An Artificial Neural Network Approach For Ranking Quenching Parameters
  In Central Galaxies
  We present a novel technique for ranking the relative importance of galaxy
properties in the process of quenching star formation. Specifically, we develop
an artificial neural network (ANN) approach for pattern recognition and apply
it to a population of over 400,000 central galaxies taken from the Sloan
Digital Sky Survey Data Release 7. We utilise a variety of physical galaxy
properties for training the pattern recognition algorithm to recognise star
forming and passive systems, for a `training set' of $\sim$100,000 galaxies. We
then apply the ANN model to a `verification set' of $\sim$100,000 different
galaxies, randomly chosen from the remaining sample. The success rate of each
parameter singly, and in conjunction with other parameters, is taken as an
indication of how important the parameters are to the process(es) of central
galaxy quenching. We find that central velocity dispersion, bulge mass and B/T
are excellent predictors of the passive state of the system, indicating that
properties related to the central mass of the galaxy are most closely linked to
the cessation of star formation. Larger scale galaxy properties (total or disk
stellar masses), or those linked to environment (halo masses or $\delta_5$)
perform significantly less well. Our results are plausibly explained by AGN
feedback driving the quenching of central galaxies, although we discuss other
possibilities as well.
