{
  "abstract": "The Multi AperTure mid-Infrared SpectroScopic Experiment (MATISSE) at the Very Large Telescope Interferometer (VLTI) delivers rich mid-infrared interferometric datasets that are reduced through a robust, multi-stage ESO pipeline. In its native form, however, the reduction relies on command-line calls to esorex, hand-edited set-of-files (SOF) and configuration files, and manual iterations that are difficult to automate, reproduce, or integrate into modern Python workflows. We present MATISSE-py, a modular Python interface that wraps the official ESO esorex recipes behind a single, user-friendly matisse command. It automates discovery, reduction, calibration, and inspection of the data, and ships as a tested, open-source package built on industry-standard practices. Beyond convenience, MATISSE-py introduces science-grade improvements developed over seven years of MATISSE operations: visibility- and photometry-factor (V-factor and P-factor) corrections derived from the GRAVITY fringe tracker to mitigate residual visibility losses, per-baseline data filtering, beam-commuting-device (BCD) \"magic-number\" corrections, and an absolute spectrophotometric flux calibration using state-of-the-art reference spectra. On faint targets observed under degraded conditions, the V-factor correction recovers up to a few percent of lost visibility and reduces the statistical uncertainties by nearly 20%, while yielding calibrator diameters that are consistent across BCD modes. MATISSE-py thus consolidates the consortium's expertise into a maintainable package and shortens the reduction-to-science loop for the community.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.31283",
  "author_total": 7,
  "authors": [
    {
      "name": "A. Soulain"
    },
    {
      "name": "A. Matter"
    },
    {
      "name": "P. Berio"
    },
    {
      "name": "F. Millour"
    },
    {
      "name": "B. Lopez"
    },
    {
      "name": "R. Petrov"
    },
    {
      "name": "S. Lagarde"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4nsc8a6s7n794.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.31283",
  "fulltext_url": null,
  "id": "4nsc8a6s7n794",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.31283",
  "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",
      "astro-ph.GA"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "arxiv",
      "astro-ph.SR"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.31283",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4nsc8a6s7n794.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:52Z",
      "native_id": "2609.31283",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.31283"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.GA",
      "subfield": "galaxies"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.SR",
      "subfield": "stellar"
    }
  ],
  "title": "MATISSE-py: a high-level Python interface for efficient, automated VLTI/MATISSE data reduction",
  "title_text": "MATISSE-py: a high-level Python interface for efficient, automated VLTI/MATISSE data reduction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:52Z",
  "url": "https://research.draconitic.com/paper/4nsc8a6s7n794",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1117/12.3102898",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}