Detecting hyperons in neutron stars: A machine learning approach

被引：0

作者：

Carvalho, Valeria ^{[1
]}

Ferreira, Marcio ^{[1
]}

Providencia, Constanca ^{[1
]}

机构：

[1] Univ Coimbra, Dept Phys, CFisUC, P-3004516 Coimbra, Portugal

来源：

PHYSICAL REVIEW D | 2024年 / 110卷 / 12期

关键词：

EQUATION-OF-STATE; DENSE MATTER; GEOMETRIC MEASUREMENTS; SYMMETRY ENERGY; MASS; INFERENCE; FIELD; RADIUS; CORES;

D O I：

10.1103/PhysRevD.110.123016

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

We present a neural network classification model for detecting the presence of hyperonic degrees of freedom in neutron stars. The models take radii and/or tidal deformabilities as input and give the probability for the presence of hyperons in the neutron star composition. Different numbers of observations and different levels of uncertainty in the neutron star properties are tested. The models have been trained on a dataset of well-calibrated microscopic equations of state of neutron star matter based on a relativistic meanfield formalism. Real data and data generated from a different description of hyperonic matter are used to test the performance of the models.

引用

页数：15

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