Abstract:The use of the Audio Spectrogram Transformer (AST) model for gravitational-wave data analysis is investigated. The AST machine-learning model is a convolution-free classifier that captures longrange global dependencies through a purely attention-based mechanism. In this paper a model is applied to a simulated dataset of inspiral gravitational wave signals from binary neutron star coalescences, built from five distinct, cold equations of state (EOS) of nuclear matter. From the analysis of the mass dependence of… Show more
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