Abstract
Multi-band photometry traces diverse physical processes across a wide range of wavelengths. In recent decades, this field has been driven by the rapid growth of multi-imaging datasets, from high-resolution observation from Hubble Space Telescope and James Webb Space Telescope to the forthcoming large-scale surveys enabled by the Roman Space Telescope and Rubin Observatory, for example. In this work, we present lightstack, a Python package for combining standalone images into photometric data cubes. The workflow consists of three main steps: cropping a region of interest from a mosaic across all available filters; stacking the images to construct the data cube; and performing point-spread function matching on the cube. This package is intended for preparing data for studies involving multi-band photometry. The code is released under an MIT license and is available on GitHub (https://github.com/AndressaWille/lightstack) together with a Jupyter tutorial notebook. The version used for this publication (v0.2.1) is archived on Zenodo (https://doi.org/10.5281/zenodo.20360028 ).
| Original language | English |
|---|---|
| Article number | 159 |
| Number of pages | 5 |
| Journal | Research Notes of the AAS |
| Volume | 10 |
| Issue number | 6 |
| Early online date | 17 Jun 2026 |
| DOIs | |
| Publication status | E-pub ahead of print - 17 Jun 2026 |
Keywords
- Astronomy data analysis
- Astronomy software
- Galaxies
- Photometry
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