Revolutionizing Neutron Star Parameter Inference through Machine Learning

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Observations of neutron stars provide estimates of their mass and radius-key parameters for constraining their still uncertain equation of state. However, the accuracy of parameter inference is limited by the complexity and computational cost of current models, with more accurate models becoming prohibitively expensive. To make parameter inference feasible, we have developed a transposed convolutional neural network (NN) that serves as a surrogate for the physics-based model within our MCMC algorithm. We test and validate this approach in a simple sta...