arXiv subjects

Yadi Wang

Publications and source records attributed to Yadi Wang.

6 recordsLinked to original sources

arXiv2025

Pinpointing Physical Solutions in Y(4230) Decays

To resolve ambiguities from multiple solutions in experimental measurements, we construct a $\chi^2$ function incorporating constraints such as isospin conservation and amplitude relations. By minimizing the global $\chi^2$, we identify physical solutions for seven hidden-charm decay channels of $Y(4230)$. Crucially, the physical solution for $Y(4230) \to \pi^{+}\pi^{-} J/\psi$ corresponds to the largest among four experimental solutions, potentially modifying inputs for theoretical calculations. Additionally, we predict $\Gamma_{ee} B(Y(4230) \to \pi^{+}\pi^{-} \psi(2S))$ based on our results, acknowledging substantial uncertainties in current measurements.

hep-ph↗

arXiv2025

Superior probabilistic computing using operationally stable probabilistic-bit constructed by manganite nanowire

Probabilistic computing has emerged as a viable approach to treat optimization problems. To achieve superior computing performance, the key aspect during computation is massive sampling and tuning on the probability states of each probabilistic bit (p-bit), demanding its high stability under extensive operations. Here, we demonstrate a p-bit constructed by manganite nanowire that shows exceptionally high stability. The p-bit contains an electronic domain that fluctuates between metallic (low resistance) and insulating (high resistance) states near its transition temperature. The probability for the two states can be directly controlled by nano-ampere electrical current. Under extensive operations, the standard error of its probability values is less than 1.3%. Simulations show that our operationally stable p-bit plays the key role to achieve correct inference in Bayesian network by strongly suppressing the relative error, displaying the potential for superior computing performance. Our p-bit also serves as high quality random number generator without extra data-processing, beneficial for cryptographic applications.

physics.app-ph↗

arXiv2025

A self-learning magnetic Hopfield neural network with intrinsic gradient descent adaption

Physical neural networks using physical materials and devices to mimic synapses and neurons offer an energy-efficient way to implement artificial neural networks. Yet, training physical neural networks are difficult and heavily relies on external computing resources. An emerging concept to solve this issue is called physical self-learning that uses intrinsic physical parameters as trainable weights. Under external inputs (i.e. training data), training is achieved by the natural evolution of physical parameters that intrinsically adapt modern learning rules via autonomous physical process, eliminating the requirements on external computation resources.Here, we demonstrate a real spintronic system that mimics Hopfield neural networks (HNN) and unsupervised learning is intrinsically performed via the evolution of physical process. Using magnetic texture defined conductance matrix as trainable weights, we illustrate that under external voltage inputs, the conductance matrix naturally evolves and adapts Oja's learning algorithm in a gradient descent manner. The self-learning HNN is scalable and can achieve associative memories on patterns with high similarities. The fast spin dynamics and reconfigurability of magnetic textures offer an advantageous platform towards efficient autonomous training directly in materials.

cond-mat.dis-nn↗

arXiv2023

Analytical formula for the cross section of hadron production from $e^{+}e^{-}$ collisions around the narrow charmouinum resonances

The paper reports an analytical formula for the production cross section of $e^{+}e^{-}$ annihilation to hadrons in the vicinity of a narrow resonance, particularly in the $\tau$-charm region, while considering initial state radiation. Despite some approximations in its derivation, the comparison between the analytical formula and direct integration of ISR shows good accuracy, indicating that the analytical formula meets current experimental requirements. Furthermore, the paper presents a comparison of the cross section between the analytical formula and calculations using the {\sc ConExc} Monte Carlo generator. The efficiency of the analytical formula in significantly reducing computing time makes it a favorable choice for the regression procedure to extract the parameters of narrow charmonium resonances in experiments.

hep-ph↗

arXiv2022

Investigation on intense axial magnetic field shielding with a large melt cast processed Bi-2212 tube

The feasibility of shielding axial magnetic fields up to 1.4~T, using a Bi-2212 hollow cylinder, is investigated at a temperature of 4.2~K. The residual magnetic flux density along the axis of the tube is measured at external fields of 1~T and 1.4~T. The shielding factor, defined as the ratio between the applied and the residual magnetic flux densities at the center of the tube, is measured to be $32\times 10^4$ at 1~T and $12\times 10^3$ at 1.4~T. The induced current density is evaluated from the measurements taking the thickness of the tube into account. The stability of the measurements over time is also addressed. Numerical simulations for the external and the residual magnetic flux densities are performed and compared to the experimental results. The study shows a high shielding performance of the Bi-2212 superconductor tube at 4.2~K up to 1.4~T.

physics.ins-det↗

arXiv2014

Background subtraction using probabilistic event weights

Background treatment is crucial to extract physics from precision experiments. In this paper, we introduce a novel method to assign each event a signal probability. This could then be used to weight the event's contribution to the likelihood during fitting. To illustrate the effect of this method, we test it with MC samples. The consistence between the constructed background and the background from MC truth shows that the background subtraction method with probabilistic event weights is feasible in partial wave analysis at BES III.

hep-ex↗