Davide Gerosa

University of Birmingham


Machine-learning interpolation of population-synthesis simulations to interpret gravitational-wave observations: a case study

Gravitational-wave astronomy is, seems obvious to say, about doing astronomy with gravitational waves. One has gravitational-wave observations (thanks LIGO and Virgo!) on hand and astrophysical models on the other hand. The more closely these two sides interact, the more we can hope to use gravitational-wave data to learn about the astrophysics of the sources. Today’s paper with JHU student Kaze Wong tries to further stimulate this dialog. And, well, one needs to throw some artificial intelligence in the game. There are three players now (astrophysics, gravitational waves, and machine learning) and things get even more interesting.

Kaze W.K. Wong, Davide Gerosa.
arXiv:1909.06373 [astro-ph.HE].


Black holes in the low mass gap: Implications for gravitational wave observations

What’s in between neutron stars and black holes? It looks like neutron stars have a maximum mass of about 2 solar masses while black holes have a minimum mass of about 5. So what’s in between? That’s the popular issue of the ‘low mass gap’. Actually, now we know something must be in there. LIGO and Virgo have seen GW170817, a merger of two neutron stars, which merged in to a black hole with the right mass to populate the gap. Can this population be seen directly with (future) gravitational-wave detectors? That’s today’s paper.

Anuradha Gupta, Davide Gerosa, K. G. Arun, Emanuele Berti, B. S. Sathyaprakash
arXiv:1909.05804 [gr-qc].