Search

Your search keyword '"H. Gu"' showing total 7,551 results
Start Over Author "H. Gu"
7,551 results on '"H. Gu"'

1. The polarimetric characteristics of dust with irregular shapes: evaluation of the spheroid model for single particles

Author

J. Luo, Z. Li, C. Fan, H. Xu, Y. Zhang, W. Hou, L. Qie, H. Gu, M. Zhu, Y. Li, and K. Li

Subjects
Environmental engineering, TA170-171, Earthwork. Foundations, TA715-787
Abstract

In the atmosphere, the dust shapes are various, and a single model is difficult to represent the complex shapes of dust. We proposed a tunable model to represent dust with various shapes. Two tunable parameters were used to represent the effects of the erosion degree and binding forces from the mass center, respectively. Thus, the model can represent various dust shapes by adjusting the tunable parameters. To evaluate the applicability of the single spheroid model in calculating the optical properties of single dust with irregular shapes, the aspect ratios of spheroids were retrieved by best fitting the phase function of dust with irregular shapes. In this work, the optical properties and polarimetric characteristics of irregular dust with a diameter range of 0.2–2.0 µm were investigated. Our findings show that the dust shapes have a substantial impact on the scattering matrix, and sometimes the sign of elements of the scattering matrix could be modified by changing the tunable parameters. The applicability of the spheroid model is significantly affected by the erosion degree and binding forces, and substantial deviations could be observed when the dust diameter is in the range of 0.8–2.0 µm. The F11 relative differences of approximately 100 % between dust with irregular shapes and best-fitted spheroids could be observed in certain scattering angles. The maximum differences in other elements between irregular dust particles and best-fitted spheroids can reach approximately 0.3–0.8. Besides, the signs of F12/F11, F33/F11, F34/F11 and F44/F11 can be modified from negative to positive at some scattering angles if substituting the irregular dust with best-fitted spheroids. As the binding force is small, the deviation of extinction or scattering cross-section generally increases with the erosion degree, and the relative differences between dust with irregular shapes and spheroids can reach approximately 30 % when the erosion degree is large, while the differences are mitigated with the binding force increasing. Besides, with the binding force increasing, the aspect ratio is closer to 1:1. The deviations of the spheroid model in estimating the polarized light were also investigated using the successive-order-of-scattering (SOS) vector radiative transfer (VRT) model. With a diameter (dp) of 0.2 µm, the relative difference of normalized radiance does not exceed 3 %, and the absolute values of the deviation of the polarized bidirectional reflectance factor (PBRF) and the ratio of radiance to polarized intensity (DoLP) are below 0.005 and 0.02, respectively. However, with the particle size increasing, the difference becomes much more substantial. The relative difference of the normalized radiance can exceed 10 %, and the deviations of the PBRF and DoLP can vary in the ranges of −0.015 to 0.025 and −0.05 to 0.15, respectively. Thus, the single spheroid model may lead to non-negligible deviations for estimating the polarimetric characteristics of single dust particles with more complex shapes. In this work, only the optical properties of single particles were considered. In the future, the applicability of an ensemble of spheroidal particles for reproducing the scattering properties and polarimetric characteristics of an ensemble of irregularly shaped dust particles should be further investigated.

Published
2022
Full Text
View/download PDF

2. Research on a Novel Clutter Map Constant False Alarm Rate Detector Based on Power Transform

Author

B. Z. Xu, Y. Q. Chen, H. Gu, and W. M. Su

Subjects
constant false alarm rate, clutter map, power transform, weak target detection, multiple persisting targets, detection probability, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

A power transform-based clutter map constant false alarm rate (CM/PT-CFAR) algorithm is proposed to improve the detection performance to the weak target in multiple persisting targets situations. In the CM/PT-CFAR detector, the radar dataset obtained at each scan is normalized and multiplied by a scale factor, and then fed to the power transform operation. The transformed dataset is divided into two parts with a numerical value of 1 as the boundary. The part exceeding 1 is fed to update the scale factor, while the other is used for updating the detection threshold. Because transcendental integrals are produced in the derivation process, an accurate analytical expression of detection probability for CM/PT-CFAR is non-existent. Hence a third-order Taylor expansion operation is introduced to approximate the result. The detection performance of CM/PT-CFAR under various conditions is evaluated and compared with those of other CM/-CFAR detectors. The advantage of CM/PT-CFAR is the detection for weak targets, especially in complex detection situations. The proposed algorithm remains a relatively stable computation load in different cases, which is beneficial for practical application.

Published
2022

3. AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment

Author

Y. Guo, X. Yu, Y.-P. Xu, H. Chen, H. Gu, and J. Xie

Subjects
Technology, Environmental technology. Sanitary engineering, TD1-1066, Geography. Anthropology. Recreation, Environmental sciences, GE1-350
Abstract

Streamflow forecasts are traditionally effective in mitigating water scarcity and flood defense. This study developed an artificial intelligence (AI)-based management methodology that integrated multi-step streamflow forecasts and multi-objective reservoir operation optimization for water resource allocation. Following the methodology, we aimed to assess forecast quality and forecast-informed reservoir operation performance together due to the influence of inflow forecast uncertainty. Varying combinations of climate and hydrological variables were input into three AI-based models, namely a long short-term memory (LSTM), a gated recurrent unit (GRU), and a least-squares support vector machine (LSSVM), to forecast short-term streamflow. Based on three deterministic forecasts, the stochastic inflow scenarios were further developed using Bayesian model averaging (BMA) for quantifying uncertainty. The forecasting scheme was further coupled with a multi-reservoir optimization model, and the multi-objective programming was solved using the parameterized multi-objective robust decision-making (MORDM) approach. The AI-based management framework was applied and demonstrated over a multi-reservoir system (25 reservoirs) in the Zhoushan Islands, China. Three main conclusions were drawn from this study: (1) GRU and LSTM performed equally well on streamflow forecasts, and GRU might be the preferred method over LSTM, given that it had simpler structures and less modeling time; (2) higher forecast performance could lead to improved reservoir operation, while uncertain forecasts were more valuable than deterministic forecasts, regarding two performance metrics, i.e., water supply reliability and operating costs; (3) the relationship between the forecast horizon and reservoir operation was complex and depended on the operating configurations (forecast quality and uncertainty) and performance measures. This study reinforces the potential of an AI-based stochastic streamflow forecasting scheme to seek robust strategies under uncertainty.

Published
2021
Full Text
View/download PDF

4. REMOTE ATTITUDE SENSING BASED ON HIGH-SPEED MUELLER MATRIX ELLIPSOMETRY

Author

S. Zhang, H. Jiang, H. Gu, X. Chen, and S. Liu

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

The attitude metrology of moving objects has always been a hot topic in engineering applications. In this article, a novel optical method based on the high-speed Mueller matrix ellipsometry (MME) is proposed for real-time remote attitude sensing. With the help of an electric level, all the attitude angles of a free-moving object with a birefringent waveplate attached can be simultaneously extracted in real time. Compared with existing ellipsometry-based attitude metrology that can only be used in the transmission mode, the proposed method with the advantages of the high-speed MME can expand the application to both the transmission and reflection modes while maintaining higher accuracy and robustness in an ultra-large measurement range. The basic principle is presented via theoretical derivation, and the change in the Mueller matrix of the birefringent waveplate versus the attitude angles is explored in the simulated experiments. In a series of the dynamic experiments, with the extremely high temporal resolution and accuracy of the high-speed MME, an angular resolution up to 0.00022° and an angular accuracy better than 0.01° are achieved in the real-time attitude tracking of the free-moving object. As an additional benefit, the real-time angular velocity and acceleration of all the attitude angles can be obtained simultaneously. These experimental results demonstrate that the proposed attitude metrology is ready to be applied to the real-time remote attitude sensing with high angular resolution and accuracy.

