Davide Gerosa

Deep learning and Bayesian inference of gravitational-wave populations: hierarchical black-hole mergers

It took a while (so many technical challenges…) but we made it! Matt‘s monster paper is finally out!

Let me introduce a fully-fledged pipeline to study populations of gravitational-wave events with deep learning. If it sounds cool, well, it is cool (just look at the flowchart in Figure 1!). We can now perform a hierarchical Bayesian analysis on GW data but, unlike current state-of-the-art applications that rely on simple functional form, we can use populations inferred from numerical simulations. This might sound like a detail but it’s not: it’s necessary to compare GW data directly against stellar physics. While we don’t do that yet here (our simulations are admittedly too simple), there’s a ton of astrophysics already in this paper. Whether you care about neural networks or hierarchical black-hole mergers (or, why not, both!), sit tight, fasten your seatbelt, and read Matt’s paper.

Matthew Mould, Davide Gerosa, Stephen R. Taylor.
Physical Review D 106 (2022) 103013.
arXiv:2203.03651 [astro-ph.HE].

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