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AstronomyCalc: A python toolkit for teaching Astronomical Calculations and Data Analysis methods

Year: 2025
Type: article
Abstract: Understanding astrophysical and cosmological processes can be challenging due to their complexity and the lack of simple, everyday analogies.To address this, we present AstronomyCalc, a user-friendly Python package designed to facilitate the learning of these processes and help develop insights based on the variation theory of learning (Ling Lo, 2012;Lo & Marton, 2011).AstronomyCalc enables students and educators to engage with key astrophysical and cosmological calculations, such as solving the Friedmann equations, which are fundamental to modeling the dynamics of the universe.The package allows users to construct and explore various cosmological models, including the de Sitter and Einstein-de Sitter universes (see Ryden, 2017 for more examples), by adjusting key parameters such as matter density and the Hubble constant.This interactive approach helps users intuitively grasp how variations in these parameters affect properties like expansion rates and cosmic time evolution.Additionally, the package is designed to be easily expanded with additional astronomical calculations as needed for a course.AstronomyCalc also includes modules for generating synthetic astronomical data or accessing publicly available datasets.In its current version, users can generate synthetic Type Ia supernova measurements of cosmological distances (VanderPlas et al., 2012) or utilize the publicly available Pantheon+ dataset (Brout et al., 2022).Additionally, the package supports the download and analysis of the SPARC dataset, which contains galaxy rotation curves for 175 disk galaxies (Lelli et al., 2016).
DOI: 10.21105/jose.00261
Language: English
Open Access status: diamond
OpenAlex ID: W4410157325
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Cites: 8
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Related to: 10