{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.27106",
  "author_total": 4,
  "authors": [
    {
      "name": "Rajneil Baruah"
    },
    {
      "name": "Subhadeep Mondal"
    },
    {
      "name": "Sunando Kumar Patra"
    },
    {
      "name": "Satyajit Roy"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d2cgd1wdrt4yy.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.27106",
  "fulltext_url": null,
  "id": "d2cgd1wdrt4yy",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.27106",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "hep-ex"
    ],
    [
      "arxiv",
      "hep-ph"
    ],
    [
      "openalex",
      "3106|Particle physics theoretical and experimental studies"
    ]
  ],
  "openalex": "W7223107924",
  "pdf_url": "https://arxiv.org/pdf/2610.27106",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d2cgd1wdrt4yy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:04Z",
      "native_id": "2610.27106",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.27106"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "This is SParTA: A modular, efficient and scalable transformer architecture for jet-tagging",
  "title_text": "This is SParTA: A modular, efficient and scalable transformer architecture for jet-tagging",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:04Z",
  "url": "https://research.draconitic.com/paper/d2cgd1wdrt4yy",
  "venue": "arXiv",
  "versions": []
}