arXiv subjects

Meng Wen

Publications and source records attributed to Meng Wen.

At least 19 recordsLinked to original sources

arXiv2025

Enhanced MVA of Polarized Proton Beams via PW Laser-Driven Plasma Bubble

The significance of laser-driven polarized beam acceleration has been increasingly recognized in recent years. We propose an efficient method for generating polarized proton beams from a pre-polarized hydrogen halide gas jet, utilizing magnetic vortex acceleration enhanced by a laser-driven plasma bubble. When a petawatt laser pulse passes through a pre-polarized gas jet, a bubble-like ultra-nonlinear plasma wave is formed. As part of the wave particles, background protons are swept by the acceleration field of the bubble and oscillate significantly along the laser propagation axis. Some of the pre-accelerated protons in the plasma wave are trapped by the acceleration field at the rear side of the target. This acceleration field is intensified by the transverse expansion of the laser-driven magnetic vortex, resulting in energetic polarized proton beams. The spin of energetic protons is determined by their precession within the electromagnetic field, as described by the Thomas-Bargmann-Michel-Telegdi equation in analytical models and particle-in-cell simulations. Multidimensional simulations reveal that monoenergetic proton beams with hundreds of MeV in energy, a beam charge of hundreds of pC, and a beam polarization of tens of percent can be produced at laser powers of several petawatts. Laser-driven polarized proton beams offer promising potential for application in polarized beam colliders, where they can be utilized to investigate particle interactions and to explore the properties of matter under unique conditions.

physics.plasm-ph↗

arXiv2023

A decomposition for transverse spins in structured vector fields

Classical vector waves can possess intricate spin angular momenta (SAM), which are \emph{perpendicular} to the propagation direction, as revealed by the recent recognition of surprisingly transverse SAM in electromagnetic (EM) fields. In this paper, we employ the Hertz potential method to define structured vector fields and analytically decompose the SAM of the wave fields in two parts. Our novel approach of decomposition not only confirms that transverse SAM may originate from the first-order spatial inhomogeneity of the Poynting momentum, but also points out that for \emph{non-planar vector waves with near fields}, an extraordinary spin appears as a distinct part out of transverse spin. By four examples of vector beams, we further demonstrate that the proposed transverse spins prevail universally in both propagating and evanescent waves. This work renews our fundamental understanding of the decomposition of SAM for classical vector waves.

physics.optics↗

arXiv2020

Spin polarized proton beam generation from gas-jet targets by intense laser pulses

A method of generating spin polarized proton beams from a gas jet by using a multi-petawatt laser is put forward. With currently available techniques of producing pre-polarized monatomic gases from photodissociated hydrogen halide molecules and petawatt lasers, proton beams with energy ~ 50 MeV and ~ 80 % polarization are proved to be obtained. Two-stage acceleration and spin dynamics of protons are investigated theoretically and by means of fully self-consistent three dimensional particle-in-cell simulations. Our results predict the dependence of the beam polarization on the intensity of the driving laser pulse. Generation of bright energetic polarized proton beams would open a domain of polarization studies with laser driven accelerators, and have potential application to enable effective detection in explorations of quantum chromodynamics.

physics.plasm-ph↗

arXiv2020

A cosmic Zevatron based on cyclotron auto-resonance

A Zevatron is an accelerator scheme envisaged to accelerate particles to ZeV energies (1 ZeV = $10^{21}$ eV). Schemes, most notably the internal shock model, have been proposed to explain the acceleration of ultra-high-energy cosmic-ray (UHECR) particles that have been sporadically detected reaching Earth since 1962. Here, the cyclotron auto-resonance acceleration (CARA) mechanism is tailored and used to demonstrate possible acceleration of particles ejected as a result of violent astrophysical processes such as the merger of a binary system or a supernova explosion. Such events result in emission of highly-energetic particles and ultra-intense beamed radiation. In the simultaneous presence of a super-strong magnetic field, the condition for cyclotron auto-resonance may be met. Thus CARA can act like a {\it booster} for particles pre-accelerated inside their progenitor by shock waves, possibly among other means. As examples, it is shown that nuclei of hydrogen, helium, and iron-56, may reach ZeV energies by CARA, under which conditions the particles, while gyrating around the lines of an ultra-strong magnetic field, also surf on the waves of a super-intense radiation field. When radiation-reaction is taken into account, it is shown that the ZeV energy gained by a particle can fall off by less than an order-of-magnitude if the resonance condition is missed by roughly less than 20\%.

astro-ph.HE↗

arXiv2019

Electron acceleration in direct laser-solid interactions far beyond the ponderomotive limit

