Gravlite is a tool to determine the mass distribution in disc-like and spherical systems. It takes as input a tracer density distribution, a line-of-sight velocity dispersion, and the velocity's fourth moment as a function of radius. It then generates a highly-dimensional parameter space for tracer density, overall density distribution, and possibly a velocity anisotropy profile ands traces it with a simple Markov Chain Monte Carlo method.

Following packets need to be installed on your system:

• python3
• matplotlib/pylab
• scipy

Unzip the file gravlite.zip, and run

python3 gravlite.py;

Sample parameter files for several possible scenarios are stored in the subfolder ./params. The file gl_params.py is a soft link to one of them. Following mass modelling methods have been implemented so far:

• gl_params_walker.py: spherical Walker mock data from the Gaia challenge catalogue, 2 populations
• gl_params_gaia.py: spherical mock data from the Gaia challenge catalogue, 1 population
• gl_params_hern.py: spherical mock data taken from a Hernquist profile
• gl_params_simple.py: disk-like mock data, generated on the fly
• gl_params_sim.py: disk-like mock data, from a simulation by S. Garbari

Initial values for all profiles and the respective stepsizes are set in gl_init.py. We use following representations:

• overall density rho: can either be set as rho_i=rho(r_i), where r_i are the radii of bins, or the coefficients of the Legendre polynomial;
• tracer densities nu_i = nu(r_i) in linear space or nu_i = log(nu(r_i)) in logarithmic space;
• velocity anisotropy beta_i are incremental changes from beta(r=0) = 0.

Have fun!