Theme: Data science challenges: tools from statistics to machine learning
Non Parametric morphologies of galaxies in the EAGLE simulation
We study the optical morphology of galaxies in a large-scale hydrodynamic cosmological simulation, the EAGLE simulation. Galaxy morphologies were characterized using non-parametric statistics (Gini, M20, Concentration, and Asymmetry) derived from mock images computed using 3D radiative transfer techniques and post-processed to mimic observational surveys. We compare the simulated morphologies against galaxies in the GAMA survey and found that the simulation correctly reproduces many morphological trends. Also, we determine the role of stellar mass, star formation rate, color and size on the optical appearance of galaxies, and contrasted optical morphologies to other classifications schemes based on the internal kinematics of stars. Finally, we determine the effect that orientation and spatial resolution have on non-parametric morphologies. This study has important implications for future surveys, such as LSST, where this kind of techniques is going to be implemented on an extremely large scale. The fact that the simulation correctly reproduces many morphological characteristics in this quantifiable way means that it can be used as a powerful testbed for new classification techniques.
