{
  "abstract": "We investigate whether a multiscale tensor-network architecture provides a useful inductive bias for reconstruction-based anomaly detection in collider jets. Jets are produced by a branching cascade, so their internal structure is naturally organised across angular and momentum scales. This motivates an autoencoder that compresses information hierarchically and can reorganise short-range correlations before coarse-graining. Guided by this picture, we formulate a Multiscale Entanglement Renormalization Ansatz (MERA)-inspired autoencoder acting directly on ordered jet constituents. To our knowledge, a MERA-inspired autoencoder has not previously been proposed, and this architecture has not been explored in collider anomaly detection. We compare this architecture to a dense autoencoder, the corresponding tree-tensor-network limit, and standard classical baselines within a background-only reconstruction framework. The paper is organised around two main questions: whether locality-aware hierarchical compression is genuinely supported by the data, and whether the disentangling layers of MERA contribute beyond a simpler tree hierarchy. To address these questions, we combine benchmark comparisons with a training-free local-compressibility diagnostic and a direct identity-disentangler ablation. In the nominal benchmark, the MERA-inspired model attains a higher mean AUC than the dense autoencoder at all three latent dimensions while using roughly one-third of its trainable parameters. The locality-preserving multiscale structure is well matched to jet data, and, at the strongest compression bottleneck, the trained disentanglers move substantially away from the identity and are accompanied by a positive mean performance shift. Overall, the study supports locality-aware hierarchical compression as a useful and parameter-efficient inductive bias for reconstruction-based jet anomaly detection in this benchmark setting.",
  "abstract_shown": true,
  "affiliations": [
    "Sinop University",
    "Karlsruhe Institute of Technology (KIT)",
    "Karlsruhe Institute of Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Emre Gurkanli",
      "orcid": "0000-0002-9543-1086"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Michael Spannowsky",
      "orcid": "0000-0002-8362-0576"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0xw955pqk4dc5.bib",
  "cited_by": 0,
  "doi": "10.1140/epjc/s10052-026-16489-z",
  "fulltext_url": "https://doi.org/10.1140/epjc/s10052-026-16489-z",
  "id": "0xw955pqk4dc5",
  "issns": [
    "1434-6044",
    "1434-6052"
  ],
  "landing_url": "https://link.springer.com/article/10.1140/epjc/s10052-026-16489-z",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QB460-466"
    ],
    [
      "lcc",
      "QC770-798"
    ],
    [
      "openalex",
      "1702|Anomaly Detection Techniques and Applications"
    ]
  ],
  "openalex": "W7152716994",
  "pdf_url": "https://link.springer.com/content/pdf/10.1140/epjc/s10052-026-16489-z.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/0xw955pqk4dc5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:04Z",
      "native_id": "10.1140/epjc/s10052-026-16489-z",
      "source": "crossref",
      "url": "https://link.springer.com/article/10.1140/epjc/s10052-026-16489-z"
    },
    {
      "first_seen": "2026-10-30T13:46:25Z",
      "native_id": "ab547534374f4daa99d1340bded33eac",
      "source": "doaj",
      "url": "https://doi.org/10.1140/epjc/s10052-026-16489-z"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "lcc:QB460-466",
      "subfield": "general"
    },
    {
      "field": "physics",
      "origin": "issn:1434-6044",
      "subfield": "high-energy"
    },
    {
      "field": "physics",
      "origin": "lcc:QC770-798",
      "subfield": "nuclear"
    }
  ],
  "title": "Quantum-inspired tensor network autoencoders for anomaly detection: a MERA-based approach",
  "title_text": "Quantum-inspired tensor network autoencoders for anomaly detection: a MERA-based approach",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:46:25Z",
  "url": "https://research.draconitic.com/paper/0xw955pqk4dc5",
  "venue": "The European Physical Journal C",
  "versions": []
}