The quenching of galaxies, bulges, and disks since cosmic noon: A
  machine learning approach for identifying causality in astronomical data
  We present an analysis of the quenching of star formation in galaxies,
bulges, and disks throughout the bulk of cosmic history, from $z=2-0$. We
utilise observations from the SDSS and MaNGA at low redshifts. We complement
these data with observations from CANDELS at high redshifts. Additionally, we
compare the observations to detailed predictions from the LGalaxies
semi-analytic model. To analyse the data, we developed a machine learning
approach utilising a Random Forest classifier. We first demonstrate that this
technique is extremely effective at extracting causal insight from highly
complex and inter-correlated model data, before applying it to various
observational surveys. Our primary observational results are as follows: At all
redshifts studied in this work, we find bulge mass to be the most predictive
parameter of quenching, out of the photometric parameter set (incorporating
bulge mass, disk mass, total stellar mass, and $B/T$ structure). Moreover, we
also find bulge mass to be the most predictive parameter of quenching in both
bulge and disk structures, treated separately. Hence, intrinsic galaxy
quenching must be due to a stable mechanism operating over cosmic time, and the
same quenching mechanism must be effective in both bulge and disk regions.
Despite the success of bulge mass in predicting quenching, we find that central
velocity dispersion is even more predictive (when available in spectroscopic
data sets). In comparison to the LGalaxies model, we find that all of these
observational results may be consistently explained through quenching via
preventative `radio-mode' active galactic nucleus (AGN) feedback. Furthermore,
many alternative quenching mechanisms (including virial shocks, supernova
feedback, and morphological stabilisation) are found to be inconsistent with
our observational results and those from the literature.
