Lightstack: A Python Package for Creating Photometric Data Cubes
arXiv:2606.20360 · doi:10.3847/2515-5172/ae7c78
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 PSF 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 together with a Jupyter tutorial notebook. The version used for this publication (v0.2.1) is archived on Zenodo.
4 pages, 1 figure, published in RNAAS
References in corpus (8)
- Astropy: A Community Python Package for Astronomy
- The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
- The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package
- LSST: from Science Drivers to Reference Design and Anticipated Data Products
- Evolutionary population synthesis: models, analysis of the ingredients and application to high-z galaxies
- The propagation of uncertainties in stellar population synthesis modeling I: The relevance of uncertain aspects of stellar evolution and the IMF to the derived physical properties of galaxies
- UV-extended E-MILES stellar population models: young components in massive early-type galaxies
- The miniJPAS survey: a preview of the Universe in 56 colours