Published
2020
Full Text
View/download PDF

7. MONITORING THE SURFACE SUBSIDENCE OF HANDAN CITY USING SENTINEL-1A IMAGES AND SBAS-INSAR TECHNOLOGY

Author

G. Huang, H. Chen, X. Li, G. Cheng, Z. Yu, and H. Gu

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

The Sentinel-1 data is currently the latest free SAR data and is well suited for land subsidence monitoring based on InSAR technology due to its 6-day revisit cycle. In this paper, the urban area and surrounding areas of Handan City are used as research areas, and 29 scences of S1A data (December 2017 – December 2018) was used for time series SBAS processing. The results shows that the surface cumulative deformation ranged from -42 to 30 mm in most regions of Handan City. The maximum settlement is 69 mm, which is near the Hankuang Group Julong Company. The areas where the settlement is obvious include Wu'an City, Daishan Village, Dashe Town, Zhangxibao Town, Baijia Street, Dongxingtai Village, Gaonan Village and Hankuang Group Julong Company. Slightly elevated in the southeast of Handan City.

Published
2019
Full Text
View/download PDF

8. EXTRACTION OF LEAF ANGLE DISTRIBUTION FROM AN INDIVIDUAL BROADLEAF TREE USING TERRESTRIAL LASER SCANNING DATA

Author

Y. Chen, Z. Liu, W. Zhang, C. Qiao, and H. Gu

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

The angular distribution of leaves is a key vegetation structural parameter for evaluating the reflection and transmission of solar radiation through vegetation canopies. Accurate extraction of Leaf Angle Distribution (LAD) is of great importance in estimating other vegetation structural parameters such as the canopy clumping and leaf area index. However, field measurement of LAD is time-consuming, labour-intensive and subjective. In most studies, LAD is assumed to follow the spherical distribution assumption within canopy which may lead to considerable errors. To address this issue, we proposed a new approach for leaf segmentation and LAD measurement of individual broadleaf tree based on the TLS point cloud data. Based on the point density, point continuity and the distribution of intensity in the point cloud, this approach provides a fast and accurate leaf segmentation and LAD extraction strategy. Results of this TLS-based LAD method compared well with that extracted by the field measurement and the MDI-based method. This strategy shows its potential and applicability in accurate LAD measurement and LAI estimation.

Published
2019
Full Text
View/download PDF

9. High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

Author

H. Gu, Z. Yu, C. Yang, Q. Ju, T. Yang, and D. Zhang

Subjects
Technology, Environmental technology. Sanitary engineering, TD1-1066, Geography. Anthropology. Recreation, Environmental sciences, GE1-350
Abstract

An ensemble simulation of five regional climate models (RCMs) from the coordinated regional downscaling experiment in East Asia is evaluated and used to project future regional climate change in China. The influences of model uncertainty and internal variability on projections are also identified. The RCMs simulate the historical (1980–2005) climate and future (2006–2049) climate projections under the Representative Concentration Pathway (RCP) RCP4.5 scenario. The simulations for five subregions in China, including northeastern China, northern China, southern China, northwestern China, and the Tibetan Plateau, are highlighted in this study. Results show that (1) RCMs can capture the climatology, annual cycle, and interannual variability of temperature and precipitation and that a multi-model ensemble (MME) outperforms that of an individual RCM. The added values for RCMs are confirmed by comparing the performance of RCMs and global climate models (GCMs) in reproducing annual and seasonal mean precipitation and temperature during the historical period. (2) For future (2030–2049) climate, the MME indicates consistent warming trends at around 1 °C in the entire domain and projects pronounced warming in northern and western China. The annual precipitation is likely to increase in most of the simulation region, except for the Tibetan Plateau. (3) Generally, the future projected change in annual and seasonal mean temperature by RCMs is nearly consistent with the results from the driving GCM. However, changes in annual and seasonal mean precipitation exhibit significant inter-RCM differences and possess a larger magnitude and variability than the driving GCM. Even opposite signals for projected changes in average precipitation between the MME and the driving GCM are shown over southern China, northeastern China, and the Tibetan Plateau. (4) The uncertainty in projected mean temperature mainly arises from the internal variability over northern and southern China and the model uncertainty over the other three subregions. For the projected mean precipitation, the dominant uncertainty source is the internal variability over most regions, except for the Tibetan Plateau, where the model uncertainty reaches up to 60 %. Moreover, the model uncertainty increases with prediction lead time across all subregions.

Published
2018
Full Text
View/download PDF

10. Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves

Author

M.S. Borhan, S. Panigrahi, M.A. Satter, and H. Gu

Subjects
Agriculture (General), S1-972, Information technology, T58.5-58.64
Abstract

Facilitating non-contact measurement, a computer-imaging system was devised and evaluated to predict the chlorophyll content in potato leaves. A charge-coupled device (CCD) camera paired with two optical filters and light chamber was used to acquire green (550 ± 40 nm) and red band (700 ± 40 nm) images from the same leaf. Potato leaves from 15 plants differing in coloration (green to yellow) and age were selected for this study. Histogram based image features, such as mean and variances of green and red band images, were extracted from the histogram. Regression analyses demonstrated that the variations in SPAD meter reading could be explained by the mean gray and variances of gray scale values. The fitted least square models based on the mean gray scale levels were inversely related to the chlorophyll content of the potato leaf with a R2 of 0.87 using a green band image and with an R2 of 0.79 using a red band image. With the extracted four image features, the developed multiple linear regression model predicted the chlorophyll content with a high R2 of 0.88). The multiple regression model (using all features) provided an average prediction accuracy of 85.08% and a maximum accuracy of 99.8%. The prediction model using only mean gray value of red band showed an average accuracy of 81.6% with a maximum accuracy of 99.14%. Keywords: Computer imaging, Chlorophyll, SPAD meter, Regression, Prediction accuracy

Published
2017
Full Text
View/download PDF

11. High-resolution mapping of time since disturbance and forest carbon flux from remote sensing and inventory data to assess harvest, fire, and beetle disturbance legacies in the Pacific Northwest

Author

H. Gu, C. A. Williams, B. Ghimire, F. Zhao, and C. Huang

Subjects
Ecology, QH540-549.5, Life, QH501-531, Geology, QE1-996.5
Abstract

Accurate assessment of forest carbon storage and uptake is central to policymaking aimed at mitigating climate change and understanding the role forests play in the global carbon cycle. Disturbances have highly diverse impacts on forest carbon dynamics, making them a challenge to quantify and report. Time since disturbance is a key intermediate determinant that aids the assessment of disturbance-driven carbon emissions and removals legacies. We propose a new methodology of quantifying time since disturbance and carbon flux across forested landscapes in the Pacific Northwest (PNW) at a fine scale (30 m) by combining remote sensing (RS)-based disturbance year, disturbance type, and above-ground biomass with forest inventory data. When a recent disturbance is detected, time since disturbance can be directly determined by combining three RS-derived disturbance products, or time since the last stand clearing can be inferred from a RS-derived 30 m biomass map and field inventory-derived species-specific biomass accumulation curves. Net ecosystem productivity (NEP) is further mapped based on carbon stock and flux trajectories derived from the Carnegie-Ames-Stanford Approach (CASA) model in our prior work that described how NEP changes with time following harvest, fire, or bark beetle disturbances of varying severity. Uncertainties from biomass map and forest inventory data were propagated by probabilistic sampling to provide a statistical distribution of stand age and NEP for each forest pixel. We mapped mean, standard deviation, and statistical distribution of stand age and NEP at 30 m in the PNW region. Our map indicated a net ecosystem productivity of 5.9 Tg C yr−1 for forestlands circa 2010 in the study area, with net uptake in relatively mature (> 24 years old) forests (13.6 Tg C yr−1) overwhelming net negative NEP from tracts that had recent harvests (−6.4 Tg C yr−1), fires (−0.5 Tg C yr−1), and bark beetle outbreaks (−0.8 Tg C yr−1). The approach will be applied to forestlands in other regions of the conterminous US to advance a more comprehensive monitoring, mapping, and reporting of the carbon consequences of forest change across the US.

