arXiv subjects

Yifan Fei

Publications and source records attributed to Yifan Fei.

7 recordsLinked to original sources

arXiv2026

Z Boson Radiative Decay $Z\to \mu^+ \mu^- \gamma$ at the LHC

We study the radiative decay of the $Z$ boson, $Z \to \mu^+\mu^-\gamma$, at the LHC, providing both Standard Model (SM) precision analysis and new physics projections. With detailed analysis of Run-2 and future HL-LHC performances, we demonstrate that this decay mode can be measured with a statistical precision at the sub-percentage level. From existing Run-1 data, we extract $\text{Br}^\text{fid}(Z \to \mu\mu\gamma) = (3.34 \pm 0.016)\times 10^{-4}$. We further explore the sensitivity of this channel to axion-like particles (ALPs) and to an anomalous $U(1)_X$ gauge boson coupled to the muon. Both scenarios feature resonant structures in the dimuon invariant mass spectrum within the $Z \to a/X + \gamma \to \mu^+\mu^-\gamma$ final state. Our results show that the radiative $Z$ decay provides a clean and statistically powerful probe of such leptophilic new physics, extending the current collider reach for ALPs and anomalous gauge forces down to $g_X \sim \mathcal{O}(10^{-3})$. This study highlights the potential of rare electroweak gauge boson decays as precision tests of the SM and sensitive probes of new interactions at the LHC.

hep-ph↗

arXiv2024

$W$-Boson Exotic Decay into Three Charged Leptons at the LHC

We investigate the $W$ boson's exotic decay channel, $W \rightarrow \ell\ell\ell ν$, at the LHC. Although the four-body final states suppress the decay branching ratio, the large production of $W$ bosons makes detecting and precisely measuring this decay probability entirely feasible. Our simulation study indicates that this tiny branching ratio can be measured with sub-percent precision at the HL-LHC. This decay channel can also constrain Standard Model extensions. Using the $ L_μ-L_τ$ model as a benchmark, we find that the current bound on the gauge coupling for $Z'$ mass in the range of $[4,75]$ GeV can significantly improve.

hep-ph↗

arXiv2024

Collisional dynamics of symmetric two-dimensional quantum droplets

The collisional dynamics of two symmetric droplets with equal intraspecies scattering lengths and particle number density for each component is studied by solving the corresponding extended Gross-Pitaevskii equation in two dimensions by including a logarithmic correction term in the usual contact interaction. We find the merging droplet after collision experiences a quadrupole oscillation in its shape and the oscillation period is found to be independent of the incidental momentum for small droplets. With increasing collision momentum the colliding droplets may separate into two, or even more, and finally into small pieces of droplets. For these dynamical phases, we manage to present boundaries determined by the remnant particle number in the central area and the damped oscillation of the quadrupole mode. A stability peak for the existence of droplets emerges at the critical particle number $N_c \simeq 48$ for the quasi-Gaussian and flat-top shapes of the droplets.

cond-mat.quant-gas↗

arXiv2024

Collective excitations in two-dimensional harmonically trapped quantum droplets

The collective excitation modes in quantum droplets trapped in a two-dimensional harmonic potential in the context of symmetric weakly interacting binary bosonic mixtures are studied. By utilizing the linearization technique, the time-dependent extended Gross-Pitaevskii equation, and a sum-rule approach with a variational approximation, the ground state properties and collective excitations of such a two-dimensional quantum system are investigated for various system parameters. We present comprehensive analysis and calculations on the effect of the confinement strength and anisotropy of the trapping potential, the number of atoms in the droplet, and the collective excitation modes. The radius of the droplet, as well as the chemical potential, is non-monotonically related to the number of atoms in the droplet, and the confinement tends to shift the minimum values towards the ideal gas limit. The excitation frequency peaks, which are prominent in a self-bounded droplet, become less pronounced and smoother when subjected to a strong trapping potential. The sum-rule approach fails to reproduce the breathing mode frequency for a moderate number of atoms in a weak trapping potential, however, works perfectly well in a strong confinement. It was found that the anisotropy in the trap eliminates the degeneracy between the quadrupole and scissors modes that occurs in an isotropic trap, causing the frequencies of these two modes to immediately diverge from each other for any degree of anisotropy. These findings provide valuable insights into the unique characteristics and behavior of quantum droplets, offering potential implications for future research and applications in the dynamic behaviors of intriguing quantum droplets.

cond-mat.quant-gas↗

arXiv2023

Ground-state Properties and Bogoliubov Modes of a Harmonically Trapped One-Dimensional Quantum Droplet

We study the stationary and excitation properties of a one-dimensional quantum droplet in the two-component Bose mixture trapped in a harmonic potential. By constructing the energy functional for the inhomogeneous mixture, we elaborate the extended the Gross-Pitaevskii equation applicable to both symmetric and asymmetric mixtures into a universal form, and the equations in two different dimensionless schemes are in a duality relation, i.e. the unique parameters left are inverse of each other. The Bogoliubov equations for the trapped droplet are obtained by linearizing the small density fluctuation around the ground state and the low-lying excitation modes are calculated numerically.It is found that the confinement trap changes easily the flat-top structure for large droplets and alters the mean square radius and the chemical potential intensively. The breathing mode of the confined droplet connects the self-bound and ideal gas limits, with the excitation in the weakly interacting Bose condensate for large particle numbers lying in between. We explicitly show how the continuum spectrum of the excitation is split into discrete modes, and finally taken over by the harmonic trap. Two critical particle numbers are identified by the minimum size of the trapped droplet and the maximum breathing mode energy, both of which are found to decrease exponentially with the trapping parameter.

cond-mat.quant-gas↗

arXiv2023

Hysteretic Behavior Simulation Based on Pyramid Neural Network:Principle, Network Architecture, Case Study and Explanation

An accurate and efficient simulation of the hysteretic behavior of materials and components is essential for structural analysis. The surrogate model based on neural networks shows significant potential in balancing efficiency and accuracy. However, its serial information flow and prediction based on single-level features adversely affect the network performance. Therefore, a weighted stacked pyramid neural network architecture is proposed herein. This network establishes a pyramid architecture by introducing multi-level shortcuts to integrate features directly in the output module. In addition, a weighted stacked strategy is proposed to enhance the conventional feature fusion method. Subsequently, the redesigned architectures are compared with other commonly used network architectures. Results show that the redesigned architectures outperform the alternatives in 87.5% of cases. Meanwhile, the long and short-term memory abilities of different basic network architectures are analyzed through a specially designed experiment, which could provide valuable suggestions for network selection.

cs.LG↗

arXiv2023

Iterative self-transfer learning: A general methodology for response time-history prediction based on small dataset

There are numerous advantages of deep neural network surrogate modeling for response time-history prediction. However, due to the high cost of refined numerical simulations and actual experiments, the lack of data has become an unavoidable bottleneck in practical applications. An iterative self-transfer learningmethod for training neural networks based on small datasets is proposed in this study. A new mapping-based transfer learning network, named as deep adaptation network with three branches for regression (DAN-TR), is proposed. A general iterative network training strategy is developed by coupling DAN-TR and the pseudo-label strategy, and the establishment of corresponding datasets is also discussed. Finally, a complex component is selected as a case study. The results show that the proposed method can improve the model performance by near an order of magnitude on small datasets without the need of external labeled samples,well behaved pre-trainedmodels, additional artificial labeling, and complex physical/mathematical analysis.

cs.LG↗