DOI: 10.5281/zenodo.12759107
title: Data: Dynamics of star clusters with tangentially anisotropic velocity distribution (Pavlik+ 2024)

description: This dataset represents the results of our N-body simulations of star clusters (SCs). The initial conditions of the models are fully described in the referenced journal article. In short, the SCs start from isotropic, radially anisotropic or tangentially anisotropic initial velocity distributions, and each model is evolved in an external Galactic tidal field, for two different choices of the filling factor.
description: The names of the data files correspond to the figures in the paper.

 

fig1, fig2

ASCII tables with the columns described in the header. Lines are commented with '#' and columns are separated by spaces.

 

fig3, fig4, fig5, fig6, fig7, fig10, fig13

ASCII tables with the columns described in the header. Each filename has a label describing the model, e.g. fig3_iso-u contains the data for plotting the panel of Figure 3 which shows the underfilling isotropic model. Lines are commented with '#' and columns are separated by spaces.

 

fig8_fig11.var

Python dictionary compressed in a binary pickle format. Read with:

import numpy as np
import pickle
with open('fig8_fig11.var', 'rb') as f:
    mydict = pickle.load(f)

To show the structure of mydict, we provide this mock script which reads the data:

''' models based on velocity anisotropy '''for mod_ in ["tan", "iso", "rad", "rh1"]: # mydict[mod_] is a dictionary
        ''' filling factors '''        for run_ in ["-u", "-f"]: # mydict[mod_][run_] is a dictionary
        
        ''' histogram edges for each model & run '''
        
        t_edges = mydict[mod_][run_]["t_edges"]
        # 1D np.array of time outputs

        r_edges = mydict[mod_][run_]["r_edges"]
        # 2D np.array of radial bins edges for each time bin
        # axis 0 has length of len(t_edges)-1
        
        ''' anisotropy parameter beta '''
        
        for mass_ in ["low", "medium", "high", "all"]: # separate by stellar mass
            beta = mydict[mod_][run_][mass_]
            # 2D np.array containing beta in each radius-time bin
            # shape is the same as r_edges[:,:-1].shape



fig9_fig12.var

Python dictionary compressed in a binary pickle format. Read with:

import numpy as np
import pickle
with open('fig9_fig12.var', 'rb') as f:
    mydict = pickle.load(f)

To show the structure of mydict, we provide this mock script which reads the data:

''' models based on velocity anisotropy '''for mod_ in ["tan", "iso", "rad", "rh1"]: # mydict[mod_] is a dictionary
        ''' filling factors '''        for run_ in ["-u", "-f"]: # mydict[mod_][run_] is a dictionary
        
        ''' histogram edges for each model & run '''
        
        t_edges = mydict[mod_][run_]["t_edges"]
        # 1D np.array of time outputs

        r_edges = mydict[mod_][run_]["r_edges"]
        # 2D np.array of radial bins edges for each time bin
        # axis 0 has length of len(t_edges)-1
        
        ''' velocity dispersion used for fitting '''
        
        for disp_ in ["3D", "rad", "tan"]: # mydict[mod_][run_][disp_] is a dictionary
                        ''' fitted equipartition mass '''            
            m_eq = mydict[mod_][run_][disp_]["m_eq"]
            # 2D np.array containing the equipartition mass in each radius-time bin
            # shape is the same as r_edges[:,:-1].shape
            
            m_err = mydict[mod_][run_][disp_]["m_err"]
            # 2D np.array containing the uncertainty of the equipartition mass in each radius-time bin
            # shape is the same as r_edges[:,:-1].shape
            
            eep_flag = mydict[mod_][run_][disp_]["eep_flag"]
            # 2D np.array containing 1 if the radius-time bin is in energy equipartition or 0 if not
            # shape is the same as r_edges[:,:-1].shape