Please visit Jefferson Lab Event Policies and Guidance before planning your next event:
May 8 – 12, 2023
Norfolk Waterside Marriott
US/Eastern timezone

Exploring Interpretability of Deep Neural Networks in Top Tagging

May 9, 2023, 4:45 PM
Hampton Roads VII (Norfolk Waterside Marriott)

Hampton Roads VII

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Oral Track 9 - Artificial Intelligence and Machine Learning Track 9 - Artificial Intelligence and Machine Learning


Roy, Avik (University of Illinois at Urbana-Champaign)


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

Primary authors

Khot, Ayush (University of Illinois at Urbana-Champaign) Neubauer, Mark (University of Illinois) Roy, Avik (University of Illinois at Urbana-Champaign)

Presentation materials