In laser-solid interactions, electrons may be generated and subsequently accelerated to energies of the order-of-magnitude of the ponderomotive limit, with the underlying process dominated by direct laser acceleration. Breaking this limit, realized here by a radially-polarized laser pulse incident upon a wire target, can be associated with several novel effects. Three-dimensional Particle-In-Cell simulations show a relativistic intense laser pulse can extract electrons from the wire and inject them into the accelerating field. Anti-dephasing, resulting from collective plasma effects, are shown here to enhance the accelerated electron energy by two orders of magnitude compared to the ponderomotive limit. It is demonstrated that ultra-short radially polarized pulses produce super-ponderomotive electrons more efficiently than pulses of the linear and circular polarization varieties.

physics.plasm-ph↗

arXiv2019

Nonconvex fraction function recovery sparse signal by convex optimization algorithm

In this paper, we will generate a convex iterative FP thresholding algorithm to solve the problem $(FP^λ_{a})$. Two schemes of convex iterative FP thresholding algorithms are generated. One is convex iterative FP thresholding algorithm-Scheme 1 and the other is convex iterative FP thresholding algorithm-Scheme 2. A global convergence theorem is proved for the convex iterative FP thresholding algorithm-Scheme 1. Under an adaptive rule, the convex iterative FP thresholding algorithm-Scheme 2 will be adaptive both for the choice of the regularized parameter $λ$ and parameter $a$. These are the advantages for our two schemes of convex iterative FP thresholding algorithm compared with our previous proposed two schemes of iterative FP thresholding algorithm. At last, we provide a series of numerical simulations to test the performance of the convex iterative FP thresholding algorithm-Scheme 2, and the simulation results show that our convex iterative FP thresholding algorithm-Scheme 2 performs very well in recovering a sparse signal.

math.OC↗

arXiv2019

A non-convex approach to low-rank and sparse matrix decomposition

In this paper, we develop a nonconvex approach to the problem of low-rank and sparse matrix decomposition. In our nonconvex method, we replace the rank function and the $l_{0}$-norm of a given matrix with a non-convex fraction function on the singular values and the elements of the matrix respectively. An alternative direction method of multipliers algorithm is utilized to solve our proposed nonconvex problem with the nonconvex fraction function penalty. Numerical experiments on some low-rank and sparse matrix decomposition problems show that our method performs very well in recovering low-rank matrices which are heavily corrupted by large sparse errors.

math.OC↗

arXiv2019

Polarized laser-wakefield-accelerated kiloampere electron beams

High-flux polarized particle beams are of critical importance for the investigation of spin-dependent processes, such as in searches of physics beyond the Standard Model, as well as for scrutinizing the structure of solids and surfaces in material science. Here we demonstrate that kiloampere polarized electron beams can be produced via laser-wakefield acceleration from a gas target. A simple theoretical model for determining the electron beam polarization is presented and supported with self-consistent three-dimensional particle-in-cell simulations that incorporate the spin dynamics. By appropriately choosing the laser and gas parameters, we show that the depolarization of electrons induced by the laser-wakefield-acceleration process can be as low as 10%. Compared to currently available sources of polarized electron beams, the flux is increased by four orders of magnitude.

physics.plasm-ph↗

arXiv2018

A New Nonconvex Strategy to Affine Matrix Rank Minimization Problem

The affine matrix rank minimization (AMRM) problem is to find a matrix of minimum rank that satisfies a given linear system constraint. It has many applications in some important areas such as control, recommender systems, matrix completion and network localization. However, the problem (AMRM) is NP-hard in general due to the combinational nature of the matrix rank function. There are many alternative functions have been proposed to substitute the matrix rank function, which lead to many corresponding alternative minimization problems solved efficiently by some popular convex or nonconvex optimization algorithms. In this paper, we propose a new nonconvex function, namely, $TL_α^ε$ function (with $0\leqα<1$ and $ε>0$), to approximate the rank function, and translate the NP-hard problem (AMRM) into the $TL_{p}^ε$ function affine matrix rank minimization (TLAMRM) problem. Firstly, we study the equivalence of problem (AMRM) and (TLAMRM), and proved that the uniqueness of global minimizer of the problem (TLAMRM) also solves the NP-hard problem (AMRM) if the linear map $\mathcal{A}$ satisfies a restricted isometry property (RIP). Secondly, an iterative thresholding algorithm is proposed to solve the regularization problem (RTLAMRM) for all $0\leqα<1$ and $ε>0$. At last, some numerical results on low-rank matrix completion problems illustrated that our algorithm is able to recover a low-rank matrix, and the extensive numerical on image inpainting problems shown that our algorithm performs the best in finding a low-rank image compared with some state-of-art methods.

