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Kun Liu

Kun Liu appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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hep-exComputer VisionArtificial Intelligencehep-phphysics.opticsquant-phcond-mat.mtrl-sciRoboticsSystems and ControlComputation and LanguageCryptography and SecurityDatabasesgr-qcmath.OCmath.STMethodologyMultimediaNeural and Evolutionary Computingphysics.geo-phStatistics Theory

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Published work

30 published item(s)

preprint2026arXiv

COMPAS: A Distributed Multi-Party SWAP Test for Parallel Quantum Algorithms

The limited number of qubits per chip remains a critical bottleneck in quantum computing, motivating the use of distributed architectures that interconnect multiple quantum processing units (QPUs). However, executing quantum algorithms across distributed systems requires careful co-design of algorithmic primitives and hardware architectures to manage circuit depth and entanglement overhead. We identify multivariate trace estimation as a key subroutine that is naturally suited for distribution, and broadly useful in tasks such as estimating Rényi entropies, virtual cooling and distillation, and certain applications of quantum signal processing. In this work, we introduce COMPAS, an architecture that realizes multivariate trace estimation across a multi-party network of interconnected modular and distributed QPUs by leveraging pre-shared entangled Bell pairs as resources. COMPAS adds only a constant depth overhead and consumes Bell pairs at a rate linear in circuit width, making it suitable for near-term hardware. Unlike other schemes, which must choose between asymptotic optimality in circuit depth or GHZ width, COMPAS achieves both at once. Additionally, we analyze network-level errors and simulate the effects of circuit-level noise on the architecture.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Detection of Earth's free oscillations utilizing TianQin

The measurement of Earth's free oscillations plays an important role in studying the Earth's large-scale structure. Space technology development presents a potential method to observe these normal modes by measuring inter-satellite distances. However, the disturbance from the Earth's low-degree gravity field makes it challenging for low Earth orbit gravity measurement satellites such as Gravity Recovery and Climate Experiment (GRACE) and TianQin-2 to extract signals from Earth's free oscillations directly. Here, we propose that by taking advantage of the high Earth orbit, the TianQin satellites can effectively avoid this disturbance, enabling direct measurement of Earth's free oscillations. We derive an analytical waveform to describe the response of Earth's free oscillations in TianQin. Based on this waveform, we use Bayesian analysis to extract the normal modes from numerical simulation data and perform parameter estimation. Our findings reveal that for a magnitude 7.9, Wenchuan-like earthquake, the resulting free oscillations will generate a signal that signal-to-noise ratio (SNR) is 73 in TianQin, and approximately 9 different modes can be distinguished. This result shows TianQin can open a new window to examine the Earth's free oscillations and study the Earth's interior and earthquakes independently from ground-based gravity measurement.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Measurements of the absolute branching fractions of the $Λ_{c}^{+}$ hadronic decays

Based on 4.5 fb$^{-1}$ of $e^+e^-$ collision data collected at center-of-mass energies between 4599.53 MeV and 4698.82 MeV with the BESIII detector, the absolute branching fractions of twelve $Λ_{c}^{+}$ hadronic decay modes are measured with a double-tag technique. A global least-square fit is implemented simultaneously among different decay modes at different energy points. This paper gives the most precise results on the branching fractions of different decay modes to date, with precision improved by a factor of 2 to 3. Among them, the branching fraction of $Λ_{c}^{+}\to pK^{-}π^+$ is determined to be $(6.61\pm0.11\pm0.12)\%$, where the first uncertainty is statistical and the second is systematic. In addition, the $e^+e^-\toΛ_c^+\barΛ_c^-$ Born cross sections and the effective form factors ($|G_{\rm eff}|$) at different energy points have been determined with the highest precision to date.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

What Limits Vision-and-Language Navigation ?

