The use of MaNGA data to establish the origin of spiral arms in galaxies 

The aim of this project is to establish if the origin of the spiral arms in galaxies is due to stationary density waves or the self-propagation of star formation through a stellar birth-death process or another process. A stationary density wave has been predicted to lead to the compression of gas and dust within a galaxy causing star formation in these unique structures. An alternative view is that spiral arms might arise due to a self-organising critical phenomenon that allows for large scale structure and whose signature is fractal behaviour. This presentation highlights the conflicts in the above opposing theories. To address this question the theories will be tested against observation. The project uses the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey data. The classification of galaxies within, and the difference between three MaNGA data releases and Value Added Catalogues (VACs) will be discussed. The classification of galaxies was investigated, and a catalogue chosen and justified. Galaxy Zoo (GZ) which uses crowd sourcing and is provided with MaNGA data did not have a satisfactory match rate in comparison to the classification by professional astronomers. AI classifications based on GZ were also unsuitable. A recent catalogue based on professional classifications was identified and will be used. MaNGA data releases- DR14, DR15 and DR17- were analysed to track the progression and changes produced by the Data Reduction Pipeline (DRP). Two VACs were investigated which contain derived data products from the DRP in the case of the Pipe3D VAC or from the Data Analysis Pipeline (DAP) in the case of Firefly. Data Releases and VACs are thoroughly compared through histograms, cumulative frequency distributions and correlation plots, both scatter and gaussian kernel density estimation contour were generated. Results were then quantified and interpreted using the Kolmogorov–Smirnov test, standard deviation of differences, reduced chi-squared tests, and bootstrapping.