arXiv subjects

Shi Yin

Publications and source records attributed to Shi Yin.

At least 19 recordsLinked to original sources

arXiv2026

Hadronic rescattering effects on net-proton cumulants from functional renormalization group calculations

Net-proton cumulants in the Beam Energy Scan region of heavy-ion collisions are widely used to probe critical fluctuations associated with the conjectured critical endpoint of Quantum Chromodynamics (QCD). Most existing studies, however, concentrate on the initial-state or phase-transition contributions, while the impact of hadronic rescattering on these observables has not been fully quantified. To address this gap, we construct event-by-event proton and antiproton distributions from functional renormalization group (fRG) cumulants using the maximum entropy principle, and propagate the resulting particles through the hadronic transport model SMASH in a simplified spherical evolution setup. We systematically investigate how the hadronic cascade modifies net-proton cumulants at collision energies $\sqrt{s_{NN}}=3.0$, 3.9, 4.9, 7.2, and 7.7~GeV. In the canonical-ensemble framework, which enforces exact net-baryon number conservation, the higher-order cumulant signal---in particular the ratio $C_4/C_2$ at $\sqrt{s_{NN}}=4.9$~GeV---is strongly reduced during the early stage of the cascade; the suppression of $C_4/C_2$ reaches approximately $20\%$. The non-monotonic energy dependence inherited from the fRG input survives the hadronic evolution, but its magnitude is substantially modified. These results demonstrate that hadronic rescattering provides a non-negligible background effect that must be accounted for when extracting QCD critical-point signals from experimental data.

nucl-th↗

arXiv2026

Critical net-proton number fluctuations with hydrodynamics

We compute the net-proton number fluctuations and their ratios $C_2/C_1$, $C_3/C_2$ and $C_4/C_2$ on the hydrodynamic freeze-out hypersurface of particlization at nine collision energies, $\sqrt{s_{\mathrm{NN}}}=7.7-200$ GeV, based on the fluctuations obtained from the functional renormalization group (fRG) approach, where both the regular and the critical fluctuations arising from the critical end point (CEP) are included. The transverse momentum and rapidity acceptance windows as same as the experimental measurements, the isospin randomization for the proton number fluctuations, and the global baryon conservation effect are implemented in the calculations. The results are also compared with the baseline results without critical fluctuations. It is found that for the low-order cumulants, e.g., $C_2/C_1$ the difference between the critical and non-critical results is small, while the difference increases with the increasing order of cumulants in the region of low collision energy. A non-monotonic dependence on the collision energy is observed in $C_4/C_2$ with critical fluctuations, which is absent in the results without critical fluctuations.

nucl-th↗

arXiv2026

Fierz-complete four-quark interactions and the QCD phase diagram

The dynamics of Fierz-complete four-quark interactions and its influence on the QCD phase diagram have been investigated within the functional renormalization group approach to QCD at finite temperature and densities. It is found that in the vacuum the pion and sigma channels play the overwhelmingly dominant role, and all the other channels are negligible. However, when it is near the critical end point (CEP), the magnitude of four-quark couplings in other channels increases sizably and they become more and more important. In comparison to the single scalar-pseudoscalar channel of four-quark interactions, the dynamics of Fierz-complete four-quark interactions increases a bit the curvature of the phase boundary, and moves the CEP to location of larger baryon chemical potential and smaller temperature.

hep-ph↗

arXiv2026

Scalar diquarks in the QCD vacuum

While QCD fundamentally only depends on the values of the strong coupling and the quark masses, it exhibits a rich nonperturbative structure at low energies, where composite fields emerge as the relevant degrees of freedom. In this work, we present a first-principles framework that captures the transition from fundamental QCD to its low-energy sector in vacuum. It builds on the dynamical hadronization technique within the functional renormalization group approach to two-flavor QCD. In this framework, the low-energy constants relevant for effective models, including effective masses and coupling strengths, naturally emerge from the underlying renormalization group flow without introducing free parameters beyond those of QCD itself. We investigate the dynamical emergence of the pion, the $\sigma$-meson and the scalar diquark in both imaginary and real time, and determine a set of QCD low-energy constants which can be used to fix the free parameters of models of dense quark matter with a two-flavor color superconducting phase. In particular, this includes previously unknown properties of the scalar diquark. Our results provide important microscopic input for constraining color superconducting phases, which are expected to play a key role in our understanding of dense neutron star matter.