Vision-and-Language Navigation (VLN) is a cornerstone of embodied intelligence. However, current agents often suffer from significant performance degradation when transitioning from simulation to real-world deployment, primarily due to perceptual instability (e.g., lighting variations and motion blur) and under-specified instructions. While existing methods attempt to bridge this gap by scaling up model size and training data, we argue that the bottleneck lies in the lack of robust spatial grounding and cross-domain priors. In this paper, we propose StereoNav, a robust Vision-Language-Action framework designed to enhance real-world navigation consistency. To address the inherent gap between synthetic training and physical execution, we introduce Target-Location Priors as a persistent bridge. These priors provide stable visual guidance that remains invariant across domains, effectively grounding the agent even when instructions are vague. Furthermore, to mitigate visual disturbances like motion blur and illumination shifts, StereoNav leverages stereo vision to construct a unified representation of semantics and geometry, enabling precise action prediction through enhanced depth awareness. Extensive experiments on R2R-CE and RxR-CE demonstrate that StereoNav achieves state-of-the-art egocentric RGB performance, with SR and SPL scores of 81.1% and 68.3%, and 67.5% and 52.0%, respectively, while using significantly fewer parameters and less training data than prior scaling-based approaches. More importantly, real-world robotic deployments confirm that StereoNav substantially improves navigation reliability in complex, unstructured environments. Project page: https://yunheng-wang.github.io/stereonav-public.github.io.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Renormalizing Antiferroelectric Nanostripes in $β'-\mathrm{In}_{2}\mathrm{Se}_{3}$ via Optomechanics

Antiferroelectric (AFE) materials have received tremendous attention owing to their high energy conversion efficiency and good tunability. Recently, an exotic two-dimensional (2D) AFE material, $β'-\mathrm{In}_{2}\mathrm{Se}_{3}$ monolayer that could host atomically thin AFE nanostripe domains has been experimentally synthesized and theoretically examined. In this work, we apply first-principles calculations and theoretical estimations to predict that light irradiation can control the nanostripe width of such a system. We suggest that an intermediate near-infrared light (below bandgap) could effectively harness the thermodynamic Gibbs free energy, and the AFE nanostripe width will gradually reduce. We also propose to use an above bandgap linearly polarized light to generate AFE nanostripespecific photocurrent, providing an all-optical pump-probe setup for such AFE nanostripe width phase transitions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Baseline Framework for Part-level Action Parsing and Action Recognition

This technical report introduces our 2nd place solution to Kinetics-TPS Track on Part-level Action Parsing in ICCV DeeperAction Workshop 2021. Our entry is mainly based on YOLOF for instance and part detection, HRNet for human pose estimation, and CSN for video-level action recognition and frame-level part state parsing. We describe technical details for the Kinetics-TPS dataset, together with some experimental results. In the competition, we achieved 61.37% mAP on the test set of Kinetics-TPS.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

An Empirical Study and Analysis of Learning Generalizable Manipulation Skill in the SAPIEN Simulator

This paper provides a brief overview of our submission to the no interaction track of SAPIEN ManiSkill Challenge 2021. Our approach follows an end-to-end pipeline which mainly consists of two steps: we first extract the point cloud features of multiple objects; then we adopt these features to predict the action score of the robot simulators through a deep and wide transformer-based network. More specially, %to give guidance for future work, to open up avenues for exploitation of learning manipulation skill, we present an empirical study that includes a bag of tricks and abortive attempts. Finally, our method achieves a promising ranking on the leaderboard. All code of our solution is available at https://github.com/liu666666/bigfish\_codes.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Anisotropic terahertz optostriction in group-IV monochalcogenide compounds

Terahertz (THz) technology is a cutting-edge scheme with various promising applications, such as next generation telecommunication, non-destructive evaluation, security check, and in-depth characterization, owing to their sensitivity to material geometric change and good transparency. Even though tremendous progresses have been made during the past decade, exploration the mechanisms of THz-matter interaction microscopically is still in its infancy. In this work, we use thermodynamic theory to show how THz illumination deforms materials and use group-IV monochalcogenide compounds to illustrate it. According to our first-principles density functional theory calculations, THz light with intermediate intensity (~109 W/cm2) could yield elastic deformations on the order of ~0.1%, depending on laser polarization direction. Large anisotropic opto-mechanical responses are also revealed. Finally, we show that such strain can be detected via measuring the layer-resolved shift current under a probe light irradiation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

DarkQuest: A dark sector upgrade to SpinQuest at the 120 GeV Fermilab Main Injector

