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

arXiv: https://arxiv.org/abs/2210.08382 · HTML: https://mlsanity.com/paper/2210.08382/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 54 references that are in the map.
- First posted on arXiv 15 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Real-time gravitational-wave science with neural posterior estimation](https://mlsanity.com/paper/2106.12594/) (2021)
- [Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy](https://mlsanity.com/paper/1909.06296/) (2019)
- [Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data](https://mlsanity.com/paper/1711.03121/) (2017)
- [Deep Neural Networks to Enable Real-time Multimessenger Astrophysics](https://mlsanity.com/paper/1701.00008/) (2016)
- [Gravitational-wave parameter estimation with autoregressive neural network flows](https://mlsanity.com/paper/2002.07656/) (2020)
- [Learning Bayesian posteriors with neural networks for gravitational-wave inference](https://mlsanity.com/paper/1909.05966/) (2019)
- [Enabling real-time multi-messenger astrophysics discoveries with deep learning](https://mlsanity.com/paper/1911.11779/) (2019)
- [Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning](https://mlsanity.com/paper/2009.07367/) (2020)
- [Identification of Binary Neutron Star Mergers in Gravitational-Wave Data Using YOLO One-Shot Object Detection](https://mlsanity.com/paper/2207.00591/) (2022)

## Abstract (arXiv)

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 long-range 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 the tidal deformability parameter for each EOS class it is shown that the AST model achieves a promising performance in correctly classifying the EOS purely from the gravitational wave signals, especially when the component masses of the binary system are in the range [1,1.5]Modot. Furthermore, the generalization ability of the model is investigated by using gravitational-wave signals from a new EOS not used during the training of the model, achieving fairly satisfactory results. Overall, the results, obtained using the simplified setup of noise-free waveforms, show that the AST model, once trained, might allow for the instantaneous inference of the cold nuclear matter EOS directly from the inspiral gravitational-wave signals produced in binary neutron star coalescences.