Published
2016
Full Text
View/download PDF

13. Structural and compositional responses to timber harvesting for an old-growth forest on Changbai Mountain, China - Short Communication

Author

H. Gu and L. Dai

Subjects
forest harvesting, stand structure, species composition, northeast china, forest conservation, pinus ko-raiensis, Forestry, SD1-669.5
Abstract

Broadleaved-Korean pine (Pinus koraiensis) mixed forest is a dominant native vegetation type in the eastern Eurasian Continent. We intended to examine the implications of high-intensity timber harvesting (ca 70% of stand volume) for the sustainable management of a mixed forest ecosystem. We measured trees at three sites: control without cutting, older-cut site cut in 1987 and newer-cut site (cutting in 1997). There were significant differences in structure and composition between these three sites in 2003. There were 4,441 trees of 14 species with basal area 56 m2 at control plot, 6,314 trees of 16 species with basal area 9 m2 at newer-cut site and 8,438 trees of 21 species with basal area 31 m2 at older-cut site (all on the area of 1 ha). The high-intensity timber harvesting system helped promote natural regeneration and the growth of small trees but it also allowed light-demanding tree species to invade into the forest. Dominant position and suitable diameter distribution of economically important species (Pinus koraiensis and Tilia amurensis) were maintained across the three sites. The existing timber harvesting appears to consider short-term economic values to a larger extent than long-term ecological values. To manage the broadleaved-Korean pine mixed forest for both timber production and biodiversity conservation, timber-harvesting intensity must be lowered.

Published
2008
Full Text
View/download PDF

15. Active Sensing Based Bolted Structure Health Monitoring Using Piezoceramic Transducers

Author

R. L. Wang, H. Gu, and G. Song

Subjects
Electronic computers. Computer science, QA75.5-76.95
Abstract

Bolted structures are commonly used in civil infrastructure. It is important to perform bolt inspection regularly to ensure the safety of structures. Traditional bolt inspection methods are time-consuming; moreover, bulky instruments are needed in these methods. In this paper, a piezoceramic based active sensing approach is developed to perform the health monitoring of bolted structures. Surface-bonded piezoceramic patches are used as health monitoring transducers. Wavelet packet analysis is used to analyze the sensor data to extract the features that indicate bolt looseness. Based on wavelet packet analysis results, a damage index is developed to quantitatively evaluate the damage status. To verify the effectiveness of the proposed method, a bolted connection experiment with piezoceramic transducers was performed. In the experiment, the looseness of the bolt is adjusted by a torque wrench. Experimental results show that the proposed approach is effective to detect and evaluate bolt looseness.

Published
2013
Full Text
View/download PDF

16. P5.02 RELATIVE CONTRIBUTION OF PRE AND AFTER-LOAD IN REDUCTION OF TIME-VARYING MYOCARDIAL STRESS BY NITROGLYCERIN

Author

H. Gu, H. Fok, B. Jiang, M. Sinha, J. Simpson, and P. Chowienczyk

Subjects
Specialties of internal medicine, RC581-951, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Background: Nitroglycerin (NTG) reduces cardiac pre-load and after-load through venodilation and arterial dilation respectively but the relative contributions of these effects to reduction in myocardial wall stress is unknown. Methods: We estimated myocardial wall stress from transthoracic echocardiographic imaging of the left ventricle (LV) and LV pressure estimated from carotid tonometry during systole. Nineteen subjects aged 43.3 ± 2.7 (mean ± SE) years were studied before and 7–12 min after NTG (400 ìg sublingually). Carotid pressure calibrated by mean and diastolic blood pressure (BP) was used to calculate time-varying LV wall stress from endocardial and epicardial volumes obtained from Tomtec wall tracking analysis. The relative contributions of reductions in systolic pressure and in LV volumes and to overall reduction in LV wall stress were calculated assuming that volume or pressures after NTG were identical to baseline values. Results: NTG decreased peak LV stress (pre: 387±22; post: 329±22kdynes/cm2, P

Published
2013
Full Text
View/download PDF

17. Electro-Migration Model Parameters Sensitivity Analysis Based on the Monte Carlo Method

Author

W. Feng, L. Wang, G. Zhou, D. Zhang, and H. Gu

Subjects
Chemical engineering, TP155-156, Computer engineering. Computer hardware, TK7885-7895
Abstract

With the narrowing of the size and the improvement of integration, Integrated Circuit devices’ reliability is increasingly becoming the focus of attention. The Electro-Migration is the one of many reasons to cause circuit malfunction. When the Integrated Circuits is working, certain current goes through the metal interconnect inside, and the metal ion moves in the effect of the "electronic wind", making certain parts of the conductor produces empty or whisker and hill, which is Electro-Migration phenomena. The migration will cause the failure of the electrical connection, which can reduce the reliability of the structure of Integrated Circuit. The physical model established the relation between EM phenomena and current density, geometry size, material properties and the temperature distribution of the metal line, which can evaluate the electric wire lifetime under the impact of Electro-Migration. The sensitivity analysis of the electric parameters is very significant for improving the reliability of the IC. Based on the classical EM failure physical model equation of Black, this paper analyzes this model by using the Monte Carlo method. Firstly, confirms the four parameters of distribution in the Black equations. Then, analyzes the Monte Carlo method about the value of sampling N, the value of r* in the limit equations, and the finally number of digits effective sensitivity. And then, analysis the four parameters using the Matlab, mainly considers four parameters' change; Change three parameters and fix the remaining one.; Two parameters change, two parameters fixed; A parameter change, three parameters fixed. Eventually, get the sensitivity order of the parameters in Black equations. This paper is useful for the physical model parameter sensitivity analysis of reliability (Ogawa E. T, 2002) and life prediction of the product.