Expanding the mass range and techniques by which we search for dark matter is an important part of the worldwide particle physics program. Accelerator-based searches for dark matter and dark sector particles are a uniquely compelling part of this program as a way to both create and detect dark matter in the laboratory and explore the dark sector by searching for mediators and excited dark matter particles. This paper focuses on developing the DarkQuest experimental concept and gives an outlook on related enhancements collectively referred to as LongQuest. DarkQuest is a proton fixed-target experiment with leading sensitivity to an array of visible dark sector signatures in the MeV-GeV mass range. Because it builds off of existing accelerator and detector infrastructure, it offers a powerful but modest-cost experimental initiative that can be realized on a short timescale.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework

Action recognition from videos, i.e., classifying a video into one of the pre-defined action types, has been a popular topic in the communities of artificial intelligence, multimedia, and signal processing. However, existing methods usually consider an input video as a whole and learn models, e.g., Convolutional Neural Networks (CNNs), with coarse video-level class labels. These methods can only output an action class for the video, but cannot provide fine-grained and explainable cues to answer why the video shows a specific action. Therefore, researchers start to focus on a new task, Part-level Action Parsing (PAP), which aims to not only predict the video-level action but also recognize the frame-level fine-grained actions or interactions of body parts for each person in the video. To this end, we propose a coarse-to-fine framework for this challenging task. In particular, our framework first predicts the video-level class of the input video, then localizes the body parts and predicts the part-level action. Moreover, to balance the accuracy and computation in part-level action parsing, we propose to recognize the part-level actions by segment-level features. Furthermore, to overcome the ambiguity of body parts, we propose a pose-guided positional embedding method to accurately localize body parts. Through comprehensive experiments on a large-scale dataset, i.e., Kinetics-TPS, our framework achieves state-of-the-art performance and outperforms existing methods over a 31.10% ROC score.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation

Deep learning approaches to 3D shape segmentation are typically formulated as a multi-class labeling problem. Existing models are trained for a fixed set of labels, which greatly limits their flexibility and adaptivity. We opt for top-down recursive decomposition and develop the first deep learning model for hierarchical segmentation of 3D shapes, based on recursive neural networks. Starting from a full shape represented as a point cloud, our model performs recursive binary decomposition, where the decomposition network at all nodes in the hierarchy share weights. At each node, a node classifier is trained to determine the type (adjacency or symmetry) and stopping criteria of its decomposition. The features extracted in higher level nodes are recursively propagated to lower level ones. Thus, the meaningful decompositions in higher levels provide strong contextual cues constraining the segmentations in lower levels. Meanwhile, to increase the segmentation accuracy at each node, we enhance the recursive contextual feature with the shape feature extracted for the corresponding part. Our method segments a 3D shape in point cloud into an unfixed number of parts, depending on the shape complexity, showing strong generality and flexibility. It achieves the state-of-the-art performance, both for fine-grained and semantic segmentation, on the public benchmark and a new benchmark of fine-grained segmentation proposed in this work. We also demonstrate its application for fine-grained part refinements in image-to-shape reconstruction.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Both qubits of the singlet state can be steered simultaneously by multiple independent observers via sequential measurement

Quantum correlation is a fundamental property which distinguishes quantum systems from classical ones, and it is also a fragile resource under projective measurement. Recently, it has been shown that a subsystem in entangled pairs can share nonlocality with multiple observers in sequence. Here we present a new steering scenario where both subsystems are accessible by multiple observers. And it is found that the two qubits in singlet state can be simultaneously steered by two sequential observers, respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Expectation Synchronization Synthesis in Non-Markovian Open Quantum Systems

In this article, we investigate the problem of engineering synchronization in non-Markovian quantum systems. First, a time-convoluted linear quantum stochastic differential equation is derived which describes the Heisenberg evolution of a localized quantum system driven by multiple colored noise inputs. Then, we define quantum expectation synchronization in an augmented system consisting of two subsystems. We prove that, for two homogenous subsystems, synchronization can always be synthesized without designing direct Hamiltonian coupling given that the degree of non-Markovianity is below a certain threshold. System parameters are explicitly designed to achieve quantum synchronization. Also, a numerical example is presented to illustrate our results.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Real-time Action Representation with Temporal Encoding and Deep Compression

