representing energy distributions. The results of our framework suggest that it can be used as a general purpose approach to correlating particle cloud jet tagger decision boundaries to physically meaningful jet substructure properties.