Deep Learning at Scale for Gravitational Wave Parameter Estimation of Binary Black Hole Mergers

This first application of deep learning at scale to do gravitational wave parameter estimation of binary black hole mergers that describe a 4-D signal manifold, i.e., black holes whose spins are aligned or anti-aligned, and which evolve on quasi-circular orbits is presented.