  We use machine learning to identify in color images of high-redshift galaxies
an astrophysical phenomenon predicted by cosmological simulations. This
phenomenon, called the blue nugget (BN) phase, is the compact star-forming
phase in the central regions of many growing galaxies that follows an earlier
phase of gas compaction and is followed by a central quenching phase. We train
a Convolutional Neural Network (CNN) with mock "observed" images of simulated
galaxies at three phases of evolution: pre-BN, BN and post-BN, and demonstrate
that the CNN successfully retrieves the three phases in other simulated
galaxies. We show that BNs are identified by the CNN within a time window of
$\sim0.15$ Hubble times. When the trained CNN is applied to observed galaxies
from the CANDELS survey at $z=1-3$, it successfully identifies galaxies at the
three phases. We find that the observed BNs are preferentially found in
galaxies at a characteristic stellar mass range, $10^{9.2-10.3} M_\odot$ at all
redshifts. This is consistent with the characteristic galaxy mass for BNs as
detected in the simulations, and is meaningful because it is revealed in the
observations when the direct information concerning the total galaxy luminosity
has been eliminated from the training set. This technique can be applied to the
classification of other astrophysical phenomena for improved comparison of
theory and observations in the era of large imaging surveys and cosmological
simulations.
