Flexible Spectro Interferometric modelling of OIFITS data with PMOIRED

TL;DR

PMOIRED is a versatile Python library for model fitting of OIFITS data in u,v space, enabling complex scene construction, parameter constraints, and advanced uncertainty evaluation methods.

Contribution

It introduces a flexible, modular approach to model fitting of interferometric data with capabilities for complex component combination and advanced optimization techniques.

Findings

Supports complex scene modeling with simple geometric components

Allows for global minimum search using grid search

Enables detailed uncertainty analysis through data resampling

Abstract

Despite image reconstruction becoming more widespread when interpreting OIFITS Data, model fitting in u,v space often remains the best way to interpret data, either because of the sparsity of the data, or because a quantitative measurement needs to be done. PMOIRED, is a flexible Python library to visualize, manipulate and model OIFITS data using simple geometric models. The strength of PMOIRED resides in its capability to combine linearly various simple components to create complex scenes, while linking, constraining, and adding priors to fitted parameters. The code also enables grid search to find global minima, as well as data resampling to better evaluate uncertainties. In addition to analytical functions, arbitrary radial profiles, azimuthal variations or sparse wavelet modelling of spectra are implemented.