Adapting to noise distribution shifts in flow-based gravitational-wave inference

Deep learning techniques for gravitational-wave parameter estimation have emerged as a fast alternative to standard samplers-producing results of comparable accuracy. These approaches (e.g., DINGO) enable amortized inference by training a normalizing flow to represent the Bayesian posterior conditional on observed data. By conditioning also on the noise power spectral density (PSD) they can even account for changing detector characteristics. However, training such networks requires knowing in advance the distribution of PSDs expected to be observed and therefore can only take place once all data to be analyzed have been gathered. Here, we develop a probabilistic model to forecast future PSDs, greatly increasing the temporal scope of trained deep learning models. Using PSDs from the second LIGO-Virgo observing run (O2)-plus just a single PSD from the beginning of the third (O3)-we show that we can train a DINGO network to perform accurate inference throughout O3 (on 37 real events). We therefore expect this approach to be a key component to enable the use of deep learning techniques for low-latency analyses of gravitational waves.

Automated summary

Deep learning techniques for gravitational-wave parameter estimation provide a fast alternative to traditional samplers with comparable accuracy. By training a normalizing flow model, these approaches, such as DINGO, can represent the Bayesian posterior conditional on observed data and account for changing detector characteristics by conditioning on the noise power spectral density. This study develops a probabilistic model to forecast future noise power spectral densities, allowing the training of deep learning models with a longer temporal scope and enabling accurate inference throughout the third LIGO-Virgo observing run (O3) using a DINGO network.