Galaxies are the basic building blocks of the Universe. Knowing how they grow is a key challenge of current astronomy, and this history is shown by their appearance. Unfortunately, right now this work uses only small samples studied by eye. To sort out the physics needs very large numbers of objects. Future surveys will provide the data but visual inspection will not be feasible. This study will apply advanced methods from the field of machine learning. Comparing real galaxies from space-based and ground-based data to computer models will make major progress in this all-important field. The lead researcher will work hard to overcome major barriers acting against women and minorities in scientific fields. This involves several different targeted activities.
Understanding the physical processes responsible for the growth of galaxies is one of the key challenges in extragalactic astronomy. The assembly history of a galaxy is imprinted in its detailed morphology, but current quantitative classification schemes are only useful for broad binning. Thus, the comparison of observations with theoretical predictions has only used small samples of visually classified galaxies. Coping with the large samples needed to disentangle the complex physics involved requires a robust quantitative classification scheme. This study will implement a promising machine learning algorithm that has proven successful at identifying bars, single-armed or multi-armed galaxies, rings and Hubble type. The project will: determine a robust quantitative classification and apply it to multiple ground and space based data sets; study the dependence of bars, rings and spiral arms on properties such as mass, star formation, environment and redshift; determine triggering and destruction mechanisms of bars and spiral arms; compare observed trends to those of simulated galaxies; and apply this robust classification scheme to 3D data cubes to compare stellar, gas and kinematic morphologies. The work for countering the underrepresentation of women and minorities in STEM fields includes more frequent public observing nights, buying a portable planetarium to encourage daytime visits by university and school students, creating planetarium-based lecture-tutorials and interactive demonstrations for teachers and pupils, and arranging discussions with former physics majors about alternate career pathways.
