AbstractMulti-component modelling of galaxies is a valuable tool in the effort to quantitatively understand galaxy evolution, yet the use of the technique is plagued by issues of convergence, model selection and parameter degeneracies. These issues limit its application over large samples to the simplest models, with complex models being applied only to very small samples. I have attempted to resolve this dilemma of “quantity or quality” by developing a novel framework, Galaxy Builder, built inside the Zooniverse citizen science platform, to enable the crowdsourcing of complex photometric model creation (containing a disc, bulge, bar and spiral arms) for Sloan Digitial Sky Survey galaxies.
I have applied the method, including a final algorithmic optimisation step, on a sample of 198 galaxies, and examined its internal robustness using a small sample of synthetic galaxies and a repeated validation sample. I also compare its results to automated fitting pipelines, demonstrating that it is possible to consistently recover accurate models that either show good agreement with, or improve on, prior work.
I have made use of the crowdsourced spiral annotations from the Galaxy Builder project in a hierarchical Bayesian model to examine the relationship between spiral tightness (pitch angle) and central morphology (bulge strength, bar presence and strength), finding that central morphology does not significantly impact spiral pitch angle. I also made use of this Bayesian framework to test a simple model of spiral winding, finding support for the picture of spiral arms as transient and recurrent disc instabilities.
I conclude that citizen science is a promising technique for modelling images of complex galaxies, and release our catalogue of models/
|Date of Award||Oct 2020|
|Supervisor||Coleman Krawczyk (Supervisor), Karen Louise Masters (Supervisor) & Bob Nichol (Supervisor)|