{
  "abstract": "The enormous recorded data at high-energy physics (HEP) colliders make the accurate identification of physics objects a bottleneck in disentangling event topologies, where machine learning has become a standard tool for jet tagging. In this work, we examine whether Vision-Language Models (VLMs) can provide a common interface for structured jet analysis from a single physics-informed image. Each JetClass jet becomes a 224x224 RGB image with four panels encoding p_T flow of all constituents; charged-hadron, neutral-hadron, and electromagnetic composition; p_T-weighted impact-parameter significances with displaced-track multiplicity; and signed-track p_T densities with local p_T^k-weighted jet-charge asymmetry. Four open VLMs are adapted with low-rank adaptation (LoRA) for class classification across QCD, Higgs, W/Z, and top jets, six-field attribute prediction, and cross-panel consistency with replaced-panel localization, evaluated on 24000 balanced JetClass test jets. Ablations identify the impact-parameter lifetime signature of heavy-flavor tagging as dominant, with jet charge separating the nearly mass-degenerate hadronic W and Z. Zero-shot recall stays near chance (3.33%-10.57%), once adapted, Task 1 Macro recall grows monotonically with training size, and Gemma4-E4B reaches 75.05% (74.87% F_1), 93.31% Task 2 field-mean, and 99.88%/99.75% binary/localization recall. Reallocating budget toward W/Z jets raises Zqq recall by up to 9.83 points at near-constant Macro recall. Transfer to broader-topology JetClass-II and real-data Aspen Open Jets (probing the simulation-to-data gap) is retained: with 3000 target jets, Task 1 recall exceeds 70% on JetClass-II and every Task 2/3 metric exceeds 92%. Thus rasterizing energy flow, species, displacement, and jet charge, with parameter-efficient adaptation, supports jet analysis through one instruction-conditioned interface for future collider data.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.24334",
  "author_total": 3,
  "authors": [
    {
      "name": "Lu Zhang"
    },
    {
      "name": "Rachik Soualah"
    },
    {
      "name": "Abbes Amira"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7q1j4ktj4nrsk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.24334",
  "fulltext_url": null,
  "id": "7q1j4ktj4nrsk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.24334",
  "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",
      "hep-ph"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "openalex",
      "3106|Particle physics theoretical and experimental studies"
    ]
  ],
  "openalex": "W7214057661",
  "pdf_url": "https://arxiv.org/pdf/2609.24334",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7q1j4ktj4nrsk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:19Z",
      "native_id": "2609.24334",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.24334"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:hep-ph",
      "subfield": "high-energy"
    }
  ],
  "title": "Multitask Jet Analysis with Vision-Language Models: A Physics-Informed Four-Panel Representation",
  "title_text": "Multitask Jet Analysis with Vision-Language Models: A Physics-Informed Four-Panel Representation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:19Z",
  "url": "https://research.draconitic.com/paper/7q1j4ktj4nrsk",
  "venue": "arXiv",
  "versions": []
}