MATISSE-py：用于高效、自动化VLTI/MATISSE数据归约的高级Python接口
MATISSE-py: a high-level Python interface for efficient, automated VLTI/MATISSE data reduction
浏览论文内容
中文总结 AI 辅助
MATISSE-py是封装ESO esorex食谱的Python接口，自动化VLTI/MATISSE数据归约，引入V因子等校正，在微弱目标上恢复可见度并减少近20%不确定性。
中文摘要 AI 辅助
甚大望远镜干涉仪（VLTI）上的多孔径中红外光谱实验（MATISSE）提供丰富的中红外干涉数据集，这些数据通过稳健的多阶段ESO流水线进行归约。然而，在其原生形式下，归约依赖于对esorex的命令行调用、手工编辑的文件集（SOF）和配置文件，以及难以自动化、复现或集成到现代Python工作流中的手动迭代。我们提出MATISSE-py，一个模块化的Python接口，将官方ESO esorex食谱封装在单一、用户友好的matisse命令之后。它自动化数据发现、归约、校准和检查，并作为一个经过测试的开源包发布，基于行业标准实践构建。除了便利性，MATISSE-py引入了基于七年MATISSE运行经验开发的科学级改进：从GRAVITY条纹跟踪器导出的可见度和光度因子（V因子和P因子）校正以缓解残余可见度损失，每基线数据过滤，光束交换装置（BCD）“幻数”校正，以及使用最先进参考光谱的绝对分光光度流量校准。在退化条件下观测的微弱目标上，V因子校正恢复了高达百分之几的损失可见度，并将统计不确定性减少了近20%，同时产生的校准器直径在BCD模式间保持一致。因此，MATISSE-py将联盟的专业知识整合到一个可维护的包中，缩短了社区从归约到科学的循环。
英文摘要
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.
发表机构
- Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange（蔚蓝海岸大学，蔚蓝海岸天文台，法国国家科学研究中心，拉格朗日实验室）
机构由 AI 辅助整理，请以论文原文为准。
