The spin distribution of binary black hole mergers contains key information concerning the formation channels of these objects , and the astrophysical environments where they form , evolve and coalesce .
To quantify the suitability of deep learning to characterize the signal manifold of quasi-circular , spinning , non-precessing binary black hole mergers , we introduce a modified version of WaveNet trained with a novel optimization scheme that incorporates general relativistic constraints of the spin properties of astrophysical black holes .
The neural network model is trained , validated and tested with 1.5 million \ell = |m| = 2 waveforms generated within the regime of validity of NRHybSur3dq8 , i.e. , mass-ratios q \leq 8 and individual black hole spins \abs { s } ^ { z } _ { \ { 1 , 2 \ } } \leq 0.8 .
To reduce time-to-insight , we deployed a distributed training algorithm at the IBM Power9 Hardware-Accelerated Learning cluster at the National Center for Supercomputing Applications to reduce the training stage from 1 month , using a single V100 NVIDIA GPU , to 12.4 hours using 64 V100 NVIDIA GPUs .
Using this neural network model , we quantify how accurately we can infer the astrophysical parameters of black hole mergers in the absence of noise .
We do this by computing the overlap between waveforms in the testing data set and the corresponding signals whose mass-ratio and individual spins are predicted by our neural network .
We find that the convergence of high performance computing and physics-inspired optimization algorithms enable an accurate reconstruction of the mass-ratio and individual spins of binary black hole mergers across the parameter space under consideration .
This is a significant step towards an informed utilization of physics-inspired deep learning models to reconstruct the spin distribution of binary black hole mergers in realistic detection scenarios .