English

A Velocity Coupled Radial Acceleration Ansatz for Disk-Galaxy Rotation Curves: Fits to SPARC, Bayesian Inference, and Parameter Identifiability

Astrophysics of Galaxies 2026-04-07 v2 Computational Physics

Abstract

Observed rotation curves of disk galaxies remain a sharp empirical probe of the relationship between baryons and dynamics. We study a minimal, explicitly \emph{phenomenological} alternative to standard halo parameterizations: an additional inward \emph{radial} acceleration proportional to the local \emph{tangential} speed, a\vca(r)=γ(r)v(r)a_{\vca}(r)=\gamma(r)\,v(r), with a saturating coupling γ(r)=\vinf/(r+\rzero)\gamma(r)=\vinf/(r+\rzero). Combining this ansatz with the circular-motion condition yields a quadratic equation for v(r)v(r) with a closed-form physical branch.We fit this ``velocity-coupled acceleration'' (\vca) model to Ngal=171N_\mathrm{gal}=171 rotation curves from the SPARC sample using the published baryonic decompositions (gas, disk, bulge), and we compare to two commonly used two-parameter halo models (NFW and Burkert) using an identical optimization pipeline and error model.For a fiducial systematic error floor σ0=5 kms1\sigma_0=5~\mathrm{km\,s^{-1}}, the \vca model is typically competitive with an NFW halo and performs comparably (though not uniformly better) than a Burkert halo in information-criterion comparisons.We further perform MCMC inference for \vca parameters, quantify posterior predictive coverage, and show that parameter posteriors exhibit a strong \vinf\vinf--\rzero\rzero degeneracy for many galaxies; only 47/17147/171 galaxies yield well-identified posteriors under simple width-based criteria. We also perform a simple radial holdout cross-validation (outer 30\% of radii) and find predictive RMSE comparable to NFW and Burkert under this protocol.Finally..

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Cite

@article{arxiv.2512.21376,
  title  = {A Velocity Coupled Radial Acceleration Ansatz for Disk-Galaxy Rotation Curves: Fits to SPARC, Bayesian Inference, and Parameter Identifiability},
  author = {Nalin Dhiman},
  journal= {arXiv preprint arXiv:2512.21376},
  year   = {2026}
}