Related papers: Decoding Neutron Star Observations: Revealing Comp…
This study is devoted to the inference problem of extracting the nuclear matter properties directly from a set of mass-radius observations. We employ Bayesian neural networks (BNNs), which is a probabilistic model capable of estimating the…
Besides their intrinsic nuclear-structure value, nuclear mass models are essential for astrophysical applications, such as r-process nucleosynthesis and neutron-star structure. To overcome the intrinsic limitations of existing…
We present a pipeline to infer the equation of state of neutron stars from observations based on deep neural networks. In particular, using the standard (deterministic), as well as Bayesian (probabilistic) deep networks, we explore how one…
We develop a new nonparametric method to reconstruct the Equation of State (EoS) of Neutron Star with multimessenger data. As an universal function approximator, the Feed-Forward Neural Network (FFNN) with one hidden layer and a sigmoidal…
We present a physics-informed Bayesian neural-network framework to infer neutron-star equations of state from theoretical priors and to propagate the associated uncertainties to stellar observables. Trained on a large and representative…
The radio astronomy community is rapidly adopting deep learning techniques to deal with the huge data volumes expected from the next generation of radio observatories. Bayesian neural networks (BNNs) provide a principled way to model…
Measuring the structural parameters (size, total brightness, light concentration, etc.) of galaxies is a significant first step towards a quantitative description of different galaxy populations. In this work, we demonstrate that a Bayesian…
The high-density behavior of nuclear matter is analyzed within a relativistic mean-field description with non-linear meson interactions. To assess the model parameters and their output, a Bayesian inference technique is used. The Bayesian…
Information on the phase structure of strongly interacting matter at high baryon densities can be gained from observations of neutron stars and their detailed analysis. In the present work Bayesian inference methods are used to set…
The difficulty in describing the equation of state (EoS) for nuclear matter at densities above the saturation density ($\rho_0$) has led to the emergence of a multitude of models based on different assumptions and techniques. These EoSs,…
Bayesian neural networks (BNNs) augment deep networks with uncertainty quantification by Bayesian treatment of the network weights. However, such models face the challenge of Bayesian inference in a high-dimensional and usually…
A Bayesian method is used in this extensive work to generate a large set of minimally constrained equations of state (EOSs) for matters in neutron stars (NS). These EOSs are analyzed for their correlations with key NS properties, such as…
Functional forms of the neutron star Equation of State (EoS) are required to extract the viable EoS band from neutron star observations. Realistic nuclear EoS, containing deconfined quarks or hyperons, present nontrivial features in the…
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…
Scientific machine learning has been successfully applied to inverse problems and PDE discovery in computational physics. One caveat concerning current methods is the need for large amounts of ("clean") data, in order to characterize the…
In this work, we compare two powerful parameter estimation methods namely Bayesian inference and Neural Network based learning to study the quark matter equation of state with constant speed of sound parametrization and the structure of the…
Bayesian Neural Networks (BNNs) provide a tool to estimate the uncertainty of a neural network by considering a distribution over weights and sampling different models for each input. In this paper, we propose a method for uncertainty…
In this work we present general predictions for the static observables of neutron stars (NSs) under the hypothesis of a purely nucleonic composition of the ultra-dense baryonic matter, using Bayesian inference on a very large parameter…
Deep Neural Networks (DNNs) are powerful tools for various computer vision tasks, yet they often struggle with reliable uncertainty quantification - a critical requirement for real-world applications. Bayesian Neural Networks (BNN) are…
The general behavior of the nuclear equation of state (EOS), relevant for the description of neutron stars (NS), is studied within a Bayesian approach applied to a set of models based on a density dependent relativistic mean field…