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We present a new method by which the total masses of galaxies including darkmatter 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 ($V$) andvelocity dispersions ($\sigma$) of GCSs predicted from numerical simulations ofdisk and elliptical galaxies. In this method, we first train the CNN usingeither 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 theCNN) for the totally unknown dataset that is not used in training the CNN. Theprincipal results show that overall accuracy for one-channel and two-channeldata is 97.6\% and 97.8\% respectively, which suggests that the new method ispromising. The mean absolute errors (MAEs) for one-channel and two-channel dataare 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. Thesesmaller MAEs and RMSEs for two-channel data (i.e., better performance) suggestthat the new method can properly consider the global rotation of GCSs in themass estimation. We also applied our proposed method to real data collectedfrom observations of NGC 3115 to compare the total mass predicted by ourproposed method and other popular methods from the literature.

Estimating galaxy masses from kinematics of globular cluster systems: a new method based on deep learning