hep-ph↗

arXiv2026

Functional renormalization group study of the jet quenching parameter near the QCD critical end point

We investigate the jet quenching parameter $\hat{q}$ in the QCD phase diagram within a QCD-assisted low-energy effective theory using the functional renormalization group (fRG). Following the formalism that relates $\hat{q}$ to the spectral functions of the chiral order-parameter field, we compute the $\sigma$ and $\pi$ meson contributions to $\hat{q}$ at finite temperature and baryon chemical potential from analytically continued mesonic two-point functions. We find that $\hat{q}$ receives appreciable contributions mainly above the chiral phase boundary and exhibits a pronounced enhancement at large baryon chemical potential as the chiral crossover sharpens toward the critical end point (CEP), a behavior consistent with the picture of partonic critical opalescence (PCO), a pronounced enhancement of jet transverse momentum broadening induced by the critical $\sigma$ field fluctuations.

hep-ph↗

arXiv2026

Strangeness neutrality and the QCD phase diagram

We map out the phase structure of $N_f=2+1$ flavour QCD at strangeness neutrality with functional QCD. We find a critical end point at $(T_{\rm CEP},\mu_{B,{\rm CEP}})|_{n_S=0} = (92, 696)$\,MeV. The computation is done with the functional renormalisation group, and we systematically improve on previous works, hence reducing the systematic error significantly. Our results pass relevant QCD benchmarks: they agree well with and corroborate the QCD phase structure from functional QCD results at vanishing strangeness chemical potential. Moreover, they agree well with lattice QCD results at vanishing chemical potential. Specifically, the ratio of the second order curvature coefficient $\kappa_2$ agrees with that obtained from lattice computations, $\kappa_2(n_S=0)/\kappa_2(\mu_S=0)=0.897(20)$.

hep-ph↗

arXiv2026

Mapping the critical region along the second-order chiral phase boundary

We investigate the extent of the critical scaling region of the chiral phase transition at finite chemical potential within the quark-meson (QM) model using the functional renormalization group (fRG) approach. By analyzing the scaling behavior of the chiral order parameter and correlation length with respect to temperature and pion mass near the second-order phase transition, we extract critical exponents from the data and quantify the range over which the scaling relations remain valid. We find that both the leading order and the next-to-leading-order scaling regions systematically shrink as the chemical potential increases. This behavior is observed in both the local potential approximation (LPA) and its extension including anomalous dimensions (LPA'), with qualitatively consistent results, while the scaling region in LPA' is slightly smaller than that in LPA.

hep-ph↗

arXiv2026

NextCrystal: a Symmetry-Driven Generative Framework for Crystal Structure Prediction

Crystal structure prediction (CSP), which aims to predict the 3D atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding. Crystal symmetry plays a crucial role in CSP, but given the composition in a unit cell, existing methods either struggle with the NP-hard combinatorial challenge of enforcing symmetry rigorously or rely on retrieving known templates, inherently limiting both physical fidelity and the discovery of genuinely new materials. To address this challenge, we introduce NextCrystal, a symmetry-driven generative framework that employs large language models to encode chemical semantics and directly generate fine-grained Wyckoff site patterns from atomic stoichiometry, eliminating reliance on database lookups. To overcome the combinatorial complexity of site assignments, we incorporate domain knowledge via an efficient, linear-complexity heuristic beam search, rigorously enforcing algebraic consistency between site multiplicities and atomic stoichiometry. By integrating this symmetry-consistent template into a diffusion backbone, the framework constrains the stochastic generative trajectory to a physically plausible geometric manifold. NextCrystal achieves state-of-the-art performance on stability, uniqueness, and novelty (SUN) benchmarks, as well as superior structural matching, establishing a rigorous paradigm for exploring previously unexplored crystallographic space without relying on prior structural templates. As a representative application, first-principles screening of HfO2 candidates generated by NextCrystal identifies a previously unreported dynamically stable Pnma phase, 0.056~eV/atom lower in energy than the conventional high-pressure Pnma phase.