Deep neural networks have achieved remarkable success for video-based action recognition. However, most of existing approaches cannot be deployed in practice due to the high computational cost. To address this challenge, we propose a new real-time convolutional architecture, called Temporal Convolutional 3D Network (T-C3D), for action representation. T-C3D learns video action representations in a hierarchical multi-granularity manner while obtaining a high process speed. Specifically, we propose a residual 3D Convolutional Neural Network (CNN) to capture complementary information on the appearance of a single frame and the motion between consecutive frames. Based on this CNN, we develop a new temporal encoding method to explore the temporal dynamics of the whole video. Furthermore, we integrate deep compression techniques with T-C3D to further accelerate the deployment of models via reducing the size of the model. By these means, heavy calculations can be avoided when doing the inference, which enables the method to deal with videos beyond real-time speed while keeping promising performance. Our method achieves clear improvements on UCF101 action recognition benchmark against state-of-the-art real-time methods by 5.4% in terms of accuracy and 2 times faster in terms of inference speed with a less than 5MB storage model. We validate our approach by studying its action representation performance on four different benchmarks over three different tasks. Extensive experiments demonstrate comparable recognition performance to the state-of-the-art methods. The source code and the pre-trained models are publicly available at https://github.com/tc3d.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Fine-Grained Instance-Level Sketch-Based Video Retrieval

Existing sketch-analysis work studies sketches depicting static objects or scenes. In this work, we propose a novel cross-modal retrieval problem of fine-grained instance-level sketch-based video retrieval (FG-SBVR), where a sketch sequence is used as a query to retrieve a specific target video instance. Compared with sketch-based still image retrieval, and coarse-grained category-level video retrieval, this is more challenging as both visual appearance and motion need to be simultaneously matched at a fine-grained level. We contribute the first FG-SBVR dataset with rich annotations. We then introduce a novel multi-stream multi-modality deep network to perform FG-SBVR under both strong and weakly supervised settings. The key component of the network is a relation module, designed to prevent model over-fitting given scarce training data. We show that this model significantly outperforms a number of existing state-of-the-art models designed for video analysis.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Language Guided Networks for Cross-modal Moment Retrieval

We address the challenging task of cross-modal moment retrieval, which aims to localize a temporal segment from an untrimmed video described by a natural language query. It poses great challenges over the proper semantic alignment between vision and linguistic domains. Existing methods independently extract the features of videos and sentences and purely utilize the sentence embedding in the multi-modal fusion stage, which do not make full use of the potential of language. In this paper, we present Language Guided Networks (LGN), a new framework that leverages the sentence embedding to guide the whole process of moment retrieval. In the first feature extraction stage, we propose to jointly learn visual and language features to capture the powerful visual information which can cover the complex semantics in the sentence query. Specifically, the early modulation unit is designed to modulate the visual feature extractor's feature maps by a linguistic embedding. Then we adopt a multi-modal fusion module in the second fusion stage. Finally, to get a precise localizer, the sentence information is utilized to guide the process of predicting temporal positions. Specifically, the late guidance module is developed to linearly transform the output of localization networks via the channel attention mechanism. The experimental results on two popular datasets demonstrate the superior performance of our proposed method on moment retrieval (improving by 5.8\% in terms of Rank1@IoU0.5 on Charades-STA and 5.2\% on TACoS). The source code for the complete system will be publicly available.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Structural Combinatorial of Network Information System of Systems based on Evolutionary Optimization Method

The network information system is a military information network system with evolution characteristics. Evolution is a process of replacement between disorder and order, chaos and equilibrium. Given that the concept of evolution originates from biological systems, in this article, the evolution of network information architecture is analyzed by genetic algorithms, and the network information architecture is represented by chromosomes. Besides, the genetic algorithm is also applied to find the optimal chromosome in the architecture space. The evolutionary simulation is used to predict the optimal scheme of the network information architecture and provide a reference for system construction.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Generalized Jensen Inequalities with Application to Stability Analysis of Systems with Distributed Delays over Infinite Time-Horizons

