Data Release: Spin it as you like: the (lack of a) measurement of the spin tilt distribution with LIGO-Virgo-KAGRA binary black holes

This is the data release associated with https://arxiv.org/abs/2209.06978

Contains all of the hyper posterior samples for the runs listed in Tables G.1.

The files are in json format. Bilby offers a dedicated routine to read them in

import bilby
data= bilby.core.result.read_in_result(path_to_json)

See the Bilby documentation for what is contained in the result object. 

For each run, we report the posterior hyper samples for the mass model, reshift model, spin magnitude model, spin tilt model and merger rate [Gpc^-3 yr^-1]

Here the name used to store and a short description of each parameter:

1. Primary mass model (Power Law + Peak for all runs)
   1. power_law_slope_m1, slope of the primary mass power law component
   2. minmass_m1, minimum BH mass
   3. maxmass_m1, maximum BH mass
   4. low_end_smoothing_m1, smoothing at the low-mass end
   5. peak_branchingratio_m1, branching ratio between Gaussian peak and power law (1= 100% peak)
   6. peak_mean_m1, mean of the Gaussian peak
   7. peak_sigma_m1, sigma of the Gaussian peak 
2. Mass ratio model (power law for all runs)
   1. power_law_slope_mass_ratio, slope of the mass ratio 
3. Redshift (power law for all runs)
   1. power_law_slope_redshift, slope of the redshift
4. Spin magnitude (IID beta distributions for all runs)
   1. alpha_chi, first argument of beta distribution
   2. beta_chi, second argument of beta distribution
5. Cosine of tilt angle
   1. Gaussian models
      1. mu_0_costilt, for Gaussian models w/o correlation, the mean of the left (or only) Gaussian
      2. sigma_0_costilt, for Gaussian models w/o correlation, the sigma of the left (or only) Gaussian
      3. mu_1_costilt, for Gaussian models w/o correlation, the mean of the right Gaussian
      4. sigma_1_costilt, for Gaussian models w/o correlation, the sigma of the right Gaussian
      5. mu_a_costilt, for Gaussian model with correlation, the constant part of the Gaussian mean
      6. mu_b_costilt, for Gaussian model with correlation, the coefficient of the linearly evolving part of the Gaussian mean
      7. sigma_a_costilt, for Gaussian model with correlation, the constant part of the Gaussian sigma
      8. sigma_b_costilt, for Gaussian model with correlation, the coefficient of the linearly evolving part of the Gaussian sigma
   2. Beta models
      1. alpha_a_costilt, for all Beta models, the constant part of the first parameter of the Beta distribution
      2. alpha_b_costilt, for all Beta models, the coefficient of the linearly evolving part of the first parameter of the Beta distribution
      3. beta_a_costilt, for all Beta models, the constant part of the second parameter of the Beta distribution
      4. beta_b_costilt, for all Beta models, the coefficient of the linearly evolving part of the second parameter of the Beta distribution
   3. Tukey models:
      1. tukey_x0, the center of the Tukey as defined in appendix E of the paper
      2. tukey_k, Tk as defined in appendix E of the paper
      3. tukey_r, Tk as defined in appendix E of the paper
   4. Branching ratios:
      1. spin_mixture_0, for 2-component models, this is the branching ratio of the non-isotropic component
      2. spin_mixture_1, for Isotropic + Gaussian + Tukey and Isotropic + Gaussian + Beta this is the branching ratio of the Gaussian component; for Isotropic + 2 Gaussian this is the branching ratio of the Gaussian on the right.
6. Merger rate
   1. rates, merger rate per unit Gpc cubed per unit year

Note that some of the parameters for the tilt models might not be used, but still stored (and fixed to - usually - zero). This can be checked by verifying what priors were used for each parameter. For example the Isotropic run was obtained from the Isotropic + Gaussian  model by setting the branching ratio of the Gaussian component to zero (at which point the values of mu and sigma costitl are irrelevant) 

> data['prior']
 {'alpha_chi': Uniform(minimum=1, maximum=5, name='alphachi', latex_label='$\text{Missing open brace for subscript}$', unit=None, boundary=None),
 'beta_chi': Uniform(minimum=1, maximum=5, name='betachi', latex_label='$\text{Missing open brace for subscript}$', unit=None, boundary=None),
 'spin_mixture_0': DeltaFunction(peak=0, name=None, latex_label=None, unit=None),
 'power_law_slope_mass_ratio': Uniform(minimum=-2, maximum=7, name=None, latex_label='$beta$', unit=None, boundary=None),
 'power_law_slope_m1': Uniform(minimum=1, maximum=6, name=None, latex_label='$alpha$', unit=None, boundary=None),
 'mu_0_costilt': DeltaFunction(peak=0, name=None, latex_label=None, unit=None),
 'mu_1_costilt': DeltaFunction(peak=0.0, name=None, latex_label=None, unit=None),
 'sigma_0_costilt': DeltaFunction(peak=1, name=None, latex_label=None, unit=None),
 'sigma_1_costilt': DeltaFunction(peak=0.0, name=None, latex_label=None, unit=None),
 'peak_branchingratio_m1': Uniform(minimum=0, maximum=0.25, name=None, latex_label='$lambda$', unit=None, boundary=None),
 'power_law_slope_redshift': Uniform(minimum=-2, maximum=10, name='lamb', latex_label='$lambd{a}_z$', unit=None, boundary=None),
 'peak_mean_m1': Uniform(minimum=20, maximum=45, name=None, latex_label='$m{u}_m$', unit=None, boundary=None),
 'peak_sigma_m1': Uniform(minimum=1, maximum=10, name=None, latex_label='$sigm{a}_m$', unit=None, boundary=None),
 'minmass_m1': Uniform(minimum=2, maximum=10, name=None, latex_label='$$\begin{gathered} m_{ \\ min} \end{gathered}$$', unit=None, boundary=None),
 'maxmass_m1': Uniform(minimum=60, maximum=100, name=None, latex_label='$$\begin{gathered} m_{ \\ max} \end{gathered}$$', unit=None, boundary=None),
 'low_end_smoothing_m1': Uniform(minimum=0, maximum=10, name=None, latex_label='$delt{a}_m$', unit=None, boundary=None)}

Drop me (Salvatore Vitale) an email if anything doesn't work or if you spot issues. Thanks!