Estimating galaxy masses from kinematics of globular cluster systems: a
  new method based on deep learning
  We present a new method by which the total masses of galaxies including dark
matter can be estimated from the kinematics of their globular cluster systems
(GCSs). In the proposed method, we apply the convolutional neural networks
(CNNs) to the two-dimensional (2D) maps of line-of-sight-velocities ($V$) and
velocity dispersions ($\sigma$) of GCSs predicted from numerical simulations of
disk and elliptical galaxies. In this method, we first train the CNN using
either only a larger number ($\sim 200,000$) of the synthesized 2D maps of
$\sigma$ ("one-channel") or those of both $\sigma$ and $V$ ("two-channel").
Then we use the CNN to predict the total masses of galaxies (i.e., test the
CNN) for the totally unknown dataset that is not used in training the CNN. The
principal results show that overall accuracy for one-channel and two-channel
data is 97.6\% and 97.8\% respectively, which suggests that the new method is
promising. The mean absolute errors (MAEs) for one-channel and two-channel data
are 0.288 and 0.275 respectively, and the value of root mean square errors
(RMSEs) are 0.539 and 0.51 for one-channel and two-channel respectively. These
smaller MAEs and RMSEs for two-channel data (i.e., better performance) suggest
that the new method can properly consider the global rotation of GCSs in the
mass estimation. We also applied our proposed method to real data collected
from observations of NGC 3115 to compare the total mass predicted by our
proposed method and other popular methods from the literature.
