Speaker
Description
We explore interpretability of deep neural network (DNN) models designed for identifying jets coming from top quark decay in the high energy proton-proton collisions at the Large Hadron Collider (LHC). Using state-of-the-art methods of explainable AI (XAI), we identify which features play the most important roles in identifying the top jets, how and why feature importance varies across different XAI metrics, and how latent space representations encode information as well as correlate with physical quantities. We additionally illustrate the activity of hidden layers as Neural Activation Pattern (NAP) diagrams to understand how DNNs relay information across the layers and how this understanding can help us to make such models significantly simpler by allowing effective model reoptimization and hyperparameter tuning.
| Consider for long presentation | Yes |
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