This is SParTA: A modular, efficient and scalable transformer architecture for jet-tagging

We present the Sparse Particle-interaction Transformer Architecture (SParTA), a modular, scalable, and efficient transformer for jet tagging at high-energy colliders. Exploiting the steeply falling transverse-momentum (p T) spectrum of jet constituents, SParTA factorizes constituent processing into two topological regimes. Ordered by descending p T, only the N hardest constituents (the hard core) participate in quadratic O(N 2) differential interaction attention driven by pairwise physical and learned relational features. The remaining soft constituents are integrated via linear O(lN) cross-attention to the hard core, preserving soft-radiation and color-flow information while avoiding the O(l 2) memory bottleneck of full self-attention over all l constituents. Pairwise interaction features are computed dynamically in-network from constituent four-momenta and conditioned on global jet-level substructure observables. On the quark-gluon tagging benchmark, a 300K-parameter SParTA model achieves an AUC of 92.02% and accuracy of 85.06%, matching the 2.14M-parameter Particle Transformer (ParT) at one-seventh the parameter count and outperforming IAFormer by 21% in background rejection at 30% signal efficiency. On the boosted top-tagging benchmark, SParTA outperforms ParT and ParticleNet with 511K parameters, while we analyze the architectural trade-offs relative to fully dense multi-layer interaction networks.