arXiv subjects

S. Shi

Publications and source records attributed to S. Shi.

11 recordsLinked to original sources

arXiv2026

Effects of Tungsten Radiative Cooling on Impurity, Heat and Momentum Transport in DIII-D Plasmas

A first-of-its-kind experiment was conducted in the DIII-D tokamak under WEST similarity constraints on plasma shape and core parameters. This work presents a detailed transport study comparing a reference regime dominated by intrinsic carbon radiation and a high-radiation regime resulting from controlled tungsten (W) injection using the Laser Blow-Off system, with a core tungsten concentration $n_{\mathrm{W}}/n_e \sim 3\times 10^{-4}$ and a radiated-power fraction $f_\mathrm{rad}>0.5$. The W-induced radiative cooling lowered the electron temperature, thereby decreasing $T_e/T_i$ and stabilizing trapped-electron-mode (TEM) turbulence. This transition in turbulence regime reduced momentum and ion thermal diffusivities, yielding ion temperature peaking and a factor-of-two increase in toroidal rotation. At the outer plasma region, enhanced $E\timesB$ shear and increased collisionality further suppressed ion-scale turbulence, causing a sharp drop in ion heat flux. Consequently, impurity transport, predominantly turbulent in the low-radiation regime, acquired a strong neoclassical inward W convection during radiative cooling, bootstrapping the cooling cycle. Despite $f_\mathrm{rad}>0.5$, radiative collapse was not observed, likely owing to collisional ion-to-electron energy exchange acting as an electron-energy reservoir, together with $1/1$ MHD activity modulating the radiated power through core impurity neoclassical $T_i$-screening. These results support preparation for a tungsten wall change in DIII-D by elucidating tungsten-induced turbulence stabilization. They also provide key insights for interpreting plasma performance in WEST and are relevant to future reactors expected to operate with radiating tungsten-walled plasmas.

physics.plasm-ph↗

arXiv2025

Distributed quantum computing with black-box subroutines

In this work, we propose a general protocol for distributed quantum computing that accommodates arbitrary unknown subroutines. It can be applied to scale up quantum computing through multi-chip interconnection, as well as to tasks such as estimating unknown parameters or processes for circuit depth reduction and constructing secure quantum cryptographic protocols. Our protocol builds upon a few techniques we develop, such as the oblivious quantum teleportation and control, which can circumvent quantum no-go theorems on the manipulation of unknown objects. Furthermore, we demonstrate that this protocol can be physically implemented using currently available quantum computing platforms. These results suggest that our framework could provide a foundation for developing more advanced quantum algorithms and protocols in the future.

quant-ph↗

arXiv2022

Excitation allocation for generic identifiability of linear dynamic networks with fixed modules

Identifiability of linear dynamic networks requires the presence of a sufficient number of external excitation signals. The problem of allocating a minimal number of external signals for guaranteeing generic network identifiability has been recently addressed in the literature. Here we will extend that work by explicitly incorporating the situation that some network modules are known, and thus are fixed in the parametrized model set. The graphical approach introduced earlier is extended to this situation, showing that the presence of fixed modules reduces the required number of external signals. An algorithm is presented that allocates the external signals in a systematic fashion.

eess.SY↗

arXiv2021

Deep-Unfolding Neural-Network Aided Hybrid Beamforming Based on Symbol-Error Probability Minimization

In massive multiple-input multiple-output (MIMO) systems, hybrid analog-digital (AD) beamforming can be used to attain a high directional gain without requiring a dedicated radio frequency (RF) chain for each antenna element, which substantially reduces both the hardware costs and power consumption. While massive MIMO transceiver design typically relies on the conventional mean-square error (MSE) criterion, directly minimizing the symbol error rate (SER) can lead to a superior performance. In this paper, we first mathematically formulate the problem of hybrid transceiver design under the minimum SER (MSER) optimization criterion and then develop a MSER-based gradient descent (GD) iterative algorithm to find the related stationary points. We then propose a deep-unfolding neural network (NN), in which the iterative GD algorithm is unfolded into a multi-layer structure wherein a set of trainable parameters are introduced for accelerating the convergence and enhancing the overall system performance. To implement the training stage, the relationship between the gradients of adjacent layers is derived based on the generalized chain rule (GCR). The deep-unfolding NN is developed for both quadrature phase shift keying (QPSK) and for $M$-ary quadrature amplitude modulated (QAM) signals and its convergence is investigated theoretically. Furthermore, we analyze the transfer capability, computational complexity, and generalization capability of the proposed deep-unfolding NN. Our simulation results show that the latter significantly outperforms its conventional counterpart at a reduced complexity.