Published
2013
Full Text
View/download PDF

18. The Tenth Visual Object Tracking VOT2022 Challenge Results.

Author

Matej Kristan, Ales Leonardis, Jirí Matas, Michael Felsberg, Roman P. Pflugfelder, Joni-Kristian Kämäräinen, Hyung Jin Chang, Martin Danelljan, Luka Cehovin Zajc, Alan Lukezic, Ondrej Drbohlav, Johanna Björklund, Yushan Zhang, Zhongqun Zhang, Song Yan, Wenyan Yang, Dingding Cai, Christoph Mayer 0007, Gustavo Fernández, Kang Ben, Goutam Bhat, Hong Chang 0001, Guangqi Chen, Jiaye Chen, Shengyong Chen, Xilin Chen 0001, Xin Chen 0032, Xiuyi Chen, Yiwei Chen, Yu-Hsi Chen, Zhixing Chen, Yangming Cheng, Angelo Ciaramella, Yutao Cui, Benjamin Dzubur, Mohana Murali Dasari, Qili Deng, Debajyoti Dhar, Shangzhe Di, Emanuel Di Nardo, Daniel K. Du, Matteo Dunnhofer, Heng Fan 0001, Zhen-Hua Feng, Zhihong Fu, Shang Gao, Rama Krishna Gorthi, Eric Granger, Q. H. Gu, Himanshu Gupta, Jianfeng He, Keji He, Yan Huang 0008, Deepak Jangid, Rongrong Ji, Cheng Jiang 0005, Yingjie Jiang, Felix Järemo Lawin, Ze Kang, Madhu Kiran, Josef Kittler, Simiao Lai, Xiangyuan Lan, Dongwook Lee, Hyunjeong Lee, Seohyung Lee, Hui Li 0037, Ming Li 0010, Wangkai Li, Xi Li 0001, Xianxian Li, Xiao Li, Zhe Li 0008, Liting Lin, Haibin Ling, Bo Liu 0005, Chang Liu 0071, Si Liu 0001, Huchuan Lu, Rafael M. O. Cruz, Bingpeng Ma, Chao Ma 0004, Jie Ma, Yinchao Ma, Niki Martinel, Alireza Memarmoghadam, Christian Micheloni, Payman Moallem, Le Thanh Nguyen-Meidine, Siyang Pan, ChangBeom Park, Danda Pani Paudel, Matthieu Paul, Houwen Peng, Andreas Robinson, Litu Rout, Shiguang Shan, Kristian Simonato, Tianhui Song, Xiaoning Song, Chao Sun, Jingna Sun, Zhangyong Tang, Radu Timofte, Chi-Yi Tsai, Luc Van Gool, Om Prakash Verma, Dong Wang 0004, Fei Wang 0032, Liang Wang 0001, Liangliang Wang, Lijun Wang, Limin Wang 0002, Qiang Wang 0023, Gangshan Wu, Jinlin Wu, Xiaojun Wu 0001, Fei Xie, Tianyang Xu, Wei Xu 0017, Yong Xu 0007, Yuanyou Xu, Wanli Xue, Zizheng Xun, Bin Yan 0004, Dawei Yang, Jinyu Yang, Wankou Yang, Xiaoyun Yang, Yi Yang 0001, Yichun Yang, Zongxin Yang, Botao Ye, Fisher Yu 0001, Hongyuan Yu, Jiaqian Yu, Qianjin Yu, Weichen Yu, Kang Ze, Jiang Zhai, Chengwei Zhang 0001, Chunhu Zhang, Kaihua Zhang 0001, Tianzhu Zhang, Wenkang Zhang, Zhibin Zhang, Zhipeng Zhang, Jie Zhao 0014, Shao-Chuan Zhao, Feng Zheng, Haixia Zheng, Min Zheng, Bineng Zhong, Jiawen Zhu, Xuefeng Zhu, and Yueting Zhuang

Published
2022
Full Text
View/download PDF

19. Double di ffential fragmentation cross sections measurements of 95 MeV/u 12C on thin targets for hadrontherapy

Author

Dudouet, J., Juliani, D., elique, J. C. Ang, Braunn, B., Colin, J., Cussol, D., Finck, Ch., Fontbonne, J. M., erin, H. Gu, Henriquet, P., Krimmer, J., Labalme, M., Rousseau, M., Saint-Laurent, M. G., and Salvador, S.

Subjects
Nuclear Experiment, Physics - Medical Physics
Abstract

During therapeutic treatment with heavy ions like carbon, the beam undergoes nuclear fragmentation and secondary light charged particles, in particular protons and alpha particles, are produced. To estimate the dose deposited into the tumors and the surrounding healthy tissues, an accurate prediction on the fluences of these secondary fragments is necessary. Nowadays, a very limited set of double di ffential carbon fragmentation cross sections are being measured in the energy range used in hadrontherapy (40 to 400 MeV/u). Therefore, new measurements are performed to determine the double di ffential cross section of carbon on di erent thin targets. This work describes the experimental results of an experiment performed on May 2011 at GANIL. The double di ffential cross sections and the angular distributions of secondary fragments produced in the 12C fragmentation at 95 MeV/u on thin targets (C, CH2, Al, Al2O3, Ti and PMMA) have been measured. The experimental setup will be precisely described, the systematic error study will be explained and all the experimental data will be presented., Comment: Submitted to PRC

Published
2013
Full Text
View/download PDF

30. A high efficiency Lie derivative algorithm for the nonautonomous nonlinear systems

Author

W. A. Jiang, Z. H. Gu, C. Liu, H. R. Feng, and L. Q. Chen

Subjects
Computational Theory and Mathematics, General Physics and Astronomy, Statistical and Nonlinear Physics, Mathematical Physics, Computer Science Applications
Abstract

Numerical procedure plays a key role in tackling the solutions of nonlinear dynamical systems. With the advent of the age of big data and high-power computing, developing efficient and fast numerical algorithms is an urgent task. This paper extends the Lie derivative discretization algorithm to the nonautonomous nonlinear systems and investigates the numerical solutions of the systems. The periodic solutions of three different classical nonlinear systems are calculated, and the results are compared to those values calculated from the Runge–Kutta fourth-order algorithm, which demonstrated that the Lie derivative algorithm has the advantages of large time step and short computation time.

Published
2023
Full Text
View/download PDF

31. Observation of <math><mrow><mi>ψ</mi><mo>(</mo><mn>3770</mn><mo>)</mo><mo>→</mo><mi>η</mi><mi>J</mi><mo>/</mo><mi>ψ</mi></mrow></math>

