Year: 2022
Type: software-paper
Abstract: We present Artificial Stellar Populations (ArtPop), an open-source Python package for synthesizing stellar populations and generating artificial images of stellar systems, populated star by star. The code is designed to be intuitive to use and as modular as possible, making it possible to use each of its functionalities independently or together. ArtPop has a wide range of scientific and pedagogical use cases, including the measurement of detection efficiencies in current and future imaging surveys, the calculation of integrated stellar population parameters, quantitative comparisons of isochrone models, and the development and validation of astronomical image-processing algorithms. In this paper, we give an overview of the ArtPop package, provide simple coding examples to demonstrate its implementation, and present results from some potential applications of the code. We provide links to the source code that created each example and figure throughout the paper. ArtPop is under active development, and we welcome bug reports, feature requests, and code contributions from the community. https://artpop.readthedocs.io/en/latest/ https://github.com/ArtificialStellarPopulations/ArtPop/ https://artpop.readthedocs.io/en/latest/tutorials/quickstart.html https://github.com/ArtificialStellarPopulations/artpop-paper-figures/blob/main/scripts/
Source: The Astrophysical Journal
Authors: Johnny P. Greco, Shany Danieli
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,499
OpenAlex ID: W3202946615