Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 82 tok/s
Gemini 2.5 Pro 62 tok/s Pro
GPT-5 Medium 32 tok/s Pro
GPT-5 High 36 tok/s Pro
GPT-4o 78 tok/s Pro
Kimi K2 195 tok/s Pro
GPT OSS 120B 423 tok/s Pro
Claude Sonnet 4.5 33 tok/s Pro
2000 character limit reached

Relativistic Nuclear Energy Density Functionals: Mean-Field and Beyond (1102.4193v1)

Published 21 Feb 2011 in nucl-th

Abstract: Relativistic energy density functionals (EDF) have become a standard tool for nuclear structure calculations, providing a complete and accurate, global description of nuclear ground states and collective excitations. Guided by the medium dependence of the microscopic nucleon self-energies in nuclear matter, semi-empirical functionals have been adjusted to the nuclear matter equation of state and to bulk properties of finite nuclei, and applied to studies of arbitrarily heavy nuclei, exotic nuclei far from stability, and even systems at the nucleon drip-lines. REDF-based structure models have also been developed that go beyond the static mean-field approximation, and include collective correlations related to the restoration of broken symmetries and to fluctuations of collective variables. These models are employed in analyses of structure phenomena related to shell evolution, including detailed predictions of excitation spectra and electromagnetic transition rates.

Citations (372)

Summary

  • The paper presents a comprehensive application of relativistic energy density functionals to predict both ground states and collective excitations in nuclei.
  • It employs self-consistent mean-field models and advanced techniques like the Generator Coordinate Method to restore symmetry and enable configuration mixing.
  • The study underscores the method's success in describing nuclei from the valley of stability to drip lines, aligning well with empirical nuclear data.

Relativistic Nuclear Energy Density Functionals: Mean-Field and Beyond

The paper, "Relativistic Nuclear Energy Density Functionals: Mean-Field and Beyond," offers a comprehensive overview of the application of relativistic energy density functionals (REDFs) in nuclear structure calculations. The authors present a meticulous analysis of how REDFs have become indispensable for providing detailed predictions of nuclear ground states and collective excitations across the nuclear chart.

In showcasing the capability of REDFs, the paper delineates their adeptness in handling a wide spectrum of nuclei, from those near the valley of β-stability to systems at the nucleon drip lines. These functionals draw on the medium dependence of microscopic nucleon self-energies in nuclear matter and are semi-empirically adjusted to finite nuclei properties. Specifically, the REDF-based models extend beyond the traditional static mean-field approximations, incorporating collective correlations through the restoration of broken symmetries and collective variable fluctuations. This approach is notably applied to phenomena associated with shell evolution, providing vital insight into excitation spectra and electromagnetic transition rates.

The authors review the relativistic energy density functional (EDF) framework, emphasizing its realization through self-consistent mean-field (SCMF) models. These models, akin to Kohn-Sham density functional theory, simplify the many-body problem into a more tractable one-body problem. Relativistic formulations, like Quantum Hadrodynamics (QHD), facilitate the natural inclusion of nucleon spin degrees of freedom and scalar and vector self-energies, which contribute to a nuanced depiction of nuclear matter. Such formulations enable meaningful interpretations of empirically observed phenomena, such as the saturation mechanism in covariant treatments.

The paper outlines significant numerical success, such as RMF-based models providing consistent descriptions of nuclear structure phenomena even in nuclei far removed from stability, and claims that these models often align with empirical data on shell evolution. The detailed review covers recent advancements wherein REDFs incorporate collective correlations, emphasizing the Generator Coordinate Method (GCM) and Hartree-(Fock)-Bogoliubov formalisms to account for symmetry restoration and configuration mixing.

Furthermore, the document ventures into the burgeoning domain of symmetry restoration and configuration mixing, crucial for depicting detailed spectroscopic levels accurately. The paper specifically highlights the development of sophisticated models that incorporate angular-momentum projected wave functions while applying the GCM approach. Through a systematic review, the authors reveal how these methodologies are pivotal in predicting and understanding the manifold facets of nuclear shape transitions.

The implications of this research are profound, particularly in expanding the theoretical underpinnings of nuclear structure physics. The functional methodology detailed offers the potential for improved predictions of nuclear phenomena, providing a clearer window into the complexities of isotopic behaviors, especially those not easily observable through experimentation alone. As the research persistently pushes boundaries, it speculates a promising trajectory for the future developments in computational nuclear physics models. The ability to extend these analyses to medium-heavy and heavy nuclei, while maintaining computational feasibility, is indicative of the robustness of these models and methodologies.

In conclusion, "Relativistic Nuclear Energy Density Functionals: Mean-Field and Beyond" serves as a crucial reference point for ongoing and future endeavors in the domain of nuclear physics, offering both a detailed methodological framework and a roadmap for exploring the nuanced structural intricacies of atomic nuclei across the chart of nuclides.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.