SPAN: A cross-platform Python GUI software for optical and near-infrared spectral analysis

TL;DR

SPAN addresses the fragmentation of spectral-analysis tools by delivering a cross-platform, Python-based GUI that unifies 1D spectrum extraction, manipulation, and both line-strength and full spectral fitting within a single environment. It centers on unresolved galaxy spectra and leverages pPXF for efficient, accurate kinematic and population analyses, while avoiding computationally heavy Bayesian methods. The authors validate SPAN against established pipelines (e.g., E-MILES LIS, Cappellari’s pPXF workflow, GIST) and demonstrate a practical, science-ready workflow on the NGC 1097 MUSE datacube, achieving agreement in kinematics, ages, metallicities, and abundance indicators. The work highlights SPAN’s potential to accelerate spectral workflows for large datasets, improve accessibility, and facilitate reproducible analyses, with planned expansions to support more instruments and additional fitting algorithms.

Abstract

The increasing availability of high-quality optical and near-infrared spectroscopic data, as well as advances in modelling techniques, have greatly expanded the scientific potential of spectroscopic studies. However, the software tools needed to fully exploit this potential often remain fragmented across multiple specialised packages, requiring scripting skills and manual integration to handle complex workflows. In this paper we present SPAN (SPectral ANalysis), a cross-platform, Python-based Graphical User Interface (GUI) software that unifies the essential tools for modern spectral analysis within a single, user-friendly environment. While SPAN can be used with a variety of spectroscopic targets, its primary focus is the analysis of unresolved galaxy spectra. SPAN allows users to extract 1D spectra from FITS images and datacubes, perform spectral processing (e.g. Doppler correction, continuum modelling, denoising), and carry out detailed analyses, including line-strength measurements, stellar and gas kinematics, and stellar population studies, using both built-in routines and the widely adopted pPXF algorithm for full spectral fitting. It runs natively on Windows, Linux, macOS, and Android, and is fully task-driven, requiring no prior coding experience. We validate SPAN by comparing its output with existing pipelines and literature studies. By offering a flexible, accessible, and well integrated environment, SPAN simplifies and accelerates the spectral analysis workflow, while maintaining scientific accuracy.

Figures (16)

• Figure 1: The main panel of SPAN.
• Figure 2: Workflow diagram of SPAN from data extraction to spectral analysis and visualisation. In black we marked the required steps, while in red we marked the optional steps. Black rectangular frames delineate the three sequential blocks that compose a complete workflow.
• Figure 3: The spectra manipulation panel of SPAN.
• Figure 4: Distribution of luminosity weighted age, metallicity and $\alpha$-enhancement measured with the Lick/IDS indices and the thomas2011 models for 4175 Voronoi bins of the galaxy NGC 1097 analysed in \ref{['sec:examples']}, between the multi-dimensional linear interpolation with the griddata function of the SciPy module (dark grey) and the machine learning GPR method (red).
• Figure 5: Residuals between the EWs measured by SPAN and the tabulated E-MILES values for four optical line-strength indices widely used as age, metallicity, and [$\alpha$/Fe] diagnostics: H$\beta$, Mgb, Fe5270, and Fe5335. The dashed line marks the zero level, while the vertical extent of the Y axis corresponds to the typical uncertainty range expected at $\mathrm{S/N}\!\approx\!100$, as discussed by cardiel1998.