The Data Behind Dark Matter: Exploring Galactic Rotation
| Autor: | Villano, A. N., Harris, Kitty C., Bergfalk, Judit, Hatami, Raphael, Vititoe, Francis, Johnston, Julia |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Journal of Open Source Education (2023), 6(66), 184 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.21105/jose.00184 |
| Popis: | 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 interactive workshop is an excellent educational tool to investigate how dark matter impacts the rotation of visible matter by providing a guide to produce galactic rotation curves. The Python-based notebooks are set up to walk you through the whole process of producing rotation curves using an online database (SPARC) and to allow you to learn about each component of the galaxy. The three steps of the rotation curve building process is plotting the measured velocity data, constructing the rotation curves for each component, and fitting the total velocity to the measured values. Comment: 6 pages, 1 table |
| Databáze: | arXiv |
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