eess.SP↗

arXiv2021

Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification

The Graph Neural Network (GNN) has been widely used for graph data representation. However, the existing researches only consider the ideal balanced dataset, and the imbalanced dataset is rarely considered. Traditional methods such as resampling, reweighting, and synthetic samples that deal with imbalanced datasets are no longer applicable in GNN. This paper proposes an ensemble model called Boosting-GNN, which uses GNNs as the base classifiers during boosting. In Boosting-GNN, higher weights are set for the training samples that are not correctly classified by the previous classifier, thus achieving higher classification accuracy and better reliability. Besides, transfer learning is used to reduce computational cost and increase fitting ability. Experimental results indicate that the proposed Boosting-GNN model achieves better performance than GCN, GraphSAGE, GAT, SGC, N-GCN, and most advanced reweighting and resampling methods on synthetic imbalanced datasets, with an average performance improvement of 4.5%

cs.LG↗

arXiv2021

The vector-apodizing phase plate coronagraph: design, current performance, and future development

Over the last decade, the vector-apodizing phase plate (vAPP) coronagraph has been developed from concept to on-sky application in many high-contrast imaging systems on 8-m class telescopes. The vAPP is an geometric-phase patterned coronagraph that is inherently broadband, and its manufacturing is enabled only by direct-write technology for liquid-crystal patterns. The vAPP generates two coronagraphic PSFs that cancel starlight on opposite sides of the point spread function (PSF) and have opposite circular polarization states. The efficiency, that is the amount of light in these PSFs, depends on the retardance offset from half-wave of the liquid-crystal retarder. Using different liquid-crystal recipes to tune the retardance, different vAPPs operate with high efficiencies ($>96\%$) in the visible and thermal infrared (0.55 $\mu$m to 5 $\mu$m). Since 2015, seven vAPPs have been installed in a total of six different instruments, including Magellan/MagAO, Magellan/MagAO-X, Subaru/SCExAO, and LBT/LMIRcam. Using two integral field spectrographs installed on the latter two instruments, these vAPPs can provide low-resolution spectra (R$\sim$30) between 1 $\mu$m and 5 $\mu$m. We review the design process, development, commissioning, on-sky performance, and first scientific results of all commissioned vAPPs. We report on the lessons learned and conclude with perspectives for future developments and applications.

astro-ph.IM↗

arXiv2021

Explanation of the anomalous redshift on nonlinear X-ray Compton scattering spectrum by a bound electron

Nonlinear Compton scattering is an inelastic scattering process where a photon is emitted due to the interaction between an electron and an intense laser field. With the development of X-ray free-electron lasers, the intensity of X-ray laser is greatly enhanced, and the signal from X-ray nonlinear Compton scattering is no longer weak. Although the nonlinear Compton scattering by an initially free electron has been thoroughly investigated, the mechanis of nonrelativistic nonlinear Compton scattering of X-ray photons by bound electrons is unclear yet. Here, we present a frequency-domain formulation based on the nonperturbative quantum electrodynamic to study nonlinear Compton scattering of two photons off a bound electron inside an atom in a strong X-ray laser field. In contrast to previous theoretical works, our results clearly reveal the existence of anomalous redshift phenomenon observed experimentally by Fuchs et al. (Nat. Phys. 11, 964 (2015)) and suggest its origin as the binding energy of the electron as well as the momentum transfer from incident photons to the electron during the scattering process. Our work builds a bridge between intense-laser atomic physics and Compton scattering process that can be used to study atomic structure and dynamics at high laser intensities.

physics.atom-ph↗

arXiv2020

Dijet Acoplanarity in CUJET3 as a Probe of the Nonperturbative Color Structure of QCD Perfect Fluids

