Interpretability Study on Deep Learning for Jet Physics at the Large Hadron Collider
This work addresses interpretability for physicists using deep learning in high-energy physics, but it appears incremental as it builds on existing methods without clear breakthroughs.
The study tackled the interpretability of deep neural networks for jet tagging at the Large Hadron Collider, aiming to understand network behavior and improve performance, but no concrete results or numbers were reported.
Using deep neural networks for identifying physics objects at the Large Hadron Collider (LHC) has become a powerful alternative approach in recent years. After successful training of deep neural networks, examining the trained networks not only helps us understand the behaviour of neural networks, but also helps improve the performance of deep learning models through proper interpretation. We take jet tagging problem at the LHC as an example, using recursive neural networks as a starting point, aim at a thorough understanding of the behaviour of the physics-oriented DNNs and the information encoded in the embedding space. We make a comparative study on a series of different jet tagging tasks dominated by different underlying physics. Interesting observations on the latent space are obtained.