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

<p>This is the data release associated with <strong>Vitale et al <a href="https://arxiv.org/abs/2209.06978">2209.06978</a></strong></p> <p><strong>Samples.zip: </strong>Contains all of the hyper posterior samples for the runs listed in Tables G.1.</p> <p>The files are in json format. Bilby offers a dedicated routine to read them in</p> <p>&nbsp;</p> <blockquote> <p>import bilby<br> data= bilby.core.result.read_in_result(path_to_json)</p> </blockquote> <p>&nbsp;</p> <p>See the <a href="https://lscsoft.docs.ligo.org/bilby/">Bilby documentation </a>for what is contained in the result object.&nbsp;</p> <p>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]</p> <p>Here the name used to store and&nbsp;a short description of each parameter (Follow the references in the Method section of the paper for a description of each sub-model):</p> <ol> <li>Primary mass model (Power Law + Peak for all runs) <ol> <li>power_law_slope_m1, slope of the primary mass power law component</li> <li>minmass_m1, minimum BH mass</li> <li>maxmass_m1, maximum BH mass</li> <li>low_end_smoothing_m1, smoothing at the low-mass end</li> <li>peak_branchingratio_m1, branching ratio between Gaussian peak and power law (1= 100% peak)</li> <li>peak_mean_m1, mean of the Gaussian peak</li> <li>peak_sigma_m1, sigma of the Gaussian peak&nbsp;</li> </ol> </li> <li>Mass ratio model (power law&nbsp;for all runs) <ol> <li>power_law_slope_mass_ratio, slope of the mass ratio&nbsp;</li> </ol> </li> <li>Redshift (power law for all runs) <ol> <li>power_law_slope_redshift, slope of the redshift</li> </ol> </li> <li>Spin magnitude (IID beta distributions for all runs) <ol> <li>alpha_chi, first argument of beta distribution</li> <li>beta_chi, second argument of beta distribution</li> </ol> </li> <li>Cosine of tilt angle <ol> <li>Gaussian models <ol> <li>mu_0_costilt, for Gaussian models w/o correlation, the mean of the left (or only) Gaussian</li> <li>sigma_0_costilt, for Gaussian models w/o correlation,&nbsp;the sigma of the left (or only) Gaussian</li> <li>mu_1_costilt, for Gaussian models w/o correlation, the mean of the right Gaussian</li> <li>sigma_1_costilt, for Gaussian models w/o correlation, the sigma of the right&nbsp;Gaussian</li> <li>mu_a_costilt, for Gaussian model with correlation,&nbsp;the constant part of the Gaussian mean</li> <li>mu_b_costilt, for Gaussian model with correlation,&nbsp;the coefficient of the linearly&nbsp;evolving part of the Gaussian mean</li> <li>sigma_a_costilt, for Gaussian model with correlation,&nbsp;the constant part of the Gaussian sigma</li> <li>sigma_b_costilt, for Gaussian model with correlation,&nbsp;the coefficient of the linearly&nbsp;evolving part of the Gaussian sigma</li> </ol> </li> <li>Beta models <ol> <li>alpha_a_costilt, for all Beta models, the&nbsp;constant part of the first parameter of the Beta distribution</li> <li>alpha_b_costilt, for all Beta models, the coefficient of the linearly&nbsp;evolving part of the first parameter of the Beta distribution</li> <li>beta_a_costilt, for all Beta models, the&nbsp;constant part of the second parameter of the Beta distribution</li> <li>beta_b_costilt, for all Beta models, the coefficient of the linearly&nbsp;evolving part of the second parameter of the Beta distribution</li> </ol> </li> <li>Tukey models: <ol> <li>tukey_x0, the center of the Tukey as defined in appendix E of the paper</li> <li>tukey_k, Tk as defined in appendix E of the paper</li> <li>tukey_r, Tk as defined in appendix E of the paper</li> </ol> </li> <li>Branching ratios: <ol> <li>spin_mixture_0, for 2-component models, this is the branching ratio of the non-isotropic component</li> <li>spin_mixture_1, for Isotropic + Gaussian + Tukey and Isotropic + Gaussian + Beta this is the branching ratio of the <strong>Gaussian</strong> component;&nbsp;for Isotropic + 2 Gaussian this is the branching ratio of the <strong>Gaussian on the right.</strong></li> </ol> </li> </ol> </li> <li>Merger rate <ol> <li>rates, merger rate per unit Gpc cubed per unit year</li> </ol> </li> </ol> <p>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&nbsp;&nbsp;</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)&nbsp;</p> <blockquote> <p>&gt; data[&#39;prior&#39;]<br> &nbsp;[...]<br> <strong>&nbsp;&#39;spin_mixture_0&#39;: DeltaFunction(peak=0, name=None, latex_label=None, unit=None),</strong><br> &nbsp;</p> </blockquote> <p>&nbsp;</p> <p><strong>Figures.zip:</strong> Contains PDFs for all figures in the paper, plus individual figures for p(costau) and dR/dcostau for each model.</p> <p>Drop me (Salvatore Vitale) an email if anything doesn&#39;t work, is missing, or if you spot issues. Thanks!&nbsp;</p> <p>&nbsp;</p>