Author

Ablikim, M., Achasov, M. N., Adlarson, P., Ahmed, S., Albrecht, M., Aliberti, R., Amoroso, A., M. R., An, An, Q., Bai, X. H., Bai, Y., Bakina, O., Baldini Ferroli, R., Balossino, I., Ban, Y., Batozskaya, V., Becker, D., Begzsuren, K., Berger, N., Bertani, M., Bettoni, D., Bianchi, F., Bloms, J., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Chang, W. L., Chelkov, G., Chen, C., Chen, G., Chen, H. S., Chen, M. L., Chen, S. J., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Z. J., Cheng, W. S., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., S. X., Du, Egorov, P., Fan, Y. L., Fang, J., Fang, S. S., Fang, Y., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Fischer, K., Fritsch, M., C. D., Fu, Gao, H., Gao, Y. N., Gao, Yang, Garzia, I., P. T., Ge, Z. W., Ge, Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Greco, M., L. M., Gu, M. H., Gu, Y. T., Gu, Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, R. P., Guo, Y. P., Guskov, A., Han, T. T., Han, W. Y., Hao, X. Q., Harris, F. A., K. K., He, K. L., He, Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Himmelreich, M., Holtmann, T., Hou, G. Y., Hou, Y. R., Hou, Z. L., H. M., Hu, J. F., Hu, Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Z., Hussain, T., Hüsken, N., Imoehl, W., Irshad, M., Jackson, J., Jaeger, S., Janchiv, S., Ji, Q., Q. P., Ji, X. B., Ji, X. L., Ji, Y. Y., Ji, Jiang, H. B., Jiang, S. S., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Johansson, T., Kalantar-Nayestanaki, N., Kang, X. S., Kappert, R., Kavatsyuk, M., B. C., Ke, Keshk, I. K., Khoukaz, A., Kiese, P., Kiuchi, R., Kliemt, R., Koch, L., Kolcu, O. B., Kopf, B., Kuemmel, M., Kuessner, M., Kupsc, A., Kühn, W., Lane, J. J., Lange, J. S., Larin, P., Lavania, A., Lavezzi, L., Lei, Z. H., Leithoff, H., Lellmann, M., Lenz, T., Li, C., C. H., Li, Cheng, Li, D. M., Li, Li, F., Li, G., Li, H., H. B., Li, H. J., Li, H. N., Li, J. Q., Li, J. S., Li, J. W., Li, Ke, Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, S. X., Li, S. Y., Li, Li, T., W. D., Li, W. G., Li, X. H., Li, X. L., Li, Xiaoyu, Li, Z. Y., Li, Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Libby, J., Limphirat, A., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, C. X., Liu, D., Liu, F. H., Liu, Fang, Liu, Feng, Liu, G. M., Liu, H. B., Liu, H. M., Liu, Huanhuan, Liu, Huihui, Liu, J. B., Liu, J. L., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. Q., Lou, X. C., F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Lu, Y., Y. P., Lu, Z. H., Lu, Luo, C. L., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., F. C., Ma, H. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, R. T., Ma, X. Y., Ma, Ma, Y., Maas, F. E., Maggiora, M., Maldaner, S., Malde, S., Malik, Q. A., Mangoni, A., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., X. H., Mo, Muchnoi, N. Yu., Muramatsu, H., Nakhoul, S., Nefedov, Y., Nerling, F., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pelizaeus, M., Peng, H. P., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Pogodin, S., Poling, R., Prasad, V., Qi, H., H. R., Qi, Qi, M., T. Y., Qi, Qian, S., Qian, W. B., Qian, Z., Qiao, C. F., Qin, J. J., Qin, L. Q., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., S. Q., Qu, Rashid, K. H., Ravindran, K., Redmer, C. F., Ren, K. J., Rivetti, A., Rodin, V., Rolo, M., Rong, G., Rosner, Ch., Sarantsev, A., Schelhaas, Y., Schnier, C., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, X. Y., Shi, B. A., Shi, H. C., Shi, R. S., Shi, X., Shi, X. D., Song, J. J., Song, W. M., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., K. X., Su, P. P., Su, Y. J., Su, Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, X., Sun, Y. J., Sun, Y. Z., Sun, Z. T., Tan, Y. H., Tan, Y. X., Tang, C. J., Tang, G. Y., Tang, J., Tao, L. Y., Tao, Q. T., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Uman, I., Wang, B., Wang, B. L., Wang, C. W., Wang, D. Y., Wang, F., Wang, H. J., Wang, H. P., Wang, K., Wang, L. L., Wang, M., Wang, M. Z., Wang, Meng, Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. H., Wang, W. P., Wang, X., Wang, X. F., Wang, X. L., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. Q., Wang, Z., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., White, D. J., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., J. F., Wu, L. H., Wu, L. J., Wu, Wu, X., X. H., Wu, Wu, Y., Y. J., Wu, Wu, Z., Xia, L., Xiang, T., Xiao, G. Y., Xiao, H., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, Q. J., Xu, X. P., Xu, Y. C., Xu, Z. P., Xu, Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yang, H. J., Yang, H. X., Yang, L., Yang, S. L., Yang, Y. X., Yang, Yifan, Yang, Zhi, Ye, M., M. H., Ye, Yin, J. H., You, Z. Y., B. X., Yu, C. X., Yu, Yu, G., J. S., Yu, Yu, T., Yuan, C. Z., Yuan, L., Yuan, S. C., Yuan, X. Q., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, X., Zeng, Y., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, G. Y., Zhang, H., Zhang, H. H., Zhang, H. Y., Zhang, J. L., Zhang, J. Q., Zhang, J. W., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, Jiawei, Zhang, L. M., Zhang, L. Q., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, Shuihan, Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Yan, Zhang, Yao, Zhang, Z. H., Zhang, Z. Y., Zhao, G., Zhao, J., Zhao, J. Y., Zhao, J. Z., Zhao, Lei, Zhao, Ling, Zhao, M. G., Zhao, Q., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, J. P., Zheng, Y. H., Zhong, B., Zhong, C., Zhong, X., Zhou, H., Zhou, L. P., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, L. X., Zhu, S. H., Zhu, S. Q., Zhu, T. J., Zhu, W. J., Zhu, Y. C., Zhu, Z. A., Zou, B. S., and Zou, J. H.

Abstract

Using a data sample collected with the BESIII detector operating at the BEPCII storage ring, the Born cross section of the process e+e−→ηJ/ψ at a center-of-mass energy s=3.773 GeV is measured to be (8.88±0.87±0.42) pb. We fit the cross section line shape before correcting for the initial state radiation from s=3.773 to 4.600 GeV to obtain the branching fraction B(ψ(3770)→ηJ/ψ). We obtain B(ψ(3770)→ηJ/ψ)=(11.3±5.9±1.1)×10−4 when the ψ(3770) decay amplitude is added coherently to the other contributions, and (8.7±1.0±0.8)×10−4 when it is added incoherently. Here the quoted uncertainties are statistical and systematic, respectively. In both cases, the statistical significance of ψ(3770) resonance is above 7σ. This is the first time the decay ψ(3770)→ηJ/ψ is observed with a statistical significance greater than 5σ.

Published
2023

40. Numerical Modal Analysis of the Correlation Between Spanwise Vortex Shedding and Far-field Aeolian Noise for Square and Circular Wall-mounted Cylinders

Author

R. P. Jiang, P. Q. Liu, J. Zhang, and H. Guo

Subjects
aeroacoustics, finite wall-mounted cylinder, dynamic mode decomposition, spanwise energy distribution, modal energy contribution, Mechanical engineering and machinery, TJ1-1570
Abstract

For square and circular finite wall-mounted cylinders (FWMCs) with an aspect ratio exceeding 10, the vortex shedding near the tip area leads to the generation of multiple tonal noises. The quantitative analysis of the spanwise distributions of the vortex modal energy with different frequencies was quite limited. This study employs dynamic mode decomposition to decompose the wake of FWMC into distinct frequencies to evaluate the modal energy distribution of pressure fluctuations at each frequency along the spanwise direction. Large eddy simulation combined with the Ffowcs Williams–Hawkings (FW–H) acoustics analogy is applied to a square and a circular FWMC with aspect ratio of 13.6 at a Reynolds number of 2.3 × 104. Two indicators to describe the spanwise energy contribution are proposed. The results reveal that, for square FWMC, the primary modal energy corresponding to Strouhal number ( St ) equal to 0.14 is concentrated below 30% of the cylinder height owing to the 3D effect. A transition mode of St ≈ 0.12 is identified in the midspan (0.3 L–0.7L ) without significant contribution to far-field noise spectrum. For circular FWMC, the modal energy is distributed over several frequencies, vortices cells corresponding to the main noise band (0.2

Published
2024
Full Text
View/download PDF

41. Semantic Segmentation Uncertainty Assessment of Different U-net Architectures for Extracting Building Footprints

Author

E. Haghighi Gashti, M. R. Delavar, H. Guan, and J. Li

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

Automatic extraction of building footprints from aerial and space imageries has been found ever increasing importance in urban planning, disaster management, and environmental monitoring. However, achieving accurate building footprint extraction poses significant challenges due to diverse building characteristics and their similarities to their background elements. While conventional methods in building footprint extraction have mainly relied on image processing techniques, recent advancements in deep learning, particularly semantic segmentation algorithms like U-Net, have shown promise in addressing these challenges through machine learning. This study explores different depths of the U-Net model for building footprint extraction, aiming to identify the optimum architecture while investigating the semantic uncertainty of the building footprint extraction. Utilizing aerial imagery from cities including Berlin, Paris, Chicago, and Zurich, collected from Google Maps and OpenStreetMap (OSM) data, five U-Net models have been compared with varying depths. In addition, the impact of dataset sizes and learning rates on model performance has been investigated. Results confirmed that the U-Net-32-1024 model achieves the highest intersection over union (IoU), Accuracy, and F1-score. Moreover, increasing the training dataset size leads to significant improvements in model performance with IoU, Accuracy and F1-score reaching their values of 73.73%, 88.65% and 88.53%. However, challenges remain in accurately delineating buildings in dense urban areas. Nonetheless, our findings demonstrated the effectiveness of U-Net models in building footprint extraction.

