pyFIT3D and pyPipe3D -- The new version of the Integral Field Spectroscopy data analysis pipeline

TL;DR

The paper introduces a new Python-based version of the FIT3D and Pipe3D data analysis pipelines for optical and integral field spectroscopy, enhancing efficiency, accuracy, and compatibility while maintaining the original algorithms.

Contribution

It provides a fully transcribed Python implementation of the pipelines, improving performance and fixing bugs, with validation on recent IFS survey data and simulations.

Findings

Enhanced accuracy in parameter recovery from simulations

Improved computational efficiency and bug fixes

Validated with recent IFS survey data

Abstract

We present a new version of the FIT3D and Pipe3D codes, two packages to derive properties of the stellar populations and the ionized emission lines from optical spectroscopy and integral field spectroscopy data respectively. The new codes have been fully transcribed to Python from the original Perl and C versions, modifying the algorithms when needed to make use of the unique capabilities of this language with the main goals of (1) respecting as much as possible the original philosophy of the algorithms, (2) maintaining a full compatibility with the original version in terms of the format of the required input and produced output files, and (3) improving the efficiency and accuracy of the algorithms, and solving known (and newly discovered) bugs. The complete package is freely distributed, with an available repository online. pyFIT3D and pyPipe3D are fully tested with data of the most recent IFS data surveys and compilations (e.g. CALIFA, MaNGA, SAMI and AMUSING++), and confronted with simulations. We describe here the code, its new implementation, its accuracy in recovering the parameters based on simulations, and a showcase of its implementation on a particular dataset.