math.OC↗

arXiv2018

Iterative thresholding algorithm based on non-convex method for modified lp-norm regularization minimization

Recently, the $ł_{p}$-norm regularization minimization problem $(P_{p}^λ)$ has attracted great attention in compressed sensing. However, the $ł_{p}$-norm $\|x\|_{p}^{p}$ in problem $(P_{p}^λ)$ is nonconvex and non-Lipschitz for all $p\in(0,1)$, and there are not many optimization theories and methods are proposed to solve this problem. In fact, it is NP-hard for all $p\in(0,1)$ and $λ>0$. In this paper, we study two modified $ł_{p}$ regularization minimization problems to approximate the NP-hard problem $(P_{p}^λ)$. Inspired by the good performance of Half algorithm and $2/3$ algorithm in some sparse signal recovery problems, two iterative thresholding algorithms are proposed to solve the problems $(P_{p,1/2,ε}^λ)$ and $(P_{p,2/3,ε}^λ)$ respectively. Numerical results show that our algorithms perform effectively in finding the sparse signal in some sparse signal recovery problems for some proper $p\in(0,1)$.

math.OC↗

arXiv2018

Sparse Portfolio Selection via Non-convex Fraction Function

In this paper, a continuous and non-convex promoting sparsity fraction function is studied in two sparse portfolio selection models with and without short-selling constraints. Firstly, we study the properties of the optimal solution to the problem $(FP_{a,λ,η})$ including the first-order and the second optimality condition and the lower and upper bound of the absolute value for its nonzero entries. Secondly, we develop the thresholding representation theory of the problem $(FP_{a,λ,η})$. Based on it, we prove the existence of the resolvent operator of gradient of $P_{a}(x)$, calculate its analytic expression, and propose an iterative fraction penalty thresholding (IFPT) algorithm to solve the problem $(FP_{a,λ,η})$. Moreover, we also prove that the value of the regularization parameter $λ>0$ can not be chosen too large. Indeed, there exists $\barλ>0$ such that the optimal solution to the problem $(FP_{a,λ,η})$ is equal to zero for any $λ>\barλ$. At last, inspired by the thresholding representation theory of the problem $(FP_{a,λ,η})$, we propose an iterative nonnegative fraction penalty thresholding (INFPT) algorithm to solve the problem $(FP_{a,λ,η}^{\geq})$. Empirical results show that our methods, for some proper $a>0$, perform effective in finding the sparse portfolio weights with and without short-selling constraints.

math.OC↗

arXiv2017

Recovering Sparse Nonnegative Signals via Non-convex Fraction Function Penalty

Many real world practical problems can be formulated as $\ell_{0}$-minimization problems with nonnegativity constraints, which seek the sparsest nonnegative signals to underdetermined linear systems. They have been widely applied in signal and image processing, machine learning, pattern recognition and computer vision. Unfortunately, this $\ell_{0}$-minimization problem with nonnegativity constraint is computational and NP-hard because of the discrete and discontinuous nature of the $\ell_{0}$-norm. In this paper, we replace the $\ell_{0}$-norm with a non-convex fraction function, and study the minimization problem of this non-convex fraction function in recovering the sparse nonnegative signals from an underdetermined linear system. Firstly, we discuss the equivalence between $(P_{0}^{\geq})$ and $(FP_{a}^{\geq})$, and the equivalence between $(FP_{a}^{\geq})$ and $(FP_{a,λ}^{\geq})$. It is proved that the optimal solution of the problem $(P_{0}^{\geq})$ could be approximately obtained by solving the regularization problem $(FP_{a,λ}^{\geq})$ if some specific conditions satisfied. Secondly, we propose a nonnegative iterative thresholding algorithm to solve the regularization problem $(FP_{a,λ}^{\geq})$ for all $a>0$. Finally, some numerical experiments on sparse nonnegative siganl recovery problems show that our method performs effective in finding sparse nonnegative signals compared with the linear programming.

math.OC↗

arXiv2017

Spin-one-half particles in strong electromagnetic fields: spin effects and radiation reaction