Using the CUJET3=DGLV+VISHNU jet-medium interaction framework, we show that dijet azimuthal acoplanarity in high energy $A+A$ collisions is sensitive to possible non-perturbative enhancement of the jet transport coefficient, $\hat{q}(T,E)$, in the QCD crossover temperature $T\sim 150-300$ MeV range. With jet-medium couplings constrained by global RHIC\& LHC $\chi^2$ fits to nuclear modification data on $R_{AA}$, we compare predictions of the medium induced dijet transverse momentum squared, $Q_s^2\sim \langle \hat{q} L \rangle \sim \Delta\phi^2 E^2$, in two models of the temperature, $T$, and jet energy $E$ dependence of the jet medium transport coefficient, $\hat{q}(T,E)$. In one model, wQGP, only perturbative quark and gluon dof are assumed. In the second model, sQGMP, nonperturbative three component semi-Quark-Gluon and Magnetic Monopole dof are asssumed. We show that the dijet path averaged medium induced azimuthal acoplanarity, $\Delta\phi^2$, in sQGMP is robustly a factor of $\sim 2$ larger than in perturbative wQGP while the radiative energy loss in both models is nearly identical as required to fit the same single jet nuclear modification $R_{AA}$ data. Future A+A dijet acoplanarity measurements correlated with $R_{AA}$ and azimuthal asymmetry $v_n$ measurements therefore appears to be a promising strategy to search for possible signatures of critical opalescence like phenomena in the QCD confinement temperature range.

hep-ph↗

arXiv2018

Precision Dijet Acoplanarity Tomography of the Chromo Structure of Perfect QCD Fluids

Dijet acoplanarity is dominated by vacuum (Sudakov) pQCD radiation even in Pb+Pb collisions, but future higher precision measurements of the tails of the acoplanarity distributions can help to resolve {\em separately} the medium opacity, $\chi=L/\lambda$, and the color screening scale $\mu^2$ from the path averaged BDMS saturation scale $Q_s^2[\chi,\mu]=\int dL\; \hat{q}(E,T(L))\propto \mu^2 L/\lambda $, that is already well constrained by nuclear modification factor data on $R_{AA}(p_T)$. We compare Gaussian (BDMS) and non-Gaussian (GLV) models of medium broadening of vacuum (Sudakov) induced acoplanarity distributions in A+A. With few percent accuracy on the ratio of A+A to p+p distributions, experiments can easily identify non-Gaussian Landau and Rutherford tails due to multiple collisions. However, we find that sub-percent precision will be required to constrain $\chi$ and $\mu$ separately from $Q_s$.

hep-ph↗

arXiv2018

Extraction of Heavy-Flavor Transport Coefficients in QCD Matter

We report on broadly based systematic investigations of the modeling components for open heavy-flavor diffusion and energy loss in strongly interacting matter in their application to heavy-flavor observables in high-energy heavy-ion collisions, conducted within an EMMI Rapid Reaction Task Force framework. Initial spectra including cold-nuclear-matter effects, a wide variety of space-time evolution models, heavy-flavor transport coefficients, and hadronization mechanisms are scrutinized in an effort to quantify pertinent uncertainties in the calculations of nuclear modification factors and elliptic flow of open heavy-flavor particles in nuclear collisions. We develop procedures for error assessments and criteria for common model components to improve quantitative estimates for the (low-momentum) heavy-flavor diffusion coefficient as a long-wavelength characteristic of QCD matter as a function of temperature, and for energy loss coefficients of high-momentum heavy-flavor particles.

nucl-th↗

arXiv2017

Entanglement between Lowly and Highly Lying Atomic Spin Waves

Establishing a quantum interface between different physical systems is of special importance for developing the practical versatile quantum networks. Entanglement between low- and high-lying atomic spin waves is essential for building up Rydberg-based quantum information engineering, otherwhile be more helpful to study the dynamics behavior of entanglement under external pertur- bations. Here, we report on the successful storage of a single photon as a high-lying atomic spin wave in quantum regime. Via storing a K-vector entanglement between single photon and lowly lying spin wave, we thereby experimentally realize the entanglement between low- and high-lying atomic spin waves in two separated atomic systems. This makes our experiment the primary demonstration of Rydberg quantum memory of entanglement, making a primary step toward the construction of a hybrid quantum interface.

quant-ph↗