Statistically-informed deep learning for gravitational wave parameter estimation
This work provides a faster alternative to Bayesian parameter estimation for gravitational wave astronomy, though it is incremental as it builds on existing methods.
The paper tackles the problem of estimating astrophysical parameters from gravitational wave signals using deep learning, achieving results comparable to traditional Bayesian methods with predictions generated within two milliseconds per event on a single GPU.
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, $(ω_R, ω_I)$. Our neural networks combine a modified $\texttt{WaveNet}$ 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 $(m_1,m_2, a_f, ω_R, ω_I)$ of five binary black holes: $\texttt{GW150914}, \texttt{GW170104}, \texttt{GW170814}, \texttt{GW190521}$ and $\texttt{GW190630}$. We use $\texttt{PyCBC Inference}$ 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 $\texttt{NVIDIA}$ 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 $\texttt{Data and Learning Hub for Science}$.