{
  "abstract": "Gaussian process (GP) based predictive periodic error correction, introduced by Klenske et al., learns a telescope mount's periodic tracking error online and applies the correction before the error is observed, removing the reaction lag inherent in purely reactive autoguiders. The published model uses a single periodic kernel and therefore represents the error of one gear stage: any repeating shape at one period, harmonics included, but only one period. Mounts with more than one reduction stage -- strain-wave drives with a separate input stage, belt-and-worm combinations, transfer gears -- produce errors at periods that are mutually incommensurate, which beat against each other and never repeat on a common cycle. We extend the model to a sum over an arbitrary number of quasi-periodic kernels, one per gear stage, each discovered from the residual spectrum, gated on the strength of its own spectral line, and refused if it is commensurate with a period already modelled. We show that a strictly periodic second kernel is unusable in practice because it demands the second period to within 0.2%, and that a squared-exponential envelope of about twenty periods removes that sensitivity at a cost of 0.02 px. On synthetic two-stage errors at realistic periods and amplitudes the extension reduces the residual by 1-40% against the single-kernel model, while leaving single-stage cases numerically unchanged. On a recorded, de-noised session with no second, incommensurate gear frequency, it still improves on the single-kernel model by 6% and on a reactive hysteresis controller by 45%, by absorbing amplitude modulation of the primary that the single kernel cannot. The implementation is dependency-free, multiplatform code in the C programming language and is released as part of the INDIGO project.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.38236",
  "author_total": 1,
  "authors": [
    {
      "name": "Rumen G. Bogdanovski"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7sfctdx4rn009.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.38236",
  "fulltext_url": null,
  "id": "7sfctdx4rn009",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.38236",
  "language": null,
  "licence": "CC-BY-SA-4.0",
  "licence_label": "CC BY-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "openalex",
      "3105|Astronomy and Astrophysical Research"
    ]
  ],
  "openalex": "W7215051191",
  "pdf_url": "https://arxiv.org/pdf/2609.38236",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7sfctdx4rn009.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:48Z",
      "native_id": "2609.38236",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.38236"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Multi Kernel Gaussian Process Guiding: Predictive Correction for Mounts with Several Gear Stages",
  "title_text": "Multi Kernel Gaussian Process Guiding: Predictive Correction for Mounts with Several Gear Stages",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:48Z",
  "url": "https://research.draconitic.com/paper/7sfctdx4rn009",
  "venue": "arXiv",
  "versions": []
}