Speaker
Description
IRC safety has long guided the design of robust jet substructure observables, and this principle has increasingly been built into machine-learned taggers as well. For attention-based architectures such as the Particle Transformer (ParT), however, it is non-trivial to enforce IRC safety without discarding the pairwise, energy-dependent features that make these models powerful. In this work, we show that IRC safety can be built directly into the attention mechanism by removing energy information from per-particle tokens and pairwise features, and instead injecting it as an additive $\log E_i + \log E_j$ bias on the pre-softmax attention logits. We prove that the resulting attention output for "ParT-IRC" is invariant under soft emissions and collinear splittings.
Building on this, we further construct a Lipschitz-constrained variant, "L-ParT-IRC", which combines our IRC-safe attention with modified self-attention and spectral normalization to bound the network's sensitivity to non-perturbative corrections. We benchmark ParT-IRC against the standard ParT on quark/gluon tagging, finding comparable in-distribution performance together with improved robustness under a Pythia-to-Herwig out-of-distribution generalization test, and we discuss the tradeoffs introduced by the Lipschitz constraint. We also demonstrate that L-ParT-IRC outperforms the Lipschitz Energy Flow Network (L-EFN) while remaining similarly insensitive to non-perturbative effects.