http://arxiv.org/abs/1711.06748
We discuss a methodology of the machine learning to deduce the neutron star equation of state from a set of mass-radius observational data. We propose an efficient procedure to deal with a mapping from finite data points with observational errors onto an equation of state. We generate training data and optimize the neural network. Using independent validation data (mock observational data) we confirm that the equation of state is correctly reconstructed with precision surpassing observational errors.
Y. Fujimoto, K. Fukushima and K. Murase
Tue, 21 Nov 17
8/79
Comments: 5 pages, 4 figures