cond-mat.mtrl-sci↗

arXiv2025

Real-time evolution of critical modes in the QCD phase diagram

A QCD-assisted relaxation dynamic model for the critical mode of the critical end point (CEP) in the QCD phase diagram is developed, which allows us to investigate the critical slowing down effect quantitatively in the QCD phase diagram, especially in the proximity of the CEP, without any phenomenological parameters. The relaxation time from nonequilibrium to equilibrium in the QCD phase diagram is extracted from the Langevin simulations of the QCD-assisted relaxation dynamic model. It is found that in a narrow region along the phase boundary radiated from the CEP, the relaxation time is enhanced significantly. Outside this narrow region, the relaxation time drops drastically, which implies that the dynamic critical region is small in the QCD phase diagram. We also find that the effects of critical slowing down are mild on the chemical freeze-out curves.

hep-ph↗

arXiv2025

Dissecting the moat regime at low energies I: Renormalization and the phase structure

Dense QCD matter can feature a moat regime, where the static energy of mesons is minimal at nonzero momentum. Valuable insights into this regime can be gained using low-energy models. This, however, requires a careful assessment of model artifacts. We therefore study the effects of renormalization and in-medium modifications of quark-meson interaction on the moat regime. To capture the main effects, we use a two-flavor quark-meson model at finite temperature and baryon density in the random phase approximation. We put forward a convenient renormalization scheme to account for the nontrivial momentum dependence of meson self-energies and discuss the role of renormalization conditions for renormalization group consistent results on the moat regime. In addition, we demonstrate and that its extent in the phase diagram critically depends on the interaction of quarks and mesons.

hep-ph↗

arXiv2025

Advancing Universal Deep Learning for Electronic-Structure Hamiltonian Prediction of Materials

Deep learning methods for electronic-structure Hamiltonian prediction has offered significant computational efficiency advantages over traditional DFT methods, yet the diversity of atomic types, structural patterns, and the high-dimensional complexity of Hamiltonians pose substantial challenges to the generalization performance. In this work, we contribute on both the methodology and dataset sides to advance universal deep learning paradigm for Hamiltonian prediction. On the method side, we propose NextHAM, a neural E(3)-symmetry and expressive correction method for efficient and generalizable materials electronic-structure Hamiltonian prediction. First, we introduce the zeroth-step Hamiltonians, which can be efficiently constructed by the initial charge density of DFT, as informative descriptors of neural regression model in the input level and initial estimates of the target Hamiltonian in the output level, so that the regression model directly predicts the correction terms to the target ground truths, thereby significantly simplifying the input-output mapping for learning. Second, we present a neural Transformer architecture with strict E(3)-Symmetry and high non-linear expressiveness for Hamiltonian prediction. Third, we propose a novel training objective to ensure the accuracy performance of Hamiltonians in both real space and reciprocal space, preventing error amplification and the occurrence of "ghost states" caused by the large condition number of the overlap matrix. On the dataset side, we curate a high-quality broad-coverage large benchmark, namely Materials-HAM-SOC, comprising 17,000 material structures spanning 68 elements from six rows of the periodic table and explicitly incorporating SOC effects. Experimental results on Materials-HAM-SOC demonstrate that NextHAM achieves excellent accuracy and efficiency in predicting Hamiltonians and band structures.

