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 $\ell =m=2$ bar mode, $({\omega }_R,{\omega }_I)$. Our neural networks combine a modified $\text{WaveNet}$ 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 $(m_1,m_2,a_f,{\omega }_R,{\omega }_I)$ of five binary black holes: $\text{GW150914},\text{GW170104},\text{GW170814},\text{GW190521}$ and $\text{GW190630}$. We use $\text{PyCBC Inference}$ 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 $\text{NVIDIA}$ 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 $\text{Data and Learning Hub for Science}$.

arXiv commentv4: 13 pages, 6 figures, First application of Neural Networks for gravitational wave parameter posterior estimation across multiple events with single training

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Cited by in corpus (1)

• Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources