Nuclear Theory·nucl-th

Tuesday·January 14, 2025

12 papers—2 primary·10 cross-listed

03
Deep learning of phase transitions with minimal examples
Ahmed Abuali · David A. Clarke · Morten Hjorth-Jensen · Ioannis Konstantinidis · Claudia Ratti · Jianyi Yang
Over the past several years, there have been many studies demonstrating the ability of deep neural networks to identify phase transitions in many physical systems, notably in classical statistical physics systems. One often finds that the prediction of deep learning methods trained on many ensembles below and above the critical temperature $T_{c}$ behaves similarly to an order parameter, and this analogy has been successfully used to locate $T_{c}$ and estimate universal critical exponents. In this work, we pay particular attention to the ability of a convolutional neural network to capture these critical parameters for the 2- $d$ Ising model when the network is trained on configurations at $T = 0$ and $T = \infty$ only. We directly compare its output to the same network trained at multiple temperatures below and above $T_{c}$ to gain understanding of how this extreme restriction of training data can impact a neural network's ability to classify phases. We find that the network trained on two temperatures is still able to identify $T_{c}$ and $ν$ , while the extraction of $γ$ becomes more challenging.
↳ cond-mat.stat-mechnucl-thphysics.data-anPRE(2025)·4 citations
04
Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
Gert Aarts🇬🇧 · Kenji Fukushima🇯🇵 · Tetsuo Hatsuda🇯🇵 · Andreas Ipp🇦🇹 · Shuzhe Shi🇨🇳 · Lingxiao Wang🇯🇵 · Kai Zhou🇨🇳
The integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems, in which accurate physical properties are extracted from complex data sets. This is particularly relevant for quantum chromodynamics (QCD), the theory of strong interactions, with its inherent limitations in observational data and demanding computational approaches. This perspective highlights advances and potential of physics-driven learning methods, focusing on predictions of physical quantities towards QCD physics, and drawing connections to machine learning(ML). It is shown that the fusion of ML and physics can lead to more efficient and reliable problem-solving strategies. Key ideas of ML, methodology of embedding physics priors, and generative models as inverse modelling of physical probability distributions are introduced. Specific applications cover first-principle lattice calculations, and QCD physics of hadrons, neutron stars, and heavy-ion collisions. These examples provide a structured and concise overview of how incorporating prior knowledge such as symmetry, continuity and equations into deep learning designs can address diverse inverse problems across different physical sciences.
↳ hep-latcs.LGhep-phnucl-thNature Rev.Phys.(2025)·53 citations
05
Dynamics of Heavy Quarks in Strongly Coupled $N = 4$ SYM Plasma
Krishna Rajagopal🇺🇸 · Bruno Scheihing-Hitschfeld🇺🇸 · Urs Achim Wiedemann🇨🇭
We calculate the probability distribution $P (k)$ for a heavy quark with velocity $v$ propagating through strongly coupled $N = 4$ SYM plasma in the 't Hooft limit at a temperature $T$ to acquire a momentum $k$ due to interactions with the plasma. This distribution encodes the well-known drag coefficient $η_{D}$ and the transverse and longitudinal momentum diffusion coefficients $κ_{T}$ and $κ_{L}$ . Furthermore, our calculation determines all of the higher order and mixed moments to leading order in $1/ λ$ for the first time. These non-Gaussian features of $P (k)$ include qualitatively novel correlations between longitudinal energy loss and transverse momentum broadening at nonzero $v$ . We demonstrate that these non-Gaussian characteristics can be sizable in magnitude and even dominant in physically relevant situations. We use these results to derive a Kolmogorov equation for the evolution of the probability distribution for the total momentum of a heavy quark that propagates through strongly coupled plasma. This evolution equation accounts for all higher order correlations between transverse momentum broadening and longitudinal energy loss, which we have calculated from first principles. It reduces to a