Classification of the equation of state of neutron stars via sparse dictionary learning

TL;DR

This paper demonstrates that sparse dictionary learning can classify neutron star equations of state from post-merger gravitational-wave signals, with high accuracy at realistic detector sensitivities, aiding future astrophysical insights.

Contribution

It introduces a novel application of sparse dictionary learning to classify neutron star EOS models using simulated post-merger gravitational-wave data.

Findings

Classification accuracy improves with higher SNR.

Dominant post-merger frequency $f_2$ is key for EOS classification.

Method performs well even with EOS outside training set.

Abstract

The post-merger phase of binary neutron star (BNS) mergers encodes valuable information about the equation of state (EOS) of supranuclear matter. Extracting this information from the analysis of the post-merger waveforms remains challenging due to the high-frequency limitations of current detectors. Future third-generation observatories, such as the Einstein Telescope (ET) and NEMO, will have the sensitivity required to resolve post-merger signals with high fidelity. In this work, we apply CLAWDIA, our recently developed sparse dictionary learning (SDL) framework, to classify different EOS models using only the post-merger gravitational-wave emission of simulated BNS mergers available in the CoRe database. Our dataset comprises five EOS models representative of a broad range of neutron star properties. The SDL framework is optimised under realistic detection conditions by injecting signals into simulated noise matching the sensitivity curves of ET and NEMO. Our results show that classification is primarily driven by the dominant post-merger frequency, $f_2$, which encodes EOS-dependent information. At a modest signal-to-noise ratio of 5, our method achieves $F_1$ scores of $0.76$ for ET and $0.70$ for NEMO, with performance improving for higher signal-to-noise ratios. The reliability and generalisation capabilities of the model are assessed with additional tests, including the classification of an EOS not included in the training dataset and the analysis of detector-specific biases.