Measuring the cosmic expansion by calibrating the source mass of binary black holes is one of our main activities. Yet, it is super difficult as we do not know how to model the mass of these black holes.
In her master’s thesis, Arianna Scarpa came up with a method to use simulated catalogues of binary black holes to calibrate gravitational wave cosmology. The basic idea is to train a normalizing flow, a type of machine learning, to mimic the complex distribution of binary black hole sources reported inside the simulated catalogues.
Arianna also showed that this methodology can be used to infer the relative mixture coefficients between different formation channels, such as black holes formed from isolated binaries and black holes formed from dynamical assembly. Of course, there is still work to be done; mock catalogues of binary black holes also have their uncertainties that should be included.
Feel free to read our latest paper and check the software release to use normalizing flows for gravitational wave cosmology!
Figure: Joint posteriors on the Hubble constant and the mixture fraction between two populations of binary black mergers for simulated data and GWTC-4 data.


