Beyond single tracers: CNN-based inference of galaxy mass profiles from combined gas and stellar kinematics

We investigate whether combining gas and stellar kinematic maps provides measurable advantages in recovering galaxy mass profiles, compared to using single-component maps alone. While traditional methods struggle to integrate multi-tracer data effectively, we test whether deep learning models can leverage this joint information. We develop a probabilistic convolutional neural network (CNN) framework trained and tested on mock galaxy kinematic maps from multiple cosmological simulation suites. Our model is trained on gas-only, stars-only, and combined gas+stellar velocity maps, allowing direct comparison of performance across tracers. To assess robustness, we include simulations with differing feedback models and galaxy properties. Combining gas and stellar maps reduces the dispersion in the inferred mass profiles by up to a factor of $\sim$1.5 compared to models using either tracer independently. The CNN architecture effectively captures complementary information from the two components. However, we find limitations in generalizing between simulation suites, with reduced performance when applying models trained on one suite to galaxies from another.