Bajes: Bayesian inference of multimessenger astrophysical data, methods and application to gravitational-waves

We release the posterior samples (and the related configuration files for reproducibility) coming from the analyses of gravitational-wave (GW) triggers presented in GWTC-1 [1] estimated with TEOBResumS model [2] using the Bajes pipeline [3].

Bajes [baɪɛs] is a Python package developed at Friedrich-Schiller-Universtaet Jena that aims to provide a simple, complete and reliableimplementation capable to robustly perform Bayesian inference on arbitrary sets of data, with specific functionalities for multimessenger astrophysics.

The Bajes software can be downloaded from Github and installed using the standard Python setuptools routine (see documentation).

The presented data contain .zip repositories for every analyzed GW events. Each repository stores the following objects:

• config.ini : the configuration file employed to execute the job;
• posterior.dat : ASCII file containing the posterior samples (sorted by increasing likelihood), with the following columns:
  • m_chirp : chirp mass, detector-frame [solar masses]
  • m_ratio : mass ratio [greater or equal to 1]
  • m_chirp_source : chirp mass, source-frame [solar masses]
  • m_1_source : primary mass component, source-frame [solar masses]
  • m_2_source : secondary mass component, source-frame [solar masses]
  • s_1_z : primary (dimensionless) spin z-component (aligned to orbital angular momentum)
  • s_2_z : secondary (dimensionless) spin z-component (aligned to orbital angular momentum)
  • chi_eff : effective spin parameter 
  • lum_distance : luminosity distance [Mpc]
  • inclination : inclination angle [rad]
  • right_ascension : right ascension angle [rad]
  • declination : declination angle [rad]
  • Two additional columns are included for GW170817 with the tidal deformabilities, lambda_1 and lambda_2

[1] LIGO Scientific and Virgo Collaboration, GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs, Phys.Rev.X 9 (2019) 3, 031040, arXiv:1811.12907 [astro-ph.HE]

[2] A. Nagar et al., Time-domain effective-one-body gravitational waveforms for coalescing compact binaries with nonprecessing spins, tides and self-spin effects, Phys.Rev.D 98 (2018) 10, 104052, arXiv:1806.01772 [gr-qc]

[3] M. Breschi et al., Bajes: Bayesian inference of multimessenger astrophysical data, methods and application to gravitational-waves, related paper (2021)