{
  "abstract": "Tuning the electron beam of a MeV-scale $γγ$ collider, first in the design and later in a digital twin of the machine, requires the beam parameters at the interaction point (IP) for many injector states. Start-to-end particle tracking is too slow for this and needs the full particle distribution, which an operating machine cannot measure. We address this problem for the 200-MeV transport line of the collider with two fast predictors. For design, second-order transfer maps from the transport entrance to the IP are applied to every particle. For a digital twin, an extremely-randomized-trees (ExtraTrees) model predicts the IP parameters from the means and rms widths of the beam at the transport entrance. On 89 held-out start-to-end simulations, the map predictor reproduces Elegant tracking for the mean energy, momentum spread, bunch length, transverse rms sizes and normalized emittances with coefficients of determination ($R^2$) of 0.97--1.00 and is ten times faster. The ExtraTrees model reaches an average $R^2$ of 0.88, compared with 0.58 when the injector settings are used as input, and performs similarly with eleven inputs that can be measured or reconstructed. Under a 2% input perturbation, the average $R^2$ of the six parameters other than the mean energy decreases only from 0.917 to 0.895, whereas the mean energy requires an accurate momentum calibration. The two predictors provide the IP-beam evaluation for the collider design and the prediction layer of a future digital twin.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.08036",
  "author_total": 6,
  "authors": [
    {
      "name": "Jiawei Zhang"
    },
    {
      "name": "Yongsheng Huang"
    },
    {
      "name": "Yuancun Nie"
    },
    {
      "name": "Qiang Gu"
    },
    {
      "name": "Yuan Chen"
    },
    {
      "name": "Yuanjie Bi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3pqh7xb3m52bq.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.08036",
  "fulltext_url": null,
  "id": "3pqh7xb3m52bq",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.08036",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.acc-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.08036",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3pqh7xb3m52bq.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T18:21:30Z",
      "native_id": "2611.08036",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.08036"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.acc-ph",
      "subfield": "applied-instrumentation"
    }
  ],
  "title": "Fast prediction of electron-beam parameters using transfer maps and machine learning in a digital model of a MeV $γγ$ collider",
  "title_text": "Fast prediction of electron-beam parameters using transfer maps and machine learning in a digital model of a MeV γγ collider",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:41Z",
  "url": "https://research.draconitic.com/paper/3pqh7xb3m52bq",
  "venue": "arXiv",
  "versions": []
}