Applying Topological Uncertainty to hidden-layer activations of a trained FNN detects failed neutron-star EoS inferences with over 90% success in the best-tested configuration.
Reconstructing the neutron star equation of state from observational data via automatic differentiation
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abstract
Neutron star observables like masses, radii, and tidal deformability are direct probes to the dense matter equation of state~(EoS). A novel deep learning method that optimizes an EoS in the automatic differentiation framework of solving inverse problems is presented. The trained neural network EoS yields narrow bands for the relationship between the pressure and speed of sound as a function of the mass density. The results are consistent with those obtained from conventional approaches and the observational bound on the tidal deformability inferred from the gravitational wave event, GW170817.
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Topological Uncertainty for Anomaly Detection in the Neural-network EoS Inference with Neutron Star Data
Applying Topological Uncertainty to hidden-layer activations of a trained FNN detects failed neutron-star EoS inferences with over 90% success in the best-tested configuration.