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Bayesian inference of massive black hole binaries formation and evolution scenarios with gravitational waves


The massive black holes (MBHs) that we currently observe in the center of galaxies are the final stage of a complex evolutionary path in which seed BHs at high redshift grow in proto-galaxies through episodes of merger and accretion.
If MBHs are brought sufficiently close during a galaxy collision, they can merge, emitting gravitational waves (GWs). In 2034, the Laser Interferometer Space Antenna (LISA) will observe the GWs from merging MBHBs across the entire Universe, providing exquisite estimates of the binary parameters. As astrophysical processes, such as gas accretion, supernova feedback and dynamics, leave an imprint on the MBHBs masses, merging redshift, spins and eccentricity distributions, detecting MBHBs will provide us indirectly information on MBHBs formation and evolution scenarios.
The scope of the project is to asses LISA ability to constrain the MBHBs astrophysical and formation mechanisms, combining state-of-the-art simulations to predict the MBHBs population with reverse Gaussian process and hierarchical Bayesian analysis.
We plan to implement a semi-analytic model (SAM) to describe the complex processes shaping the MBHBs distribution and to model astrophysical processes and formation scenarios with population hyperparameters. We will construct a grid in the hyperparameter space where each node will correspond to a realistic population of MBHBs. These points will serve as a template bank for a Gaussian Process interpolator, which in turn will be the backbone of a hierarchical Bayesian inference framework. This framework will be employed to extract the posterior distributions of the population hyperparameters with the final goal of inferring the contribution of different astrophysical processes to the MBHBs population.


Net EU contribution
€ 195 914,88
Rue Michel Ange 3
75794 Paris

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Ile-de-France Ile-de-France Paris
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Research Organisations
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