The Jensen inequality has been recognized as a powerful tool to deal with the stability of time-delay systems. Recently, a new inequality that encompasses the Jensen inequality was proposed for the stability analysis of systems with finite delays. In this paper, we first present a generalized integral inequality and its double integral extension. It is shown how these inequalities can be applied to improve the stability result for linear continuous-time systems with gamma-distributed delays. Then, for the discrete-time counterpart we provide an extended Jensen summation inequality with infinite sequences, which leads to less conservative stability conditions for linear discrete-time systems with poisson-distributed delays. The improvements obtained thanks to the introduced generalized inequalities are demonstrated by examples.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Scalable SUM-Shrinkage Schemes for Distributed Monitoring Large-Scale Data Streams

In this article, motivated by biosurveillance and censoring sensor networks, we investigate the problem of distributed monitoring large-scale data streams where an undesired event may occur at some unknown time and affect only a few unknown data streams. We propose to develop scalable global monitoring schemes by parallel running local detection procedures and by combining these local procedures together to make a global decision based on SUM-shrinkage techniques. Our approach is illustrated in two concrete examples: one is the nonhomogeneous case when the pre-change and post-change local distributions are given, and the other is the homogeneous case of monitoring a large number of independent $N(0,1)$ data streams where the means of some data streams might shift to unknown positive or negative values. Numerical simulation studies demonstrate the usefulness of the proposed schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Networked control systems in the presence of scheduling protocols and communication delays

This paper develops the time-delay approach to Networked Control Systems (NCSs) in the presence of variable transmission delays, sampling intervals and communication constraints. The system sensor nodes are supposed to be distributed over a network. Due to communication constraints only one node output is transmitted through the communication channel at once. The scheduling of sensor information towards the controller is ruled by a weighted Try-Once-Discard (TOD) or by Round-Robin (RR) protocols. Differently from the existing results on NCSs in the presence of scheduling protocols (in the frameworks of hybrid and discrete-time systems), we allow the communication delays to be greater than the sampling intervals. A novel hybrid system model for the closed-loop system is presented that contains {\it time-varying delays in the continuous dynamics and in the reset conditions}. A new Lyapunov-Krasovskii method, which is based on discontinuous in time Lyapunov functionals is introduced for the stability analysis of the delayed hybrid systems. Polytopic type uncertainties in the system model can be easily included in the analysis. The efficiency of the time-delay approach is illustrated on the examples of uncertain cart-pendulum and of batch reactor.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Breaching Euclidean Distance-Preserving Data Perturbation Using Few Known Inputs

We examine Euclidean distance-preserving data perturbation as a tool for privacy-preserving data mining. Such perturbations allow many important data mining algorithms e.g. hierarchical and k-means clustering), with only minor modification, to be applied to the perturbed data and produce exactly the same results as if applied to the original data. However, the issue of how well the privacy of the original data is preserved needs careful study. We engage in this study by assuming the role of an attacker armed with a small set of known original data tuples (inputs). Little work has been done examining this kind of attack when the number of known original tuples is less than the number of data dimensions. We focus on this important case, develop and rigorously analyze an attack that utilizes any number of known original tuples. The approach allows the attacker to estimate the original data tuple associated with each perturbed tuple and calculate the probability that the estimation results in a privacy breach. On a real 16-dimensional dataset, we show that the attacker, with 4 known original tuples, can estimate an original unknown tuple with less than 7% error with probability exceeding 0.8.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Monte-Carlo-based electromagnetic modeling of nanoscale structures: accounting for inhomogeneous broadening in polydisperse ensembles

Many experimental systems consist of large ensembles of uncoupled or weakly interacting elements operating as a single whole; this is the case in many experimental systems in nano-optics and plasmonics including colloidal solutions, plasmonic nanoparticles, dielectric resonators, antenna arrays, and others. In such experiments, measurements of the optical spectra of ensembles will differ from measurements of the independent elements even if these elements are designed to be identical as a result of small variations from element to element, known as polydispersity. In particular, sharp spectral features arising from narrow-band resonances will tend to appear broader and can even be washed out completely. Here, we explore this effect of inhomogeneous broadening as it occurs in colloidal nano-polymers comprising self-assembled nanorod chains in solution. Using a technique combining finite-difference time-domain (FDTD) simulations and Monte-Carlo sampling, we predict the inhomogeneously-broadened optical spectra of these colloidal nano-polymers, and observe significant qualitative differences compared to the unbroadened spectra. The approach combining an electromagnetic simulation technique with Monte-Carlo sampling is widely applicable for quantifying the effects of inhomogeneous broadening in a variety of physical systems, including those with many degrees of freedom which are otherwise computationally intractable.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Evidence for the Direct Two-Photon Transition from $ψ(3686)$ to $J/ψ$

