The Nearly Universal Disk Galaxy Rotation Curve

Abstract

The universal rotation curve (URC) of disk galaxies was originally proposed to predict the shape and amplitude of any rotation curve (RC) based solely on photometric data. Here, the URC is investigated with an extensive set of spatially resolved RCs drawn from the PROBES-I, PROBES-II, and MaNGA databases with matching multiband surface brightness profiles from the DESI-LIS and Wide-Field Infrared Survey Explorer surveys for 3846 disk galaxies. Common URC formulations fail to achieve an adequate level of accuracy to qualify as truly universal over fully sampled RCs. We develop neural network (NN) equivalents for the proposed URCs that predict RCs with higher accuracy, showing that URC inaccuracies are not due to insufficient data but rather nonoptimal formulations or sampling effects. This conclusion remains even if the total RC sample is pruned for symmetry. The latest URC prescriptions and their NN equivalents trained on our subsample of 579 disk galaxies with symmetric RCs perform similarly to the URC/NN trained on the complete data sample. We conclude that a URC with an acceptable level of accuracy (ΔV circ ≲ 15%) at all radii would require a detailed modeling of a galaxy’s central regions and outskirts (e.g., for baryonic effects leading to contraction or expansion of any dark-matter-only halo).