The Nearly Universal Disk Galaxy Rotation Curve
Raj Patel (Queen's), Nikhil Arora (Queen's), St\'ephane Courteau, (Queen's), Connor Stone (UdeM), Matthew Frosst (ICRAR/UWA), Lawrence Widrow, (Queen's)

TL;DR
This study evaluates the Universal Rotation Curve (URC) for disk galaxies using extensive data, finds existing formulations inadequate, and develops neural network models that improve rotation curve predictions, highlighting the need for detailed galaxy modeling.
Contribution
The paper demonstrates that traditional URC models are insufficiently accurate and introduces neural network equivalents that better predict galaxy rotation curves, emphasizing the importance of detailed galaxy modeling.
Findings
URC formulations fail to be truly universal across sampled rotation curves.
Neural network models outperform traditional URC formulations in predicting rotation curves.
Accurate rotation curve modeling requires detailed analysis of galaxy centers and outskirts.
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 rotation curves drawn from the PROBES-I, PROBES-II, and MaNGA data bases with matching multi-band surface brightness profiles from the DESI-LIS and WISE surveys for 3,846 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 which predict RCs with higher accuracy, showing that URC inaccuracies are not due to insufficient data but rather non-optimal formulations or sampling effects. This conclusion remains even if the total RC sample is pruned for symmetry. The latest URC prescriptions and their NN…
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Taxonomy
TopicsStellar, planetary, and galactic studies
