{
  "abstract": "The tree-level open superstring has an integer mass-squared spectrum on linear Regge trajectories. To what extent is this fixed by consistency conditions: locality, analyticity, crossing, unitarity together with the requirement that $2\\to2$ scattering minimize the entanglement it generates? Using the local two-channel crossing symmetric dispersion relation with one-parameter ambiguity ($λ$-CSDR) we set up a bootstrap treating both the mass-squared spectrum and the partial-wave coefficients as unknowns which lets the machine learning algorithm to learn the mass-squared spectrum and partial-wave coefficients simultaneously, going beyond earlier work in which the spectrum was fixed. Using the local three-channel crossing symmetric dispersion relation with one-parameter ambiguity we obtain, within the class of amplitudes with soft Regge behavior and infinite higher-spin tower, a bound on the leading Wilson coefficients that we compare with existing bounds and trace to spin-0 dominance. We locate the Virasoro-Shapiro amplitude in the allowed region of the Wilson coefficients space. For amplitudes with two-channel crossing symmetry we find that a Non-dual amplitude exhibits a Polchinski-Strassler type transition from Regge to power-law behavior.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.27667",
  "author_total": 1,
  "authors": [
    {
      "name": "Debapriyo Chowdhury"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f6j8jwerg1w1q.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.27667",
  "fulltext_url": null,
  "id": "f6j8jwerg1w1q",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.27667",
  "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-th"
    ],
    [
      "arxiv",
      "math-ph"
    ],
    [
      "arxiv",
      "math.MP"
    ],
    [
      "openalex",
      "3106|Black Holes and Theoretical Physics"
    ]
  ],
  "openalex": "W7223095971",
  "pdf_url": "https://arxiv.org/pdf/2610.27667",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f6j8jwerg1w1q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:04Z",
      "native_id": "2610.27667",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.27667"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math-ph",
      "subfield": "mathematical-physics"
    },
    {
      "field": "physics",
      "origin": "arxiv:hep-th",
      "subfield": "high-energy"
    }
  ],
  "title": "Learning the open string spectrum with neural networks",
  "title_text": "Learning the open string spectrum with neural networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:08Z",
  "url": "https://research.draconitic.com/paper/f6j8jwerg1w1q",
  "venue": "arXiv",
  "versions": []
}