相关论文: Insights into neutron star equation of state by ma…
Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of…
The composition and properties of infinite nuclear matter under extreme conditions of temperature and pressure remain incompletely understood. In this work, we constrain the equation of state (EoS) of nuclear matter - constructed within the…
Relativistic mean-field models (RMF) based on the exchange of $\sigma$, $\omega$, and $\rho$ mesons including non-linear nucleon-$\sigma$ couplings and density-dependent $\rho$ coupling, are considered. A large set of models is generated…
We study an alternative use of machine learning. We train neural nets to provide the parameter estimate of a given (structural) econometric model, for example, discrete choice or consumer search. Training examples consist of datasets…
Neutron stars (NS) are compact objects with strong gravitational fields, and a matter composition subject to extreme physical conditions. The properties of strongly interacting matter at ultra-high densities and temperatures impose a big…
We use an effective quark model to describe both hadronic matter and deconfined quark matter. By calculating the equations of state and the corresponding neutron star properties, we show that the internal properties of the nucleon have…
The density dependencies of various effective interaction strengths in the relativistic mean field are studied and carefully compared for nuclear matter and neutron stars. The influences of different density dependencies are presented and…
Molecular modeling is an important topic in drug discovery. Decades of research have led to the development of high quality scalable molecular force fields. In this paper, we show that neural networks can be used to train a universal…
The main goal of the present contribution is a pedagogical introduction to the fascinating world of neutron stars by relying on relativistic density functional theory. Density functional theory provides a powerful--and perhaps…
One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of…
Neural networks (NNs) are employed to predict equations of state from a given isotropic pair potential using the virial expansion of the pressure. The NNs are trained with data from molecular dynamics simulations of monoatomic gases and…
We report neutron star predictions based on our most recent equations of state. These are derived from chiral effective field theory, which allows for a systematic development of nuclear forces, order by order. We utilize high-quality…
Characterization of quantum systems from experimental data is a central problem in quantum science and technology. But which measurements should be used to gather data in the first place? While optimal measurement choices can be worked out…
After giving an overview of the history and idea of neutron stars, I shall introduce, in part one of my lectures, a representative collection of models for the equation of state of superdense neutron star matter, which account for various…
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…
Deep neural networks (DNNs) are powerful machine learning models and have succeeded in various artificial intelligence tasks. Although various architectures and modules for the DNNs have been proposed, selecting and designing the…
Machine Learning is an efficient method for analyzing and interpreting the increasing amount of astronomical data that is available. In this study, we show, a pedagogical approach that should benefit anyone willing to experiment with Deep…
We explore unique considerations involved in fitting ML models to data with very high precision, as is often required for science applications. We empirically compare various function approximation methods and study how they scale with…
The nuclear symmetry energy plays important role on the structure of finite nuclei as well as on the bulk properties of neutron stars. However, its values at high densities are completely uncertain and the corresponding experimental data…
The equation of state of the neutron-star outer crust is sensitive to nuclear mass predictions and provides a direct connection to properties of nuclei throughout the nuclide map, including those beyond experimental reach. We quantify the…