  Stellar feedback created by radiation and winds from massive stars plays a
significant role in both physical and chemical evolution of molecular clouds.
This energy and momentum leaves an identifiable signature ("bubbles") that
affect the dynamics and structure of the cloud. Most bubble searches are
performed "by-eye", which are usually time-consuming, subjective and difficult
to calibrate. Automatic classifications based on machine learning make it
possible to perform systematic, quantifiable and repeatable searches for
bubbles. We employ a previously developed machine learning algorithm, Brut, and
quantitatively evaluate its performance in identifying bubbles using synthetic
dust observations. We adopt magneto-hydrodynamics simulations, which model
stellar winds launching within turbulent molecular clouds, as an input to
generate synthetic images. We use a publicly available three-dimensional dust
continuum Monte-Carlo radiative transfer code, hyperion, to generate synthetic
images of bubbles in three Spitzer bands (4.5 um, 8 um and 24 um). We designate
half of our synthetic bubbles as a training set, which we use to train Brut
along with citizen-science data from the Milky Way Project. We then assess
Brut's accuracy using the remaining synthetic observations. We find that after
retraining Brut's performance increases significantly, and it is able to
identify yellow bubbles, which are likely associated with B-type stars. Brut
continues to perform well on previously identified high-score bubbles, and over
10% of the Milky Way Project bubbles are reclassified as high-confidence
bubbles, which were previously marginal or ambiguous detections in the Milky
Way Project data. We also investigate the size of the training set, dust model,
evolution stage and background noise on bubble identification.
