English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The use of the Audio Spectrogram Transformer (AST) model forgravitational-wave data analysis is investigated. The AST machine-learningmodel is a convolution-free classifier that captures long-range globaldependencies through a purely attention-based mechanism. In this paper a modelis applied to a simulated dataset of inspiral gravitational wave signals frombinary neutron star coalescences, built from five distinct, cold equations ofstate (EOS) of nuclear matter. From the analysis of the mass dependence of thetidal deformability parameter for each EOS class it is shown that the AST modelachieves a promising performance in correctly classifying the EOS purely fromthe gravitational wave signals, especially when the component masses of thebinary system are in the range $[1,1.5]M_{\odot}$. Furthermore, thegeneralization ability of the model is investigated by using gravitational-wavesignals from a new EOS not used during the training of the model, achievingfairly satisfactory results. Overall, the results, obtained using thesimplified setup of noise-free waveforms, show that the AST model, oncetrained, might allow for the instantaneous inference of the cold nuclear matterEOS directly from the inspiral gravitational-wave signals produced in binaryneutron star coalescences.

Machine-Learning Love: classifying the equation of state of neutron stars with Transformers