cs.LG↗

arXiv2025

High-order fluctuations of temperature in hot QCD matter

A new thermodynamic state function is introduced to describe the thermodynamics relevant for the mean transverse momentum fluctuations of charged particles in heavy-ion collisions, which allows us to compute the temperature fluctuations of different orders in hot quantum chromodynamics (QCD) matter for the first time. Consequently, it is found that the temperature fluctuations are suppressed remarkably as the system transitions from the hadron resonance gas (HRG) to the quark-gluon plasma (QGP) with increasing temperature or baryon chemical potential, alongside a negative skewness. This is attributed to the general fact that the heat capacity of QCD matter increases significantly in QGP in comparison to that in HRG. These predictions provide a candidate observable to discover the thermodynamic temperature fluctuations in upcoming heavy-ion collision experiments, which also paves a novel way to study QCD thermodynamics and QCD phase diagram through measurements of the mean transverse momentum fluctuations of charged particles.

hep-ph↗

arXiv2024

The QCD moat regime and its real-time properties

Dense QCD matter may exhibit crystalline phases. Their existence is reflected in a moat regime, where mesonic correlations feature spatial modulations. We study the realtime properties of pions at finite temperature and density in QCD in order to elucidate the nature of this regime. We show that the moat regime arises from particle-hole-like fluctuations near the Fermi surface. This gives rise to a characteristic peak in the spectral function of the pion at nonzero \emph{spacelike} momentum. This peak can be interpreted as a new quasi particle, the moaton. In addition, our framework also allows us to directly test the stability of the homogeneous chiral phase against the formation of an inhomogeneous condensate in QCD. We find that the formation of such a phase is highly unlikely for baryon chemical potentials $\mu_B \leq 630$\,MeV.

hep-ph↗

arXiv2024

HC$^3$L-Diff: Hybrid conditional latent diffusion with high frequency enhancement for CBCT-to-CT synthesis

Background: Cone-beam computed tomography (CBCT) plays a crucial role in image-guided radiotherapy, but artifacts and noise make them unsuitable for accurate dose calculation. Artificial intelligence methods have shown promise in enhancing CBCT quality to produce synthetic CT (sCT) images. However, existing methods either produce images of suboptimal quality or incur excessive time costs, failing to satisfy clinical practice standards. Methods and materials: We propose a novel hybrid conditional latent diffusion model for efficient and accurate CBCT-to-CT synthesis, named HC$^3$L-Diff. We employ the Unified Feature Encoder (UFE) to compress images into a low-dimensional latent space, thereby optimizing computational efficiency. Beyond the use of CBCT images, we propose integrating its high-frequency knowledge as a hybrid condition to guide the diffusion model in generating sCT images with preserved structural details. This high-frequency information is captured using our designed High-Frequency Extractor (HFE). During inference, we utilize denoising diffusion implicit model to facilitate rapid sampling. We construct a new in-house prostate dataset with paired CBCT and CT to validate the effectiveness of our method. Result: Extensive experimental results demonstrate that our approach outperforms state-of-the-art methods in terms of sCT quality and generation efficiency. Moreover, our medical physicist conducts the dosimetric evaluations to validate the benefit of our method in practical dose calculation, achieving a remarkable 93.8% gamma passing rate with a 2%/2mm criterion, superior to other methods. Conclusion: The proposed HC$^3$L-Diff can efficiently achieve high-quality CBCT-to-CT synthesis in only over 2 mins per patient. Its promising performance in dose calculation shows great potential for enhancing real-world adaptive radiotherapy.

eess.IV↗

arXiv2024

GPU Acceleration of Numerical Atomic Orbitals-Based Density Functional Theory Algorithms within the ABACUS package

