RotCurves: A PYTHON package for efficient modelling and fitting of galactic rotation curves at high-z

TL;DR

RotCurves is a fast, flexible Python tool for modeling and fitting galactic rotation curves at high redshift, correcting for beam smearing and enabling efficient analysis of large IFU survey data.

Contribution

It introduces RotCurves, a novel, highly efficient Python package that significantly speeds up rotation curve analysis and improves parameter recovery compared to existing methods.

Findings

RotCurves is approximately 250 times faster than dysmalpy.

It recovers galaxy parameters with less than 5% error in well-resolved systems.

Parameter recovery remains robust at high S/N ratios, with some biases at low S/N.

Abstract

Rotation curves are a fundamental tool in the study of galaxies across cosmic time, and with the advent of large integral field unit (IFU) kinematic surveys there is an increasing need for efficient and flexible modelling tools. We present RotCurves, a parametric forward-modeling tool designed for rotation curve analysis at high-z, correcting for ``beam smearing" by projecting and convolving the beam PSF in the plane of the galaxy. We benchmark RotCurves against the established parametric code dysmalpy using synthetic observations. The typical runtime with RotCurves is a few ~10ms, a factor 250 faster than dysmalpy for a single realization. For well-resolved systems (PSF FWHM < Reff), the mock observed rotation and dispersion curves agree to within 5% up to 3Reff, where most of the discrepancies are in the inner disk. whereas in marginally resolved systems (PSF FWHM > 1.5 Reff) discrepancies increase to up to 15%. Using a built-in MCMC fitting procedure, RotCurves recovers well the intrinsic model parameters across a wide range of galaxy properties and accounting for realistic noise patterns. Systematic biases emerge for the effective radius and for low disk masses (Mdisk < 3x10^9 Msun). We show excellent parameter recovery at high signal-to-noise ratios (S/N > 25), with increasing deviations in parameter recovery at lower S/N. RotCurves is best suited for inclinations of 10 < i < 80. RotCurves is built as an exploratory tool for rapid testing of mass model assumptions, parameter studies and for efficiently processing large samples of observational data from large IFU surveys. The code is publicly available on github.