arXiv subjects

Emanuel V. Chimanski

Publications and source records attributed to Emanuel V. Chimanski.

3 recordsLinked to original sources

arXiv2022

Bayesian Inference of Phenomenological EoS of Neutron Stars with Recent Observations

The description of stellar interior remains as a big challenge for the nuclear astrophysics community. The consolidated knowledge is restricted to density regions around the saturation of hadronic matter $ρ_{0} = 2.8\times 10^{14} {\rm\ g\ cm^{-3}}$, regimes where our nuclear models are successfully applied. As one moves towards higher densities and extreme conditions up to five to twenty times $ρ_{0}$, little can be said about the microphysics of such objects. Here, we employ a Markov Chain Monte Carlo (MCMC) strategy to access the variability of polytropic three-pircewised models for neutron star equation of state. With a fixed description of the hadronic matter, we explore a variety of models for the high density regimes leading to stellar masses up to $2.5\ M_{\odot}$. In addition, we also discuss the use of a Bayesian power regression model with heteroscedastic error. The set of EoS from the Laser Interferometer Gravitational-Wave Observatory (LIGO) was used as inputs and treated as data set for testing case.

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arXiv2022

Towards a Predictive HFB+QRPA Framework for Deformed Nuclei: Selected Tools and Technique

Reliable predictions of the static and dynamic properties of a nucleus require a fully microscopic description of both ground and excited states of this complicated many-body quantum system. Predictive calculations are key to understanding such systems and are important ingredients for simulating stellar environments and for enabling a variety of contemporary nuclear applications. Challenges that theory has to address include accounting for nuclear deformation and the ability to describe medium-mass and heavy nuclei. Here, we perform a study of nuclear states in an Hartree-Fock-Bogoliubov (HFB) and Quasiparticle Random Phase Approximation (QRPA) framework that utilizes an axially-symmetric deformed basis. We present some useful techniques for testing the consistency of such calculations and for interpreting the results.

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arXiv2022

Unsupervised machine learning correlations in EoS of neutron stars

Neutron stars are compact objects of large interest in the nuclear astrophysics community. The extreme conditions present in such systems impose big challenges to our current microscopic models of nuclear structure. Equation of states (EoS) are frequently derived from sophisticated quantum mechanical models, such as: relativistic, non-relativistic and many mean-field approaches. Every single model, in general, contains many parameters such as the NN interaction strength, particle compositions, etc. These are particular features of each model and can be represented by numbers and categories in a machine learning context. Different choices of features will affect EoS properties leading to different macroscopic properties of the star. In this work we analyze a selection of EoS containing a variety of different physics models. One of our objectives is to develop tools that enable a better understanding of the correlations among the different model features and the outcome produced by them when employed to model neutron stars.

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