Extensive Studies of the Neutron Star Equation of State from the Deep Learning Inference with the Observational Data Augmentation

TL;DR

This paper applies deep learning to infer the neutron star equation of state from observational data, demonstrating that data augmentation improves model robustness and reduces overfitting compared to traditional methods.

Contribution

It introduces a deep learning approach with observational data augmentation for neutron star EoS inference, showing improved accuracy and overfitting mitigation.

Findings

Deep learning outperforms polynomial regression in EoS modeling.

Data augmentation helps prevent overfitting in neural network training.

The inferred EoS allows for possible weak first-order phase transitions.

Abstract

We discuss deep learning inference for the neutron star equation of state (EoS) using the real observational data of the mass and the radius. We make a quantitative comparison between the conventional polynomial regression and the neural network approach for the EoS parametrization. For our deep learning method to incorporate uncertainties in observation, we augment the training data with noise fluctuations corresponding to observational uncertainties. Deduced EoSs can accommodate a weak first-order phase transition, and we make a histogram for likely first-order regions. We also find that our observational data augmentation has a byproduct to tame the overfitting behavior. To check the performance improved by the data augmentation, we set up a toy model as the simplest inference problem to recover a double-peaked function and monitor the validation loss. We conclude that the data augmentation could be a useful technique to evade the overfitting without tuning the neural network architecture such as inserting the dropout.