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paper/paper.md

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 # Summary
 
-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 [@lo2011towards; @ling2012variation].
+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 [@lo2011towards;@ling2012variation].
 
 `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 @ryden2017introduction 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.