SpheCow: flexible dynamical models for galaxies and dark matter haloes

TL;DR

SpheCow is a new, flexible, and lightweight code that enables detailed exploration of spherical galaxy and dark matter halo models, supporting both analytical and novel sigmoid-based density profiles.

Contribution

It introduces SpheCow, a versatile code for modeling spherical systems with easy extensibility and validation, including new models with sigmoid density slopes.

Findings

Validated against analytical and numerical calculations.

Includes implementations for standard models like NFW and Hernquist.

Demonstrated addition of new sigmoid-based models.

Abstract

Simple but flexible dynamical models are useful for many purposes, including serving as the starting point for more complex models or numerical simulations of galaxies, clusters, or dark matter haloes. We present SpheCow, a new light-weight and flexible code that allows one to easily explore the structure and dynamics of any spherical model. Assuming an isotropic or Osipkov-Merritt anisotropic orbital structure, the code can automatically calculate the dynamical properties of any model with either an analytical density profile or an analytical surface density profile as starting point. We have extensively validated SpheCow using a combination of comparisons to analytical and high-precision numerical calculations, as well as the calculation of inverse formulae. SpheCow contains readily usable implementations for many standard models, including the Plummer, Hernquist, NFW, Einasto, S\'ersic, and Nuker models. The code is publicly available as a set of C++ routines and as a Python module, and it is designed to be easily extendable, in the sense that new models can be added in a straightforward way. We demonstrate this by adding two new families of models in which either the density slope or the surface density slope is described by an algebraic sigmoid function. We advocate the use of the SpheCow code to investigate the full dynamical structure for models for which the distribution function cannot be expressed analytically and to explore a much wider range of models than is possible using analytical models alone.