DOI: 10.5281/zenodo.7301707
title: Data Release: Spin it as you like: the (lack of a) measurement of the spin tilt distribution with LIGO-Virgo-KAGRA binary black holes

description: This is the data release associated with <strong>https://arxiv.org/abs/2209.06978</strong> 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<br> 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: Primary mass model (Power Law + Peak for all runs) power_law_slope_m1, slope of the primary mass power law component minmass_m1, minimum BH mass maxmass_m1, maximum BH mass low_end_smoothing_m1, smoothing at the low-mass end peak_branchingratio_m1, branching ratio between Gaussian peak and power law (1= 100% peak) peak_mean_m1, mean of the Gaussian peak peak_sigma_m1, sigma of the Gaussian peak Mass ratio model (power law for all runs) power_law_slope_mass_ratio, slope of the mass ratio Redshift (power law for all runs) power_law_slope_redshift, slope of the redshift Spin magnitude (IID beta distributions for all runs) alpha_chi, first argument of beta distribution beta_chi, second argument of beta distribution Cosine of tilt angle Gaussian models mu_0_costilt, for Gaussian models w/o correlation, the mean of the left (or only) Gaussian sigma_0_costilt, for Gaussian models w/o correlation, the sigma of the left (or only) Gaussian mu_1_costilt, for Gaussian models w/o correlation, the mean of the right Gaussian sigma_1_costilt, for Gaussian models w/o correlation, the sigma of the right Gaussian mu_a_costilt, for Gaussian model with correlation, the constant part of the Gaussian mean mu_b_costilt, for Gaussian model with correlation, the coefficient of the linearly evolving part of the Gaussian mean sigma_a_costilt, for Gaussian model with correlation, the constant part of the Gaussian sigma sigma_b_costilt, for Gaussian model with correlation, the coefficient of the linearly evolving part of the Gaussian sigma Beta models alpha_a_costilt, for all Beta models, the constant part of the first parameter of the Beta distribution alpha_b_costilt, for all Beta models, the coefficient of the linearly evolving part of the first parameter of the Beta distribution beta_a_costilt, for all Beta models, the constant part of the second parameter of the Beta distribution beta_b_costilt, for all Beta models, the coefficient of the linearly evolving part of the second parameter of the Beta distribution Tukey models: tukey_x0, the center of the Tukey as defined in appendix E of the paper tukey_k, Tk as defined in appendix E of the paper tukey_r, Tk as defined in appendix E of the paper Branching ratios: spin_mixture_0, for 2-component models, this is the branching ratio of the non-isotropic component spin_mixture_1, for Isotropic + Gaussian + Tukey and Isotropic + Gaussian + Beta this is the branching ratio of the <strong>Gaussian</strong> component; for Isotropic + 2 Gaussian this is the branching ratio of the <strong>Gaussian on the right.</strong> Merger rate 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 <em>Isotropic</em> run was obtained from the <em>Isotropic + Gaussian </em>model by setting the branching ratio of the Gaussian component to zero (at which point the values of mu and sigma costitl are irrelevant) &gt; data['prior']<br> {'alpha_chi': Uniform(minimum=1, maximum=5, name='alphachi', latex_label='$\\alpha_\\chi$', unit=None, boundary=None),<br> 'beta_chi': Uniform(minimum=1, maximum=5, name='betachi', latex_label='$\\beta_\\chi$', unit=None, boundary=None),<br> <strong> 'spin_mixture_0': DeltaFunction(peak=0, name=None, latex_label=None, unit=None),</strong><br> 'power_law_slope_mass_ratio': Uniform(minimum=-2, maximum=7, name=None, latex_label='$\\beta$', unit=None, boundary=None),<br> 'power_law_slope_m1': Uniform(minimum=1, maximum=6, name=None, latex_label='$\\alpha$', unit=None, boundary=None),<br> 'mu_0_costilt': DeltaFunction(peak=0, name=None, latex_label=None, unit=None),<br> 'mu_1_costilt': DeltaFunction(peak=0.0, name=None, latex_label=None, unit=None),<br> 'sigma_0_costilt': DeltaFunction(peak=1, name=None, latex_label=None, unit=None),<br> 'sigma_1_costilt': DeltaFunction(peak=0.0, name=None, latex_label=None, unit=None),<br> 'peak_branchingratio_m1': Uniform(minimum=0, maximum=0.25, name=None, latex_label='$\\lambda$', unit=None, boundary=None),<br> 'power_law_slope_redshift': Uniform(minimum=-2, maximum=10, name='lamb', latex_label='$\\lambda_z$', unit=None, boundary=None),<br> 'peak_mean_m1': Uniform(minimum=20, maximum=45, name=None, latex_label='$\\mu_{m}$', unit=None, boundary=None),<br> 'peak_sigma_m1': Uniform(minimum=1, maximum=10, name=None, latex_label='$\\sigma_{m}$', unit=None, boundary=None),<br> 'minmass_m1': Uniform(minimum=2, maximum=10, name=None, latex_label='$m_{\\min}$', unit=None, boundary=None),<br> 'maxmass_m1': Uniform(minimum=60, maximum=100, name=None, latex_label='$m_{\\max}$', unit=None, boundary=None),<br> 'low_end_smoothing_m1': Uniform(minimum=0, maximum=10, name=None, latex_label='$\\delta_{m}$', unit=None, boundary=None)} Drop me (Salvatore Vitale) an email if anything doesn't work or if you spot issues. Thanks!
description: {"references": ["https://arxiv.org/abs/2209.06978"]}