Davide Gerosa

Caltech

Impact of Bayesian priors on the characterization of binary black hole coalescences

Salvatore Vitale, Davide Gerosa, Carl-Johan Haster, Katerina Chatziioannou, Aaron Zimmerman.
Physical Review Letters 119 (2017) 251103.
arXiv:1707.04637 [gr-qc].

Bayesian statistics is really cool. It lets you specify clearly and openly all the assumptions that enter an analysis. What’s the effect of these prior assumptions on current inference with gravitational-wave data from black-hole binaries? Here we tackle this question head-on, and perform parameter estimation runs on LIGO data with many (astrophysically motivated!) prior assumptions. Some parameters (like chirp mass) do not suffer from prior choices but others (like the effective spin) do! Specifying the astrophysics as priors is a powerful tool to extract more science from GW data

Posterior sample data release.

Update: at the time of publication, this was the first independent reanalysis of any GW data! (There are many more now…). Also, use our data for your research!

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