Fokker-Planck (FP) equation when truncated to only include the effects of $η_{D}$ , $κ_{T}$ and $κ_{L}$ . Remarkably, while heavy quarks do not reach kinetic equilibrium with the plasma if evolved with this FP equation, we demonstrate that heavy quarks do reach kinetic equilibrium if evolved with the all-order Kolmogorov equation we have derived. Our results thus provide a dynamically complete framework for understanding the thermalization of a heavy quark that may be initially far from equilibrium in the strongly coupled $N = 4$ SYM plasma -- as well as new insight into heavy quark transport and equilibration in quark-gluon plasma.
↳ hep-phhep-thnucl-thJHEP(2025)·16 citations
06
Transition balance in QCD nucleation
Tianzhe Zhou🇨🇳 · Qiuze Sun🇨🇳 · Jin Hu🇨🇳 · Carsten Greiner🇩🇪 · Zhe Xu🇨🇳
As an extended and more complete version of the primary QCD nucleation model presented in Ref. [1], the new model introduces explicitly the transition balance and formulates it in both macroscopic and microscopic descriptions. The microscopic description of the transition balance in QCD nucleation is implemented in a kinetic parton cascade model and tested for a first-order phase transition from gluons to pions in a one-dimensional expansion with Bjorken boost invariance.
↳ hep-phnucl-thPRC(2026)·0 citations
07
Systematic uncertainties from higher-twist corrections in DIS at large x
Matteo Cerutti🇺🇸 · Alberto Accardi🇺🇸 · Ishara P. Fernando🇺🇸 · Shujie Li🇺🇸 · Joseph F. Owens🇺🇸 · Sanghwa Park🇺🇸
We investigate the systematic uncertainties and potential biases arising from the inclusion of large- $x$ corrections to proton and deuteron deep inelastic scattering (DIS) data in global quantum chromodynamics (QCD) analyses. Using the CTEQ-JLab framework, we examine various approaches to implementing higher-twist corrections in nucleon structure functions and off-shell PDF modifications in deuteron targets. We analyze how these components interact and influence the determination of the $d$ -quark PDF and the neutron structure function at large $x$ . We find that it is very important to consider isospin-dependent higher-twist corrections in order to minimize implementation biases in the extracted quantities.
↳ hep-phhep-exnucl-thPRD(2025)·19 citations
08
Constraint on the magnetic field for the stable strange quark matter
Xin-Jian Wen🇨🇳 · Xiao-Wen Jiang🇨🇳
The quasiparticle model is employed to investigate the quark matter at finite chemical potential. The effective bag constant is derived to be dependent on both the chemical potential and the magnetic field. The self-consistent thermodynamics is fulfilled that the free energy minimum corresponds to the zero pressure. It is shown that the strong magnetic field is helpful for the stabilization of the strange quark matter. However, the increase in the coupling constant and the vacuum bag constant could reduce the stability. For the absolutely stable strange quark matter, there is a lower limit of the allowed magnetic field, which rises with the increase in the coupling constant and the vacuum bag constant.
↳ hep-phnucl-thMod.Phys.Lett.A(2025)·0 citations
09
Impact of dark matter distribution on neutron star properties
Ankit Kumar🇯🇵 · Hajime Sotani🇯🇵
We investigate the structural and observable impacts of dark matter (DM) on neutron stars using a combined equation of state that integrates the relativistic mean field (RMF) model for baryonic matter with a variable density profile for DM, incorporating DM-baryon interactions mediated by the Higgs field. Employing three RMF parameter sets (NL3, BigApple, and IOPB-I) for baryonic matter, we analyze mass-radius relations, maximum mass, and tidal deformability, focusing on DM density scaling ( $α$ ) and steepness ( $β$ ) parameters. Our findings reveal that increased DM concentration significantly enhances NS compactness, shifting mass-radius profiles and reducing tidal deformability. The DM influence strongly depends on the steepness of the DM density profile, where high $β$ values lead to strongly confined DM within the NS core, resulting in more compact and less deformable configurations. Observational constraints from PSR J0740+6620 and GW170817 impose consistent structural limits on DM fractions across different equations of state models, narrowing the allowable parameter space for DM and linking specific combinations of $α M_{χ}$ ( $M_{χ}$ being the mass of dark matter particle) and $β$ values to viable NS structures. This study highlights the interplay among DM concentration, nuclear stiffness, and observational data in shaping NS structure, offering insights into future constraints on DM in high-density astrophysical environments.