With the fast developments of high-performance computing, first-principles methods based on quantum mechanics play a significant role in materials research, serving as fundamental tools for predicting and analyzing various properties of materials. However, the inherent complexity and substantial computational demands of first-principles algorithms, such as density functional theory, limit their use in larger systems. The rapid development of heterogeneous computing, particularly General-Purpose Graphics Processing Units (GPGPUs), has heralded new prospects for enhancing the performance and cost-effectiveness of first-principles algorithms. We utilize GPGPUs to accelerate the electronic structure algorithms in Atomic-orbital Based Ab-initio Computation at USTC (ABACUS), a first-principles computational package based on the linear combination of atomic orbitals (LCAO) basis set. We design algorithms on GPGPU to efficiently construct and diagonalize the Hamiltonian of a given system, including the related force and stress calculations. The effectiveness of this computational acceleration has been demonstrated through calculations on twisted bilayer graphene with the system size up to 10,444 atoms.

cond-mat.mtrl-sci↗

arXiv2024

Serialized Output Training by Learned Dominance

Serialized Output Training (SOT) has showcased state-of-the-art performance in multi-talker speech recognition by sequentially decoding the speech of individual speakers. To address the challenging label-permutation issue, prior methods have relied on either the Permutation Invariant Training (PIT) or the time-based First-In-First-Out (FIFO) rule. This study presents a model-based serialization strategy that incorporates an auxiliary module into the Attention Encoder-Decoder architecture, autonomously identifying the crucial factors to order the output sequence of the speech components in multi-talker speech. Experiments conducted on the LibriSpeech and LibriMix databases reveal that our approach significantly outperforms the PIT and FIFO baselines in both 2-mix and 3-mix scenarios. Further analysis shows that the serialization module identifies dominant speech components in a mixture by factors including loudness and gender, and orders speech components based on the dominance score.

cs.SD↗

arXiv2024

A Comprehensive Investigation on Speaker Augmentation for Speaker Recognition

Data augmentation (DA) has played a pivotal role in the success of deep speaker recognition. Current DA techniques primarily focus on speaker-preserving augmentation, which does not change the speaker trait of the speech and does not create new speakers. Recent research has shed light on the potential of speaker augmentation, which generates new speakers to enrich the training dataset. In this study, we delve into two speaker augmentation approaches: speed perturbation (SP) and vocal tract length perturbation (VTLP). Despite the empirical utilization of both methods, a comprehensive investigation into their efficacy is lacking. Our study, conducted using two public datasets, VoxCeleb and CN-Celeb, revealed that both SP and VTLP are proficient at generating new speakers, leading to significant performance improvements in speaker recognition. Furthermore, they exhibit distinct properties in sensitivity to perturbation factors and data complexity, hinting at the potential benefits of their fusion. Our research underscores the substantial potential of speaker augmentation, highlighting the importance of in-depth exploration and analysis.

cs.SD↗

arXiv2024

TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear Representations for Electronic-Structure Hamiltonian Prediction

We propose a framework to combine strong non-linear expressiveness with strict SO(3)-equivariance in prediction of the electronic-structure Hamiltonian, by exploring the mathematical relationships between SO(3)-invariant and SO(3)-equivariant quantities and their representations. The proposed framework, called TraceGrad, first constructs theoretical SO(3)-invariant trace quantities derived from the Hamiltonian targets, and use these invariant quantities as supervisory labels to guide the learning of high-quality SO(3)-invariant features. Given that SO(3)-invariance is preserved under non-linear operations, the learning of invariant features can extensively utilize non-linear mappings, thereby fully capturing the non-linear patterns inherent in physical systems. Building on this, we propose a gradient-based mechanism to induce SO(3)-equivariant encodings of various degrees from the learned SO(3)-invariant features. This mechanism can incorporate powerful non-linear expressive capabilities into SO(3)-equivariant features with consistency of physical dimensions to the regression targets, while theoretically preserving equivariant properties, establishing a strong foundation for predicting Hamiltonian. Our method achieves state-of-the-art performance in prediction accuracy across eight challenging benchmark databases on Hamiltonian prediction. Experimental results demonstrate that this approach not only improves the accuracy of Hamiltonian prediction but also significantly enhances the prediction for downstream physical quantities, and also markedly improves the acceleration performance for the traditional Density Functional Theory algorithms.

cs.LG↗