Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction
This work addresses the problem of building trustworthy machine learning tools for physicists by improving interpretability in jet classification, though it is incremental as it builds on existing methods with specific constraints.
The paper tackled the challenge of interpretability in deep learning for jet classification in collider physics by designing graph neural networks with physics-motivated inductive biases like IRC safety and equivariance, showing these networks are more stable and their learned representations directly correspond to established QCD observables.
Deep learning has achieved remarkable success in jet classification tasks, yet a key challenge remains: understanding what these models learn and how their features relate to known QCD observables. Improving interpretability is essential for building robust and trustworthy machine learning tools in collider physics. To address this challenge, we investigate graph neural networks for quark-gluon discrimination, systematically incorporating physics-motivated inductive biases. In particular, we design message-passing architectures that enforce infrared and collinear (IRC) safety, as well as E(2) and O(2) equivariance in the rapidity-azimuth plane. Using simulated jet datasets, we compare these networks against unconstrained baselines in terms of classification performance, robustness to soft emissions, and latent representation structures. Our analysis shows that physics-aware networks are more stable across training instances and distribute their latent variance across multiple interpretable directions. By regressing Energy Flow Polynomials onto the leading principal components, we establish a direct correspondence between learned representations and established IRC-safe jet observables. These results demonstrate that embedding symmetry and safety constraints not only improves robustness but also grounds network representations in known QCD structures, providing a principled approach toward interpretable deep learning in collider physics.