MATISSE-py: a high-level Python interface for efficient, automated VLTI/MATISSE data reduction
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.
Versions
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BibTeX
@misc{soulain2026matissepy,
title = {{MATISSE-py: a high-level Python interface for efficient, automated VLTI/MATISSE data reduction}},
author = {A. Soulain and A. Matter and P. Berio and F. Millour and B. Lopez and R. Petrov and S. Lagarde},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.31283},
eprint = {2609.31283},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.31283},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - MATISSE-py: a high-level Python interface for efficient, automated VLTI/MATISSE data reduction
AU - A. Soulain
AU - A. Matter
AU - P. Berio
AU - F. Millour
AU - B. Lopez
AU - R. Petrov
AU - S. Lagarde
PY - 2026
DA - 2026/09/25
PB - arXiv
DO - 10.48550/arxiv.2609.31283
UR - https://arxiv.org/abs/2609.31283
AB - 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.
ER -