The Data Behind Dark Matter: Exploring Galactic Rotation
A. N. Villano, Kitty C. Harris, Judit Bergfalk, Raphael Hatami,, Francis Vititoe, Julia Johnston

TL;DR
This paper introduces an educational Python-based workshop that guides users through creating galactic rotation curves using real data, helping to understand dark matter's influence on galaxy dynamics.
Contribution
It provides an accessible, reproducible method for visualizing dark matter effects through interactive notebooks and real astronomical data.
Findings
Interactive workshop demonstrates dark matter's impact on galaxy rotation
Uses real data from the SPARC database for educational purposes
Provides a step-by-step guide to constructing and fitting rotation curves
Abstract
Dark matter is estimated to make up ~84% of all normal/baryonic matter, but cannot be directly imaged. Despite the fact that dark matter cannot be directly observed yet, its influence on the motion of stars and gas in spiral galaxies have been detected. One way to show motion in galaxies are rotation curves that are plots of velocity measurements of how fast stars and gas move in a galaxy around the center of mass. According to Newton's Law of Gravitation, the rotational velocity is an indication of the amount of visible and non-visible mass in the galaxy. Given that the visible matter is measurable using photometry, dark matter mass can therefore be estimated, offering an insight into the size distribution in galaxies. In order to gain a greater appreciation of the research scientists' findings about dark matter, their method should be easily reproduced by any curious individual. Our…
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Taxonomy
TopicsAstronomical Observations and Instrumentation · Astronomy and Astrophysical Research · History and Developments in Astronomy
