Statistically-informed deep learning for gravitational wave parameter estimation

Abstract

We introduce deep learning models to estimate the masses of the binary components of black hole mergers, (m_1,m_2), and three astrophysical properties of the post-merger compact remnant, namely, the final spin, a_f, and the frequency and damping time of the ringdown oscillations of the fundamental =m=2 bar mode, (_R, _I). Our neural networks combine a modified WaveNet architecture with contrastive learning and normalizing flow. We validate these models against a Gaussian conjugate prior family whose posterior distribution is described by a closed analytical expression. Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters (m_1,m_2, a_f, _R, _I) of five binary black holes: GW150914, GW170104, GW170814, GW190521 and GW190630. We use PyCBC Inference to directly compare traditional Bayesian methodologies for parameter estimation with our deep-learning-based posterior distributions. Our results show that our neural network models predict posterior distributions that encode physical correlations, and that our data-driven median results and 90\% confidence intervals are similar to those produced with gravitational wave Bayesian analyses. This methodology requires a single V100 NVIDIA GPU to produce median values and posterior distributions within two milliseconds for each event. This neural network, and a tutorial for its use, are available at the Data and Learning Hub for Science.

Tasks

Reproductions