Various classical models of electrons including their spin degrees of freedom are commonly applied to describe the electron dynamics in strong electromagnetic fields. We demonstrate that different models can lead to different or even contradicting predictions regarding how the spin degree of freedom modifies the electron's orbital motion when the electron moves in strong electromagnetic fields. This discrepancy is rooted in the model-specific energy dependency of the spin-induced Stern-Gerlach force acting on the electron. The Frenkel model and the classical Foldy-Wouthuysen model are compared exemplarily in the nonrelativistic and the relativistic limits in order to identify parameter regimes where these classical models make different predictions. This allows for experimental tests of these models. In ultrastrong laser setups in parameter regimes where effects of the Stern-Gerlach force become relevant, radiation-reaction effects are also expected to set in. We incorporate the radiation reaction classically via the Landau-Lifshitz equation and demonstrate that although radiation-reaction effects can have a significant effect on the electron trajectory, the Frenkel model and the classical Foldy-Wouthuysen model remain distinguishable also if radiation-reaction effects are taken into account. Our calculations are also suitable to verify the Landau-Lifshitz equation for the radiation reaction of electrons and other spin-1/2 particles.

physics.plasm-ph↗

arXiv2016

A randomized intertial primal-dual fixed point algorithm for monotone inclusions

In this paper, we propose a randomized intertial block-coordinate primaldual fixed point algorithm to solve a wide array of monotone inclusion problems base on the modification of the heavy ball method of Nesterov. These methods rely on a sweep of blocks of variables which are activated at each iteration according to a random rule. To this end we formulate the inertial version of the Krasnosel'skii-Mann algorithm for approximating the set of fixed points of a quasinonexpansive operator, for which we also provide an exhaustive convergence analysis. As a by-product, we can obtain some intertial block-coordinate operator splitting methods for solving composite monotone inclusion and convex minimization problems.

math.OC↗

arXiv2016

Identifying the Stern-Gerlach force of classical electron dynamics

Different classical theories are commonly applied in various branches of physics to describe the relativistic dynamics of electrons by coupled equations for the orbital motion and spin precession. Exemplarily, we benchmark the Frenkel model and the classical Foldy-Wouthuysen model with spin-dependent forces (Stern-Gerlach forces) to the quantum dynamics as predicted by the Dirac equation. Both classical theories can lead to different or even contradicting predictions how the Stern-Gerlach forces modify the electron's orbital motion, when the electron moves in strong electromagnetic field configurations of emerging high-intensity laser facilities. In this way, one may evaluate the validity and identify the limits of these classical theories via a comparison with possible experiments to provide a proper description of spin-induced dynamics. Our results indicate that the Foldy-Wouhuysen model is qualitatively in better agreement with the Dirac theory than the widely used Frenkel model.

physics.plasm-ph↗

arXiv2016

A stochastic coordinate descent inertial primal-dual algorithm for large-scale composite optimization

We consider an inertial primal-dual algorithm to compute the minimizations of the sum of two convex functions and the composition of another convex function with a continuous linear operator. With the idea of coordinate descent, we design a stochastic coordinate descent inertial primal-dual splitting algorithm. Moreover, in order to prove the convergence of the proposed inertial algorithm, we formulate first the inertial version of the randomized Krasnosel'skii-Mann iterations algorithm for approximating the set of fixed points of a nonexpansive operator and investigate its convergence properties. Then the convergence of stochastic coordinate descent inertial primal-dual splitting algorithm is derived by applying the inertial version of the randomized Krasnosel'skii-Mann iterations to the composition of the proximity operator.

math.OC↗

arXiv2016

Efficient primal-dual fixed point algorithm with dynamic stepsize for convex problems with applications to imaging restoration

We consider the problem of finding the minimization of the sum of a convex function and the composition of another convex function with a continuous linear operator from the view of fixed point algorithms based on proximity operators. We design a primal-dual fixed point algorithm with dynamic stepsize based on the proximity operator and obtain a scheme with a closed form solution for each iteration. Based on Modified Mann iteration and the firmly nonexpansive properties of the proximity operator, we achieve the convergence of the proposed algorithm.

math.OC↗

arXiv2016

An inertial primal-dual fixed point algorithm for composite optimization problems

We consider an inertial primal-dual fixed point algorithm (IPDFP) to compute the minimizations of the following Problem (1.1). This is a full splitting approach, in the sense that the nonsmooth functions are processed individually via their proximity operators. The convergence of the IPDFP is obtained by reformulating the Problem (1.1) to the sum of three convex functions. This work brings together and notably extends several classical splitting schemes, like the primaldual method proposed by Chambolle and Pock, and the recent proximity algorithms of Charles A. et al designed for the L1/TV image denoising model. The iterative algorithm is used for solving nondifferentiable convex optimization problems arising in image processing. The experimental results indicate that the proposed IPDFP iterative algorithm performs well with respect to state-of-the-art methods.

math.OC↗