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It is becoming ever clearer that the neutrino signal from the next supernova in our Galaxy can reveal missing information about the neutrino as well as allowing us to probe the explosion of the star by decoding the temporal and spectral…

High Energy Physics - Phenomenology · Physics 2010-10-14 James P. Kneller

The statistical model of compound-nucleus reactions predicts that the fluctuations of the partial $\gamma$-decay widths for a compound-nucleus resonance are governed by the Porter-Thomas distribution (PTD), and that consequently the…

Nuclear Theory · Physics 2020-01-27 P. Fanto , Y. Alhassid , H. A. Weidenmüller

There have been many reports of non-statistical effects in neutron-capture measurements. However, reports of deviations of reduced-neutron-width distributions from the expected Porter-Thomas (PT) shape largely have been ignored. Most of…

In the frameworks of hypothesis of practical constancy of the neutron resonance number in small fixed intervals of neutron energy, their most probable value was determined for nucleus mass region 230<A<244 from approximation of the reduced…

Nuclear Experiment · Physics 2011-05-31 A. M. Sukhovoj , V. A. Khitrov

The radius $R_{1.4}$ of neutron stars (NSs) with a mass of 1.4 M$_{\odot}$ has been extracted consistently in many recent studies in the literature. Using representative $R_{1.4}$ data, we infer high-density nuclear symmetry energy…

High Energy Astrophysical Phenomena · Physics 2019-10-04 Wen-Jie Xie , Bao-An Li

Random matrix theory (RMT) is based on two assumptions: (1) matrix-element independence, and (2) base invariance. Most of the proposed generalizations keep the first assumption and violate the second. Recently, several authors presented…

Statistical Mechanics · Physics 2009-07-14 A. Y. Abul-Magd

We study complex networks under random matrix theory (RMT) framework. Using nearest-neighbor and next-nearest-neighbor spacing distributions we analyze the eigenvalues of adjacency matrix of various model networks, namely, random,…

Statistical Mechanics · Physics 2009-11-13 Sarika Jalan , Jayendra N. Bandyopadhyay

The compatibility of neutrino-nucleus deep inelastic scattering data within the universal, factorizable nuclear parton distribution functions has been studied independently by several groups in the past few years. The conclusions are…

High Energy Physics - Phenomenology · Physics 2013-07-03 Hannu Paukkunen , Carlos A. Salgado

Density matrix perturbation theory [Phys. Rev. Lett. Vol. 92, 193001 (2004)] provides an efficient framework for the linear scaling computation of response properties [Phys. Rev. Lett. Vol. 92, 193002 (2004)]. In this article, we generalize…

Computational Physics · Physics 2009-11-11 Anders M. N. Niklasson , Valery Weber , Matt Challacombe

We address the question of the role of low-energy nuclear physics data in constraining neutron star global properties, e.g., masses, radii, angular momentum, and tidal deformability, in the absence of a phase transition in dense matter. To…

Nuclear Theory · Physics 2023-04-06 Brett V. Carlson , Mariana Dutra , Odilon Lourenço , Jérôme Margueron

Recently, the emergence of cosmological tension has raised doubts about the consistency of the $\Lambda$CDM model. In order to constrain the neutrino mass within a consistent cosmological framework, we investigate three massive neutrinos…

Cosmology and Nongalactic Astrophysics · Physics 2023-12-13 Ye-Huang Pang , Xue Zhang , Qing-Guo Huang

Nuclear masses are of great importance in nuclear physics and astrophysics. Descriptive experimental data on nuclear masses and the prediction of unknown masses based on residual proton-neutron interactions are a focus in nuclear physics.…

Nuclear Theory · Physics 2020-05-18 B. B. Jiao

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…

Nuclear Theory · Physics 2025-04-01 Luca Passarella , Jerome Margueron , Giuseppe Pagliara

In this work, we study the properties of neutron stars using the linear Relativistic Mean-Field (RMF) theory and consider multiple degrees of freedom inside neutron stars, including hyperons and $\Delta$ resonances. We investigate different…

Nuclear Theory · Physics 2025-10-17 Chen Wu , Wenjun Guo

To shed light on the deuteron radius puzzle we analyze the theoretical uncertainties of the nuclear structure corrections to the Lamb shift in muonic deuterium. We find that the discrepancy between the calculated two-photon exchange…

Nuclear Theory · Physics 2018-06-12 Oscar Javier Hernandez , Andreas Ekström , Nir Nevo Dinur , Chen Ji , Sonia Bacca , Nir Barnea

The systematic study of the correlation between the experimental giant dipole resonance (GDR) width and the average deformation <\beta> of the nucleus at finite excitation is presented for the mass region A ~ 59 to 208. We show that the…

The recently published cosmological bound on the absolute neutrino masses obtained from the Wilkinson Microwave Anisotropy Probe (WMAP) data has important consequences for neutrino experiments and models. Taken at face value, the new bound…

High Energy Physics - Phenomenology · Physics 2010-04-05 G. Bhattacharyya , H. Päs , L. Song , T. J. Weiler

The radii and tidal deformabilities of neutron stars are investigated in the framework of relativistic mean-field (RMF) model with different density-dependent behaviors of symmetry energy. To study the effects of symmetry energy on the…

Nuclear Theory · Physics 2020-02-04 Jinniu Hu , Shishao Bao , Ying Zhang , Ken'ichiro Nakazato , Kohsuke Sumiyoshi , Hong Shen

The \emph{Relativistic Schr\"odinger Theory} (RST) has been set up as an alternative form of particle theory. This theory obeys the fundamental symmetries which are required to hold for any meaningful theory: gauge and Lorentz covariance…

General Physics · Physics 2017-01-11 M. Mattes , M. Sorg

Modern Machine Learning (ML) and Deep Neural Networks (DNNs) often operate on high-dimensional data and rely on overparameterized models, where classical low-dimensional intuitions break down. In particular, the proportional regime where…

Machine Learning · Statistics 2026-04-17 Zhenyu Liao , Michael W. Mahoney