The two-photon transition $ψ(3686)\toγγJ/ψ$ is studied in a sample of 106 million $ψ(3686)$ decays collected by the BESIII detector. The branching fraction is measured to be $(3.1\pm0.6(\unit{stat})^{+0.8}_{-1.0}(\unit{syst})) \times10^{-4}$ using $J/ψ\to e^+e^-$ and $J/ψ\toμ^+μ^-$ decays, and its upper limit is estimated to be $4.5\times10^{-4}$ at the 90% conference level. This work represents the first measurement of a two-photon transition among charmonium states. The orientation of the $ψ(3686)$ decay plane and the $J/ψ$ polarization in this decay are also studied. In addition, the product branching fractions of sequential $E1$ transitions $ψ(3686)\toγχ_{cJ}, χ_{cJ}\toγJ/ψ(J=0,1,2)$ are reported.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Experimental study of psi' decays to K+K- pi^0 and K+K- eta

Using $(106\pm4)\times 10^6$ $\psip$ events accumulated with the BESIII detector at the BEPCII $e^+e^-$ collider, we present measurements of the branching fractions for psi' decays to $K^{+}K^{-}π^{0}$ and $K^{+}K^{-}η$. In these final states, the decay $ψ'\to K_2^{*}(1430)^+K^-+c.c.$ is observed for the first time, and its branching fraction is measured to be $(7.12\pm{0.62}{\rm (stat.)}^{+1.13}_{-0.61}{\rm (syst.)})\times 10^{-5}$, which indicates a violation of the helicity selection rule in $\psip$ decays. The branching fractions of $ψ'\to K^*(892)^+K^-+c.c., ϕη, ϕπ^0$ are also measured. The measurements are used to test the QCD predictions on charmonium decays.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

First observation of the isospin violating decay $J/ψ\rightarrow Λ\barΣ^{0}+c.c.$

Using a sample of $(225.2\pm 2.8)\times 10^6$ $J/ψ$ events collected with the BESIII detector, we present results of a study of $J/ψ\rightarrow γΛ\barΛ$ and report the first observation of the isospin violating decay $J/ψ\rightarrowΛ\barΣ^{0}+c.c.$, in which $\barΣ^{0}$ decays to $γ\barΛ$. The measured branching fractions are $\mathcal{B}(J/ψ\rightarrow\barΛΣ^{0}$) = $(1.46\pm0.11\pm0.12) \times10^{-5}$ and $\mathcal{B}(J/ψ\rightarrowΛ\bar{Σ^{0}}$) = $(1.37\pm0.12\pm0.11) \times10^{-5}$. We search for $Λ(1520) \rightarrow γΛ$ decay, and find no evident signal, and an upper limit for the product branching fraction $\mathcal{B}(J/ψ\rightarrowΛ\barΛ(1520)+c.c.)\times \mathcal{B}(Λ(1520)\rightarrowγΛ)<4.1 \times10^{-6}$ is set at the 90% confidence level. We also report the observation of $η_{c}\rightarrowΛ\barΛ$ in $J/ψ\rightarrow γη_{c}$, $η_{c}\rightarrowΛ\barΛ$ and measure the branching fraction $\mathcal{B}(η_{c}\rightarrowΛ\barΛ) =(1.16\pm0.12 (stat)\pm0.19(syst)\pm0.28(PDG))\times10^{-3}$.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