Published
2024
Full Text
View/download PDF

42. Measurement report: In situ vertical profiles of below-cloud aerosol over the central Greenland Ice Sheet

Author

H. Guy, A. S. Martin, E. Olson, I. M. Brooks, and R. R. Neely III

Subjects
Physics, QC1-999, Chemistry, QD1-999
Abstract

Surface radiative cooling in polar regions can generate persistent stability in the atmospheric boundary layer. Stable layers below clouds can decouple the cloud layer from the near-surface environment. Under these conditions, surface aerosol measurements are not necessarily representative of the near-cloud or intra-cloud aerosol populations. To better understand the variability in the vertical structure of aerosol properties over the central Greenland Ice Sheet, in situ measurements of aerosol particle size distributions up to cloud base were made at Summit Station in July and August 2023. These measurements identified distinct vertical aerosol layers between the surface- and cloud-base-associated thermodynamic decoupling layers. Such decoupling layers occur 49 % of the time during the summer in central Greenland, suggesting that surface aerosol measurements are insufficient for describing the cloud-relevant aerosol population half of the time. Experience during this first measurement season demonstrated the ability of a tethered-balloon platform to operate effectively under icing conditions and at low surface pressure (< 680 hPa). The results presented here illustrate the value of vertically resolved in situ measurements of aerosol properties in developing a nuanced understanding of the aerosol effects on cloud properties in polar regions.

Published
2024
Full Text
View/download PDF

43. Occurrence, abundance, and formation of atmospheric tarballs from a wide range of wildfires in the western US

Author

K. Adachi, J. E. Dibb, J. M. Katich, J. P. Schwarz, H. Guo, P. Campuzano-Jost, J. L. Jimenez, J. Peischl, C. D. Holmes, and J. Crawford

Subjects
Physics, QC1-999, Chemistry, QD1-999
Abstract

Biomass burning emits large numbers of organic aerosol particles, a subset of which are called tarballs (TBs). TBs possess a spherical morphology and unique physical, chemical, and optical properties. They are recognized as brown-carbon aerosol particles, influencing the climate through the absorption of solar radiation. Aerosol particles were collected from wildfire and agricultural-fire smoke sampled by NASA's DC-8 aircraft during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign in the western US from July to September 2019. This study developed an image analysis method using deep learning to distinguish TBs from other round particles that deformed on the substrate, based on the particles' morphological characteristics in transmission electron microscopy images. This study detected 4567 TBs, with most occurring < 10 h downwind of the emissions, and measured their compositions, abundance, sizes, and mixing states. The number fraction, mass fraction, and concentration of TBs in wildfire smoke corresponded to 10 ± 1 %, 10 ± 2 %, and 10.1 ± 4.6 µg m−3, respectively. As the smoke aged for up to 5 h after emission, the TB number fractions roughly increased from 5 % to 15 %, indicating that TBs are processed primary particles. We also observed TBs associated with pyrocumulonimbus (pyroCb) activity and various TB mixing states. This study reveals the abundance, as well as the physical and chemical properties, of a wide range of TBs from various biomass-burning events and enhances our knowledge of TB emissions, contributing to the evaluation of the climate impact of TBs.

Published
2024
Full Text
View/download PDF

45. Improved measurement of the absolute branching fraction of inclusive semileptonic <math><msubsup><mi>Λ</mi><mi>c</mi><mo>+</mo></msubsup></math> decay

Author

Ablikim, M., Achasov, M. N., Adlarson, P., Albrecht, M., Aliberti, R., Amoroso, A., M. R., An, An, Q., Bai, X. H., Bai, Y., Bakina, O., Baldini Ferroli, R., Balossino, I., Ban, Y., Batozskaya, V., Becker, D., Begzsuren, K., Berger, N., Bertani, M., Bettoni, D., Bianchi, F., Bloms, J., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Chang, W. L., Chelkov, G., Chen, C., Chen, Chao, Chen, G., Chen, H. S., Chen, M. L., Chen, S. J., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Z. J., Cheng, W. S., Choi, S. K., Chu, X., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., S. X., Du, Egorov, P., Fan, Y. L., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Fischer, K., Fritsch, M., Fritzsch, C., C. D., Fu, Gao, H., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., P. T., Ge, Z. W., Ge, Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Greco, M., L. M., Gu, M. H., Gu, Y. T., Gu, Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, R. P., Guo, Y. P., Guskov, A., Han, T. T., Han, W. Y., Hao, X. Q., Harris, F. A., K. K., He, K. L., He, Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Himmelreich, M., Hou, G. Y., Hou, Y. R., Hou, Z. L., H. M., Hu, J. F., Hu, Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Z., Hussain, T., Hüsken, N., Imoehl, W., Irshad, M., Jackson, J., Jaeger, S., Janchiv, S., Jang, E., Jeong, J. H., Ji, Q., Q. P., Ji, X. B., Ji, X. L., Ji, Y. Y., Ji, Jia, Z. K., Jiang, H. B., Jiang, S. S., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Johansson, T., Kalantar-Nayestanaki, N., Kang, X. S., Kappert, R., B. C., Ke, Keshk, I. K., Khoukaz, A., Kiuchi, R., Kliemt, R., Koch, L., Kolcu, O. B., Kopf, B., Kuemmel, M., Kuessner, M., Kupsc, A., Kühn, W., Lane, J. J., Lange, J. S., Larin, P., Lavania, A., Lavezzi, L., Lei, Z. H., Leithoff, H., Lellmann, M., Lenz, T., Li, C., C. H., Li, Cheng, Li, D. M., Li, Li, F., Li, G., Li, H., H. B., Li, H. J., Li, H. N., Li, J. Q., Li, J. S., Li, J. W., Li, Ke, Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, S. X., Li, S. Y., Li, Li, T., W. D., Li, W. G., Li, X. H., Li, X. L., Li, Xiaoyu, Li, Z. X., Li, Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Libby, J., Limphirat, A., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, C. X., Liu, D., Liu, F. H., Liu, Fang, Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. M., Liu, Huanhuan, Liu, Huihui, Liu, J. B., Liu, J. L., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. Q., Lou, X. C., F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Lu, Y., Y. P., Lu, Z. H., Lu, Luo, C. L., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., F. C., Ma, H. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, R. T., Ma, X. Y., Ma, Ma, Y., Maas, F. E., Maggiora, M., Maldaner, S., Malde, S., Malik, Q. A., Mangoni, A., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., X. H., Mo, Muchnoi, N. Yu., Nefedov, Y., Nerling, F., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pelizaeus, M., Peng, H. P., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Pogodin, S., Prasad, V., F. Z., Qi, Qi, H., H. R., Qi, Qi, M., T. Y., Qi, Qian, S., Qian, W. B., Qian, Z., Qiao, C. F., Qin, J. J., Qin, L. Q., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., S. Q., Qu, Rashid, K. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rodin, V., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Sang, H. S., Sarantsev, A., Schelhaas, Y., Schnier, C., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, X. Y., Shi, B. A., Shi, H. C., Shi, J. Y., Shi, q. q., Shi, R. S., Shi, X., Shi, X. D., Song, J. J., Song, W. M., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., K. X., Su, P. P., Su, Y. J., Su, Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, X., Sun, Y. J., Sun, Y. Z., Sun, Z. T., Tan, Y. H., Tan, Y. X., Tang, C. J., Tang, G. Y., Tang, J., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Uman, I., Wang, B., Wang, B. L., Wang, C. W., Wang, D. Y., Wang, F., Wang, H. J., Wang, H. P., Wang, K., Wang, L. L., Wang, M., Wang, M. Z., Wang, Meng, Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. H., Wang, W. P., Wang, X., Wang, X. F., Wang, X. L., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. Q., Wang, Yaqian, Wang, Z., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., White, D. J., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., J. F., Wu, L. H., Wu, L. J., Wu, Wu, X., X. H., Wu, Wu, Y., Wu, Z., Xia, L., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, Q. J., Xu, X. P., Xu, Y. C., Xu, Z. P., Xu, Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yang, H. J., Yang, H. L., Yang, H. X., Yang, L., Yang, S. L., Yang, Tao, Yang, Y. F., Yang, Y. X., Yang, Yifan, Ye, M., M. H., Ye, Yin, J. H., You, Z. Y., B. X., Yu, C. X., Yu, Yu, G., Yu, T., X. D., Yu, Yuan, C. Z., Yuan, L., Yuan, S. C., Yuan, X. Q., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, X., Zeng, Y., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. H., Zhang, H. Y., Zhang, J. L., Zhang, J. Q., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, Jiawei, Zhang, L. M., Zhang, L. Q., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, Shuihan, Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Yan, Zhang, Yao, Zhang, Z. H., Zhang, Z. Y., Zhao, G., Zhao, J., Zhao, J. Y., Zhao, J. Z., Zhao, Lei, Zhao, Ling, Zhao, M. G., Zhao, Q., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, J. P., Zheng, Y. H., Zhong, B., Zhong, C., Zhong, X., Zhou, H., Zhou, L. P., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, L. X., Zhu, S. H., Zhu, S. Q., Zhu, T. J., Zhu, W. J., Zhu, Y. C., Zhu, Z. A., Zou, B. S., and Zou, J. H.

