  We developed Convolutional Neural Networks (CNNs) to rapidly and directly
infer the planet mass from radio dust continuum images. Substructures induced
by young planets in protoplanetary disks can be used to infer the potential
young planets' properties. Hydrodynamical simulations have been used to study
the relationships between the planet's properties and these disk features.
However, these attempts either fine-tuned numerical simulations to fit one
protoplanetary disk at a time, which was time-consuming, or azimuthally
averaged simulation results to derive some linear relationships between the gap
width/depth and the planet mass, which lost information on asymmetric features
in disks. To cope with these disadvantages, we developed Planet Gap neural
Networks (PGNets) to infer the planet mass from 2D images. We first fit the
gridded data in Zhang et al. (2018) as a classification problem. Then, we
quadrupled the data set by running additional simulations with near-randomly
sampled parameters, and derived the planet mass and disk viscosity together as
a regression problem. The classification approach can reach an accuracy of
92\%, whereas the regression approach can reach 1$\sigma$ as 0.16 dex for
planet mass and 0.23 dex for disk viscosity. We can reproduce the degeneracy
scaling $\alpha$ $\propto$ $M_p^3$ found in the linear fitting method, which
means that the CNN method can even be used to find degeneracy relationship. The
gradient-weighted class activation mapping effectively confirms that PGNets use
proper disk features to constrain the planet mass. We provide programs for
PGNets and the traditional fitting method from Zhang et al. (2018), and discuss
each method's advantages and disadvantages.
