Reconstruction of fast-rotating neutron star observables with the neural network
This paper introduces a causal convolutional neural network trained on RNS-generated data to rapidly and accurately reconstruct observables for fast-rotating neutron stars, reducing computation time from approximately 30 minutes to 50 milliseconds per configuration and enabling efficient inference analyses that were previously hindered by the high cost of traditional two-dimensional modeling.