First observation of the M1 transition $ψ(3686)\to γη_c(2S)$

Using a sample of 106 million ψ(3686) events collected with the BESIII detector at the BEPCII storage ring, we have made the first measurement of the M1 transition between the radially excited charmonium S-wave spin-triplet and the radially excited S-wave spin-singlet states: ψ(3686)\toγη_c(2S). Analyses of the processes ψ(2S)\to γη_c(2S) with η_c(2S)\to \K_S^0 Kπand K^+K^-π^0 gave an η_c(2S) signal with a statistical significance of greater than 10 standard deviations under a wide range of assumptions about the signal and background properties. The data are used to obtain measurements of the η_c(2S) mass (M(η_c(2S))=3637.6\pm 2.9_\mathrm{stat}\pm 1.6_\mathrm{sys} MeV/c^2), width (Γ(η_c(2S))=16.9\pm 6.4_\mathrm{stat}\pm 4.8_\mathrm{sys} MeV), and the product branching fraction (\BR(ψ(3686)\to γη_c(2S))\times \BR(η_c(2S)\to K\bar Kπ) = (1.30\pm 0.20_\mathrm{stat}\pm 0.30_\mathrm{sys})\times 10^{-5}). Combining our result with a BaBar measurement of \BR(η_c(2S)\to K\bar K π), we find the branching fraction of the M1 transition to be \BR(ψ(3686)\toγη_c(2S)) = (6.8\pm 1.1_\mathrm{stat}\pm 4.5_\mathrm{sys})\times 10^{-4}.

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preprint2012arXiv

Study of $J/ψ\to p\bar{p}$ and $J/ψ\to n\bar{n}$

The decays $J/ψ\to p\bar{p}$ and $J/ψ\to n\bar{n}$ have been investigated with a sample of 225.2 million $J/ψ$ events collected with the BESIII detector at the BEPCII $e^+e^-$ collider. The branching fractions are determined to be $\mathcal{B}(J/ψ\to p\bar{p})=(2.112\pm0.004\pm0.031)\times10^{-3}$ and $\mathcal{B}(J/ψ\to n\bar{n})=(2.07\pm0.01\pm0.17)\times10^{-3}$. Distributions of the angle $θ$ between the proton or anti-neutron and the beam direction are well described by the form $1+α\cos^2θ$, and we find $α=0.595\pm0.012\pm0.015$ for $J/ψ\to p\bar{p}$ and $α=0.50\pm0.04\pm0.21$ for $J/ψ\to n\bar{n}$. Our branching-fraction results suggest a large phase angle between the strong and electromagnetic amplitudes describing the $J/ψ\to N\bar{N}$ decay.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Two-photon widths of the $χ_{c0, 2}$ states and helicity analysis for $χ_{c2}\arγγ$}

Based on a data sample of 106 M $ψ^{\prime}$ events collected with the BESIII detector, the decays $ψ^{\prime}\arγχ_{c0, 2}$,$χ_{c0, 2}\arγγ$ are studied to determine the two-photon widths of the $χ_{c0, 2}$ states. The two-photon decay branching fractions are determined to be ${\cal B}(χ_{c0}\arγγ) = (2.24\pm 0.19\pm 0.12\pm 0.08)\times 10^{-4}$ and ${\cal B}(χ_{c2}\arγγ) = (3.21\pm 0.18\pm 0.17\pm 0.13)\times 10^{-4}$. From these, the two-photon widths are determined to be $Γ_{γγ}(χ_{c0}) = (2.33\pm0.20\pm0.13\pm0.17)$ keV, $Γ_{γγ}(χ_{c2}) = (0.63\pm0.04\pm0.04\pm0.04)$ keV, and $\cal R$ $=Γ_{γγ}(χ_{c2})/Γ_{γγ}(χ_{c0})=0.271\pm 0.029\pm 0.013\pm 0.027$, where the uncertainties are statistical, systematic, and those from the PDG ${\cal B}(ψ^{\prime}\arγχ_{c0,2})$ and $Γ(χ_{c0,2})$ errors, respectively. The ratio of the two-photon widths for helicity $λ=0$ and helicity $λ=2$ components in the decay $χ_{c2}\arγγ$ is measured for the first time to be $f_{0/2} =Γ^{λ=0}_{γγ}(χ_{c2})/Γ^{λ=2}_{γγ}(χ_{c2}) = 0.00\pm0.02\pm0.02$.

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preprint2010arXiv

Significantly enhanced second order nonlinearity in domain walls of ferroelectrics

Significantly enhanced second order nonlinearity resulting from local symmetry breaking in domain walls is reported. By this new giant nonlinearity, novel hexagonally, conically and trigonally distributed second harmonic beams closely related to domain wall patterns are generated without contribution of reciprocal vectors. Experimental results show that such nonlinearities have made important impact on quasi-phase-matching technique and electro-optic Solc filter, which needs careful reconsiderations.

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