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

TL;DR

AstronomyCalc is a Python toolkit designed to enhance university-level astrophysics education by integrating theoretical calculations, data analysis, and visualization tools for cosmology and astronomy.

Contribution

It introduces a comprehensive, user-friendly Python package that combines cosmological calculations, synthetic data generation, and analysis tools tailored for teaching and learning.

Findings

Enables solving Friedmann equations and exploring cosmological models.

Provides tools for generating and analyzing synthetic and real astronomical data.

Includes simplified algorithms like MCMC for educational purposes.

Abstract

Understanding astrophysical and cosmological processes can be challenging due to their complexity and lack of intuitive analogies. To address this, we present \texttt{AstronomyCalc}, a Python package specifically designed to aid university-level teaching by integrating theoretical physics with practical astronomical data analysis methods. The package enables students to solve key cosmological calculations, such as the Friedmann equations, and explore various models while visualizing how parameter variations affect cosmic dynamics. It includes tools for generating synthetic astronomical data, such as Type Ia supernova measurements, and supports analysis of publicly available datasets, including Pantheon+ and the SPARC galaxy database. Simplified implementations of advanced algorithms, such as Monte Carlo Markov Chains, allow students to engage with data analysis techniques used in contemporary research. Additionally, \texttt{AstronomyCalc} will be consistently updated with more tools and user-friendly Jupyter notebooks, making it a continually evolving educational resource for developing conceptual understanding and practical skills in astrophysics and cosmology.