NUSYM 2024, XIIth International Symposium on Nuclear Symmetry Energy

Session

Combined analysis of nuclear and astrophysics information, Bayesian approach, and machine learning

12 sept. 2024, 14:00

GANIL Guest House (Caen)

Présidents de session

Combined analysis of nuclear and astrophysics information, Bayesian approach, and machine learning

• Arnaud Le Fèvre (GSI Helmholtzzentrum für Schwerionenforschung GmbH)

Combined analysis of nuclear and astrophysics information, Bayesian approach, and machine learning

• David Tsang (University of Bath)

80. The Spirit project

Betty Tsang (NSCL/Michigan State University)

12/09/2024 14:00

Invited Presentation

22. Determination of the Equation of State from Nuclear Experiments and Neutron Star Observations

Chun Yuen Tsang (Brookhaven National Laboratory, Kent State University)

12/09/2024 14:30

Combined analysis of nuclear and astrophysics information, Bayesian approach, and machine learning

Invited Presentation

With recent advances in neutron star observations, major progress has been made in determining the pressure of neutron star matter at high density. This pressure is constrained by the neutron star deformability, determined from gravitational waves emitted in a neutron-star merger, and measurements of radii of two neutron stars, using a new X-ray observatory on the International Space Station....

8. Bayesian model averaging for nuclear symmetry energy from effective proton-neutron chemical potential difference of neutron-rich nuclei

Mengying Qiu (Sun Yat-sen University)

12/09/2024 15:00

Oral Presentation

The data-driven Bayesian model averaging is a rigorous statistical approach to combining multiple models for a unified prediction. Compared with the individual model, it provides more reliable information, especially for problems involving apparent model dependence. In this work, within both the non-relativistic Skyrme energy density functional and the nonlinear relativistic mean field model,...

5. Exploring Composition of Neutron Star Matter with Astrophysical Observations

PRASANTA CHAR (Universidad de Salamanca)

12/09/2024 15:20

Astrophysical multi-messenger observations

Oral Presentation

Astrophysical observations of neutron stars allow us to study the physics of matter at extreme conditions which are beyond the scope of any terrestrial experiments. In this work, we perform a Bayesian analysis putting together the available knowledge from the nuclear physics experiments, observations of different X-ray sources, and gravitational wave events to constrain the equation of state...

15. Nuclear equation of state from nuclear experiments and neutron star observations

M. Pietro Klausner (Laboratoire de Physique Corpusculaire, CNRS, ENSICAEN, UMR6534, Université de Caen Normandie, CEDEX, 14050 Caen, France / Dipartimento di Fisica Aldo Pontremoli, Università degli Studi di Milano, 20133 Milano, Italy / INFN, Sezione di Milano, 20133 Milano, Italy)

12/09/2024 15:40

Combined analysis of nuclear and astrophysics information, Bayesian approach, and machine learning

Oral Presentation

The Equation of State (EoS) is crucial for understanding the structure of compact objects such as neutron stars. In the conservative hypothesis of a purely nucleonic composition of neutron star matter, the EoS is fully determined in terms of the so-called nuclear matter parameters (NMPs), which, in principle, can be determined from nuclear theory and experiments, though with error bars....

45. An overview of existing and new nuclear and astrophysical constraints on the equation of state of neutron-rich dense matter

Dr Peter T. H. Pang (Nikhef / Utrecht University)

13/09/2024 09:00

Invited Presentation

Through continuous progress in nuclear theory and experiment and an increasing number of neutron-star observations, a multitude of information about the equation of state (EOS) for matter at extreme densities is available. Here, we apply these different pieces of data individually to a broad set of physics-agnostic candidate EOSs and analyze the resulting constraints. Specifically, we make use...

11. Decoding the nuclear symmetry energy event-by-event in heavy-ion collisions with machine learning

Yongjia Wang (Huzhou University)

13/09/2024 09:30

Combined analysis of nuclear and astrophysics information, Bayesian approach, and machine learning

Oral Presentation

Inferences of the nuclear symmetry energy from heavy-ion collisions are currently based on the comparison of measured observables and transport model simulations. Only the expectation values of observables over all considered events are used in these approaches, however, observables can be obtained event-by-event both in experiments and transport model simulations. By using the light gradient...

34. Alpha-decay half-lives and symmetry energy in KIDS model

Chang-Hwan Lee (Pusan National University)

13/09/2024 09:50

Combined analysis of nuclear and astrophysics information, Bayesian approach, and machine learning

Oral Presentation

We studied alpha-decay half-lives of 84 <= Z <= 92 in the semiclassical WKB approximation frame work using the density-dependent cluster model and the density distribution described by various Korea-IBS-Daegu-SKKU (KIDS) models. Main goal of this work is to find a correlation between alpha-decay half-lives and the stiffness of the symmtery energy. Parameters of KIDS model are determined to...

10. Calibrating global behaviour of equation of state by combining nuclear and astrophysics inputs in a machine learning approach

SK MD ADIL IMAM (Saha Institute of Nuclear Physics)

13/09/2024 10:10

Microscopic calculations of neutron-rich dense nuclear matter

Oral Presentation

Recovering the nuclear matter parameters (NMPs), crucial elements in neutron star equations of state for the nucleonic core configuration, is a significant ongoing task in nuclear astrophysics. This involves utilizing various experimental data and astrophysical observations through a Bayesian approach. However, the conventional method of computing the equation of state (EoS) and solving the...