↳ astro-ph.HEhep-thnucl-thPRD(2025)·23 citations
10
Test for universality of short-range correlations in pion-induced Drell-Yan Process
Fei Huang🇨🇳 · Shu-Man Hu🇨🇳 · De-Min Li🇨🇳 · Ji Xu🇨🇳
We investigate nuclear modification and the universality of short-range correlation (SRC) in pion-induced Drell-Yan process. Employing nuclear parton distribution functions (nPDFs) and pion PDFs, the ratio of differential cross sections of different nuclei relative to the free nucleon is presented. A kind of universal modification function was proposed which would provide nontrivial tests of SRC universality on the platform of pion-induced Drell-Yan. This work improves our understanding of nuclear structure and strong interactions.
↳ hep-phnucl-thEPJC(2025)·3 citations
11
Effects of inner crusts on $g$ -mode oscillations in neutron stars
Hao Sun🇨🇳 · Jia-Xing Niu🇨🇳 · Hong-Bo Li🇨🇳 · Cheng-Jun Xia🇨🇳 · Enping Zhou🇨🇳 · Yiqiu Ma🇨🇳 · Ying-Xun Zhang🇨🇳
In this work we investigate the influence of neutron stars' crusts on the non-radial $g$ -mode oscillations and examine their correlations with nuclear matter properties fixed by adopting 10 different relativistic density functionals. At subsaturation densities, neutron star matter takes non-uniform structures and form the crusts. We find that the Brunt-Väisälä (BV) frequency increases significantly at densities slightly above the neutron drip density (i.e., neutron stars' inner crusts), which leads to crust $g$ -mode oscillations with their frequencies insensitive to the adopted density functional. At larger densities, BV frequency increases as well due to the core-crust transitions and emergence of muons, which lead to core $g$ -mode oscillations. It is found that the obtained core $g$ -mode frequencies generally increase with the slope of nuclear symmetry energy $L$ , which eventually intersect with that of the crust $g$ modes adopting large enough $L$ . This leads to the avoid-crossing phenomenon for the global $g$ modes that encompass contributions from both the crust and core. The correlation between the global $g_{1}$ mode and $L$ is identified for neutron stars with masses $M ≳ 1.4 M_{⊙}$ , which enables the measurements of $L$ based on gravitational wave observations. In our future study, the effects of the discontinuities in density or shear modulus should be considered, while the temperature, rotation, magnetic field, and superfluid neutron gas in neutron stars could also play important roles.
↳ astro-ph.HEnucl-thPRD(2025)·8 citations
12
Dominance of Electric Fields in the Charge Splitting of Elliptic Flow
Ankit Kumar Panda🇮🇳
In this study, we investigate the impact of electromagnetic fields, highlighting the dominant effect of electric fields on the splitting of elliptic flow, $ \Delta v_2 $ with transverse momentum ( $p_{T}$ ). The velocity and temperature profiles of quark-gluon plasma (QGP) is described through thermal model calculations. The electromagnetic field evolution is however determined from the solutions of Maxwell's equations, assuming constant electric and chiral conductivities. We find that the slower decay of the electric fields compared to the magnetic fields makes its impact on the splitting of the elliptic flow more dominant. We further estimated that the maximum value of $ |\langle eF \rangle| $, evaluated by averaging the field values over all spatial points on the hypersurface and across all field components, is approximately $ (0.010003 \pm 0.000195) \, m_{\pi}^2 $ for $ \sqrt{s_{\text{NN}}} = 7.7 \, \text{GeV} $, which could describe the splitting of elliptic flow data within the current experimental uncertainty reasonably well.
↳ hep-phhep-thnucl-thJ.Phys.G(2025)·5 citations

Affiliations

first authorsco-authorsvia INSPIRE