{
  "abstract": "Maximizing luminosity at the interaction point (IP) requires the collision location $s_{IP}$ to coincide with the longitudinal position of the minimum beta function, $s^$. Accurate optics measurements and control of $s^$ are therefore essential for luminosity optimization. At the Relativistic Heavy Ion Collider (RHIC), average horizontal beta-beat measurements between operating IPs are approximately $20%$, with significant variation in measured $s^*$. Precise control of the beam waist position $s^$ is particularly challenging for modern high-energy colliders with short bunch lengths and large crossing angles. We present an online Bayesian optimization (BO) application using the GPTune framework to optimize sPHENIX luminosity through $s^$ control at RHIC. GPTune was first validated at the RHIC Electron Beam Ion Source (EBIS), where it achieved up to a $70%$ increase in beam intensity over the baseline, although experienced operators could reach similar performance with longer manual tuning. The framework was subsequently deployed during sPHENIX operations. Using an intensity-normalized Zero-Degree Calorimeter (ZDC) signal as the optimization objective, due to the unavailability of the live sPHENIX MVTX signal, GPTune identified local luminosity maxima, recovered from intentionally degraded $s^$ configurations, and revealed residual horizontal and vertical waist offsets in the interaction region. These results demonstrate the robustness and efficiency of Bayesian optimization for real-time collider tuning under noisy, time-varying conditions. The $s^$ control methodology provides a promising tool for precision luminosity optimization and is particularly relevant to next-generation short-bunch colliders such as the Electron-Ion Collider (EIC).",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.24978",
  "author_total": 10,
  "authors": [
    {
      "name": "Xiaofeng Gu"
    },
    {
      "name": "Benjamin Coe"
    },
    {
      "name": "Guillaume Robert-Demolaize"
    },
    {
      "name": "Takeshi Kanesue"
    },
    {
      "name": "Masahiro Okamura"
    },
    {
      "name": "William Fung"
    },
    {
      "name": "Yue Hao"
    },
    {
      "name": "Ji Qiang"
    },
    {
      "name": "Xiaoye S. Li"
    },
    {
      "name": "Yang Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/121xy30xah28q.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.24978",
  "fulltext_url": null,
  "id": "121xy30xah28q",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.24978",
  "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",
      "2202|Particle accelerators and beam dynamics"
    ]
  ],
  "openalex": "W7214051516",
  "pdf_url": "https://arxiv.org/pdf/2609.24978",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/121xy30xah28q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:20Z",
      "native_id": "2609.24978",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.24978"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.acc-ph",
      "subfield": "applied-instrumentation"
    }
  ],
  "title": "Bayesian Optimization of The Relativistic Heavy Ion Collider Luminosity via $s^*$ Control",
  "title_text": "Bayesian Optimization of The Relativistic Heavy Ion Collider Luminosity via s^* Control",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:20Z",
  "url": "https://research.draconitic.com/paper/121xy30xah28q",
  "venue": "arXiv",
  "versions": []
}