Scientific Seminars

Gravitational-wave data exploitation with deep learning

Davide Gerosa
Universita' di Milano - Bicocca

2023-01-16    14:30    Dip. di Fisica, UNIMI, Via Celoria - Aula Polvani

The catalog of gravitational-wave events is growing, and so are our hopes of constraining the underlying astrophysics of stellar-mass black-hole mergers by inferring the distributions of, e.g., masses and spins. While conventional analyses parametrize this population with simple phenomenological models, we propose an innovative physics-first approach that compares gravitational-wave data against astrophysical simulations. We combine state-of-the-art deep- learning techniques with hierarchical Bayesian inference and exploit our approach to constrain the properties of repeated black-hole mergers from the gravitational-wave events in the most recent LIGO/Virgo catalog. Deep neural networks allow us to (i) construct a flexible population model that accurately emulates simulations of hierarchical mergers, (ii) estimate selection effects, and (iii) recover the branching ratios of repeated-merger generations. Among our results we find that: the distribution of host-environment escape speeds favors values <100 km s−1 but is relatively flat; first-generation black holes are born with a maximum mass that is compatible with current estimates from pair- instability supernovae; there is multimodal substructure in both the mass and spin distributions due to repeated mergers; and binaries with a higher- generation component make up at least 15% of the underlying population. The deep-learning pipeline we present is ready to be used in conjunction with realistic astrophysical population-synthesis predictions.