Neural Networks Accelerate Gravitational-Wave Background Inference from Pulsar Timing Arrays

The paper investigates the use of neural networks to replace Gaussian processes in pulsar timing array analyses of the gravitational-wave background. Gaussian processes, while effective, become computationally expensive for large training sets. The study demonstrates that probabilistic neural networks can recover consistent posteriors compared to Gaussian processes while significantly reducing both training and Markov chain Monte Carlo runtime. The largest gains are observed for computationally demanding models, suggesting that neural networks are particularly well-suited for complex analyses. The findings indicate that neural networks offer a promising approach to accelerate the analysis of pulsar timing array data, potentially enabling the detection and characterization of the gravitational-wave background with greater efficiency. The use of machine learning techniques in gravitational-wave astronomy could lead to new discoveries and a deeper understanding of the universe.

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