One mixture to rule them all: Bayesian non-parametric methods for gravitational-wave astrophysics
by
Sala Jappelli
Osservatorio Astronomico di Padova
An astrophysical black hole is the compact object left after the explosion of a massive star. Stellar objects undergo several processes during their life and are influenced by the environment in which they are, e.g. dense clusters or isolated binary systems – the so-called formation channels. Similarly, the poorly understood physics regulating mass-loss in stellar winds, or the common envelope evolution, all leave an imprint on the black mass, spin and redshift distribution.
In this talk, I will describe how we can use Bayesian non-parametric methods, which are powerful tools to perform inference without the need to specify a model, to infer the black hole population. In particular, I will present (H)DPGMM, a non-parametric model based on the Dirichlet Process Gaussian Mixture Model, and FIGARO, its implementation.
Using such methods, features in the black hole distribution will arise naturally without the need of including them in the model, leaving astrophysicists tasked with explaining them in terms of formation channels and astrophysical processes.
The seminar will be held on google.meet link:
https://meet.google.com/ezz-irtm-fvg
Oppure digita: (IT) +39 02 3041 9671 PIN: 749 617 084#
Altri numeri di telefono: https://tel.meet/ezz-irtm-fvg?pin=9231790670922
Michela Mapelli