Abstract

Using 4.5 fb−1 of e+e− annihilation data samples collected at center-of-mass energies ranging from 4.600 to 4.698 GeV with the BESIII detector at the BEPCII collider, we measured the absolute branching fraction for the inclusive semileptonic decay Λc+→Xe+νe, where X refers to any possible particle system. The branching fraction of the decay is determined to be B(Λc+→Xe+νe)=(4.06±0.10stat±0.09syst)%. Our result improves the precision of previous measurements of B(Λc+→Xe+νe) by more than threefold. Using the known Λc+ lifetime and the charge-averaged semileptonic decay width of nonstrange charmed mesons, we measure the ratio of inclusive semileptonic decay widths Γ(Λc+→Xe+νe)/Γ¯(D→Xe+νe)=1.28±0.05, where statistical and systematic uncertainties are combined.

Published
2023

46. Measurements of the branching fractions of the inclusive decays D0(D+)→π+π+π−X

Author

Ablikim, M., Achasov, M. N., Adlarson, P., Aliberti, R., Amoroso, A., M. R., An, An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Batozskaya, V., Becker, D., Begzsuren, K., Berger, N., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bloms, J., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Chang, W. L., Che, G. R., Chelkov, G., Chen, C., Chen, Chao, Chen, G., Chen, H. S., Chen, M. L., Chen, S. J., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Cheng, W. S., Choi, S. K., Chu, X., Cibinetto, G., Coen, S. C., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., S. X., Du, Duan, Z. H., Egorov, P., Fan, Y. L., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Fischer, K., Fritsch, M., Fritzsch, C., C. D., Fu, Y. W., Fu, Gao, H., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., P. T., Ge, Z. W., Ge, Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Greco, M., M. H., Gu, Y. T., Gu, Guan, C. Y., Guan, Z. L., Guo, A. Q., Guo, L. B., Guo, R. P., Guo, Y. P., Guskov, A., X. T., H., Han, W. Y., Hao, X. Q., Harris, F. A., K. K., He, K. L., He, Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, Y. R., Hou, Z. L., H. M., Hu, J. F., Hu, Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Hussain, T., Hüsken, N., Imoehl, W., Irshad, M., Jackson, J., Jaeger, S., Janchiv, S., Jang, E., Jeong, J. H., Ji, Q., Q. P., Ji, X. B., Ji, X. L., Ji, Y. Y., Ji, Jia, Z. K., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Johansson, T., K., X., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kappert, R., Kavatsyuk, M., B. C., Ke, Khoukaz, A., Kiuchi, R., Kliemt, R., Koch, L., Kolcu, O. B., Kopf, B., Kuessner, M., Kupsc, A., Kühn, W., Lane, J. J., Lange, J. S., Larin, P., Lavania, A., Lavezzi, L., Lei, T. T., Lei, Z. H., Leithoff, H., Lellmann, M., Lenz, T., Li, C., C. H., Li, Cheng, Li, D. M., Li, Li, F., Li, G., Li, H., H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, J. W., Li, Ke, Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, S. X., Li, S. Y., Li, Li, T., W. D., Li, W. G., Li, X. H., Li, X. L., Li, Xiaoyu, Li, Y. G., Li, Z. J., Li, Z. X., Li, Z. Y., Li, Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Libby, J., Limphirat, A., Lin, D. X., Lin, T., Liu, B. X., Liu, B. J., Liu, C., Liu, C. X., Liu, D., Liu, F. H., Liu, Fang, Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. M., Liu, Huanhuan, Liu, Huihui, Liu, J. B., Liu, J. L., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. Q., Lou, X. C., F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Lu, Y., Y. P., Lu, Z. H., Lu, Luo, C. L., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., F. C., Ma, H. L., Ma, J. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, R. T., Ma, X. Y., Ma, Ma, Y., Maas, F. E., Maggiora, M., Maldaner, S., Malde, S., Mangoni, A., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., X. H., Mo, Muchnoi, N. Yu., Nefedov, Y., Nerling, F., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Pogodin, S., Prasad, V., F. Z., Qi, Qi, H., H. R., Qi, Qi, M., T. Y., Qi, Qian, S., Qian, W. B., Qiao, C. F., Qin, J. J., Qin, L. Q., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., S. Q., Qu, Redmer, C. F., Ren, K. J., Rivetti, A., Rodin, V., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H. C., Shi, J. Y., Shi, Q. Q., Shi, R. S., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Y. J., Su, Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. T., Tan, Y. X., Tang, C. J., Tang, G. Y., Tang, J., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wang, B., Wang, B. L., Wang, C. W., Wang, D. Y., Wang, F., Wang, H. J., Wang, H. P., Wang, K., Wang, L. L., Wang, M., Wang, Meng, Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. H., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D., Wei, D. H., Weidner, F., Wen, S. P., Wenzel, C. W., White, D. J., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., J. F., Wu, L. H., Wu, L. J., Wu, Wu, X., X. H., Wu, Wu, Y., Y. J., Wu, Wu, Z., Xia, L., Xian, X. M., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, Q. J., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. P., Xu, Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, Tao, Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Yifan, Ye, M., M. H., Ye, Yin, J. H., You, Z. Y., B. X., Yu, C. X., Yu, Yu, G., Yu, T., X. D., Yu, Yuan, C. Z., Yuan, L., Yuan, S. C., Yuan, X. Q., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, X., Zeng, Y., Zhai, X. Y., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. H., Zhang, H. Q., Zhang, H. Y., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jiawei, Zhang, L. M., Zhang, L. Q., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, Shuihan, Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Yan, Zhang, Yao, Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhao, G., Zhao, J., Zhao, J. Y., Zhao, J. Z., Zhao, Lei, Zhao, Ling, Zhao, M. G., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, L. P., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, S. Q., Zhu, T. J., Zhu, W. J., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
BESIII
Published
2023
Full Text
View/download PDF

