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

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-modelling 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 $\sim 10$ ms, a factor $\approx 250$ faster than dysmalpy for a single realization. For well-resolved systems (PSF FWHM $< R_{\rm eff}$), the mock observed rotation and dispersion curves agree to within 5 per cent up to $3R_{\rm eff}$, whereas in marginally resolved systems (PSF FWHM $\gtrsim 1.5 R_{\rm eff}$) discrepancies increase to up to 15 per cent. Using a built-in MCMC fitting procedure, RotCurves recovers well the intrinsic model parameters across a wide range of galaxy properties, accounting for realistic noise patterns. Systematic biases emerge for the effective radius and for low disc masses ($M_{\rm disc} \lesssim 3 \times 10^{9} \, \rm M_\odot$). We show excellent parameter recovery at high signal-to-noise ratios (S/N $\gtrsim 25$ ), with increasing deviations in parameter recovery at lower S/N. RotCurves is best suited for inclinations of $10^\circ < i < 80^\circ$. 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.