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Neural Networks Reconstruction of the Dense-matter Equation of State from Neutron-star Parameters by Morawski, Filip; Bejger, Michał is a scholarly article available to read on EtoBox.

What is Neural Networks Reconstruction of the Dense-matter Equation of State from Neutron-star Parameters about?

Aims: The aim of this work is to study the application of the artificial neural networks guided by the autoencoder architecture as a method for precise reconstruction of the neutron star equation of state, using their observable parameters: masses, radii and tidal deformabilities. In addition we study how well the neutron star radius can be reconstructed using the gravitational-wave only observations of tidal deformability, i.e. quantities which are not related in a straightforward way. Methods: Application of artificial neural network in the equation of state reconstruction exploits the non-linear potential of this machine learning model. Since each neuron in the network is basically a non-linear function, it is possible to create a complex mapping between the input sets of observations and the output equation of state table. Within the supervised training paradigm, we construct a few hidden layer deep neural network on a generated data set, consisting of a realistic equation of state for the neutron star crust connected with a piecewise relativistic polytropes dense core, with parameters representative to the state-of-the art realistic equations of state. Results: We demonstrate

Author

Morawski, Filip; Bejger, Michał

Published

2020

Language

EN