  Conventional galaxy mass estimation methods suffer from model assumptions and
degeneracies. Machine learning, which reduces the reliance on such assumptions,
can be used to determine how well present-day observations can yield
predictions for the distributions of stellar and dark matter. In this work, we
use a general sample of galaxies from the TNG100 simulation to investigate the
ability of multi-branch convolutional neural network (CNN) based machine
learning methods to predict the central (i.e., within $1-2$ effective radii)
stellar and total masses, and the stellar mass-to-light ratio $M_*/L$. These
models take galaxy images and spatially-resolved mean velocity and velocity
dispersion maps as inputs. Such CNN-based models can in general break the
degeneracy between baryonic and dark matter in the sense that the model can
make reliable predictions on the individual contributions of each component.
For example, with $r$-band images and two galaxy kinematic maps as inputs, our
model predicting $M_*/L$ has a prediction uncertainty of 0.04 dex. Moreover, to
investigate which (global) features significantly contribute to the correct
predictions of the properties above, we utilize a gradient boosting machine. We
find that galaxy luminosity dominates the prediction of all masses in the
central regions, with stellar velocity dispersion coming next. We also
investigate the main contributing features when predicting stellar and dark
matter mass fractions ($f_*$, $f_{\rm DM}$) and the dark matter mass $M_{DM}$,
and discuss the underlying astrophysics.