47. Study of at center-of-mass energies from 3.49 to 3.67??GeV

Author

Ablikim, M., Achasov, M. ???N., Adlarson, P., Albrecht, M., Aliberti, R., Amoroso, A., M. ???R., An, An, Q., Bai, X. ???H., Bai, Y., Bakina, O., Baldini Ferroli, R., Balossino, I., Ban, Y., Batozskaya, V., Becker, D., Begzsuren, K., Berger, N., Bertani, M., Bettoni, D., Bianchi, F., Bloms, J., Bortone, A., Boyko, I., Briere, R. ???A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. ???F., Cao, N., Cetin, S. ???A., Chang, J. ???F., Chang, W. ???L., Chelkov, G., Chen, C., Chao, Chen, Chen, G., Chen, H. ???S., Chen, M. ???L., Chen, S. ???J., Chen, S. ???M., Chen, T., Chen, X. ???R., Chen, X. ???T., Chen, Y. ???B., Chen, Z. ???J., Cheng, W. ???S., Choi, S. ???K., Chu, X., Cibinetto, G., Cossio, F., Cui, J. ???J., Dai, H. ???L., Dai, J. ???P., Dbeyssi, A., de Boer, R. ???E., Dedovich, D., Deng, Z. ???Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, Y., Dong, J., Dong, L. ???Y., Dong, M. ???Y., Dong, X., S. ???X., Du, Egorov, P., Fan, Y. ???L., Fang, J., Fang, S. ???S., Fang, W. ???X., Fang, Y., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. ???Q., Feng, J. ???H., Fischer, K., Fritsch, M., Fritzsch, C., C. ???D., Fu, Gao, H., Gao, Y. ???N., Yang, Gao, Garbolino, S., Garzia, I., P. ???T., Ge, Z. ???W., Ge, Geng, C., Gersabeck, E. ???M., Gilman, A., Goetzen, K., Gong, L., Gong, W. ???X., Gradl, W., Greco, M., L. ???M., Gu, M. ???H., Gu, Y. ???T., Gu, Guan, C. ???Y., Guo, A. ???Q., Guo, L. ???B., Guo, R. ???P., Guo, Y. ???P., Guskov, A., Han, T. ???T., Han, W. ???Y., Hao, X. ???Q., Harris, F. ???A., K. ???K., He, K. ???L., He, Heinsius, F. ???H., Heinz, C. ???H., Heng, Y. ???K., Herold, C., Himmelreich, M., Hou, G. ???Y., Hou, Y. ???R., Hou, Z. ???L., H. ???M., Hu, J. ???F., Hu, Hu, T., Hu, Y., Huang, G. ???S., Huang, K. ???X., Huang, L. ???Q., Huang, X. ???T., Huang, Y. ???P., Huang, Z., Hussain, T., H??sken, N., Imoehl, W., Irshad, M., Jackson, J., Jaeger, S., Janchiv, S., Jang, E., Jeong, J. ???H., Ji, Q., Q. ???P., Ji, X. ???B., Ji, X. ???L., Ji, Y. ???Y., Ji, Jia, Z. ???K., Jiang, H. ???B., Jiang, S. ???S., Jiang, X. ???S., Jiang, Y., Jiao, J. ???B., Jiao, Z., Jin, S., Jin, Y., Jing, M. ???Q., Johansson, T., Kalantar-Nayestanaki, N., Kang, X. ???S., Kappert, R., Kavatsyuk, M., B. ???C., Ke, Keshk, I. ???K., Khoukaz, A., Kiuchi, R., Kliemt, R., Koch, L., Kolcu, O. ???B., Kopf, B., Kuemmel, M., Kuessner, M., Kupsc, A., K??hn, W., Lane, J. ???J., Lange, J. ???S., Larin, P., Lavania, A., Lavezzi, L., Lei, Z. ???H., Leithoff, H., Lellmann, M., Lenz, T., Li, C., C. ???H., Li, Cheng, Li, D. ???M., Li, Li, F., Li, G., Li, H., H. ???B., Li, H. ???J., Li, H. ???N., Li, J. ???Q., Li, J. ???S., Li, J. ???W., Li, Ke, Li, L. J., Li, L. ???K., Li, Lei, Li, M. ???H., Li, P. ???R., Li, S. ???X., Li, S. ???Y., Li, Li, T., W. ???D., Li, W. ???G., Li, X. ???H., Li, X. ???L., Li, Xiaoyu, Li, Z. ???X., Li, Liang, H., Liang, Y. ???F., Liang, Y. ???T., Liao, G. ???R., Liao, L. ???Z., Libby, J., Limphirat, A., Lin, C. ???X., Lin, D. ???X., Lin, T., Liu, B. ???J., Liu, C. ???X., Liu, D., Liu, F. ???H., Fang, Liu, Feng, Liu, Liu, G. ???M., Liu, H., Liu, H. ???B., Liu, H. ???M., Huanhuan, Liu, Huihui, Liu, Liu, J. ???B., Liu, J. ???L., Liu, J. ???Y., Liu, K., Liu, K. ???Y., Liu, Ke, Liu, L., Liu, Lu, Liu, M. ???H., Liu, P. ???L., Liu, Q., Liu, S. ???B., Liu, T., Liu, W. ???K., Liu, W. ???M., Liu, X., Liu, Y., Liu, Y. ???B., Liu, Z. ???A., Liu, Z. ???Q., Lou, X. ???C., F. ???X., Lu, H. ???J., Lu, J. ???G., Lu, X. ???L., Lu, Lu, Y., Y. ???P., Lu, Z. ???H., Lu, Luo, C. ???L., Luo, M. ???X., Luo, T., Luo, X. ???L., Lyu, X. ???R., Lyu, Y. ???F., F. ???C., Ma, H. ???L., Ma, L. ???L., Ma, M. ???M., Ma, Q. ???M., Ma, R. ???Q., Ma, R. ???T., Ma, X. ???Y., Ma, Ma, Y., Maas, F. ???E., Maggiora, M., Maldaner, S., Malde, S., Malik, Q. ???A., Mangoni, A., Mao, Y. ???J., Mao, Z. ???P., Marcello, S., Meng, Z. ???X., Mezzadri, G., Miao, H., Min, T. ???J., Mitchell, R. ???E., X. ???H., Mo, Muchnoi, N. ???Yu., Nefedov, Y., Nerling, F., Nikolaev, I. ???B., Ning, Z., Nisar, S., Niu, Y., Olsen, S. ???L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pelizaeus, M., Peng, H. ???P., Peters, K., Ping, J. ???L., Ping, R. ???G., Plura, S., Pogodin, S., Prasad, V., F. ???Z., Qi, Qi, H., H. ???R., Qi, K. ???H., Qi, Qi, M., T. ???Y., Qi, Qian, S., Qian, W. ???B., Qian, Z., Qiao, C. ???F., Qin, J. ???J., Qin, L. ???Q., Qin, X. ???P., Qin, X. ???S., Qin, Z. ???H., Qiu, J. ???F., S. ???Q., Qu, Rashid, K. ???H., Redmer, C. ???F., Ren, K. ???J., Rivetti, A., Rodin, V., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. ???N., Sang, H. ???S., Sarantsev, A., Schelhaas, Y., Schnier, C., Schoenning, K., Scodeggio, M., Shan, K. ???Y., Shan, W., Shan, X. ???Y., Shangguan, J. ???F., Shao, L. ???G., Shao, M., Shen, C. ???P., Shen, H. ???F., Shen, X. ???Y., Shi, B. ???A., Shi, H. ???C., Shi, J. ???Y., Shi, q. ???q., Shi, R. ???S., Shi, X., Shi, X. D., Song, J. ???J., Song, W. ???M., Song, Y. ???X., Sosio, S., Spataro, S., Stieler, F., K. ???X., Su, P. ???P., Su, Y. ???J., Su, Sun, G. ???X., Sun, H., Sun, H. ???K., Sun, J. ???F., Sun, L., Sun, S. ???S., Sun, T., Sun, W. ???Y., Sun, X, Sun, Y. ???J., Sun, Y. ???Z., Sun, Z. ???T., Tan, Y. ???H., Tan, Y. ???X., Tang, C. ???J., Tang, G. ???Y., Tang, J., Tao, L. Y., Tao, Q. ???T., Tat, M., Teng, J. ???X., Thoren, V., Tian, W. ???H., Tian, Y., Uman, I., Wang, B., Wang, B. ???L., Wang, C. ???W., Wang, D. ???Y., Wang, F., Wang, H. ???J., Wang, H. ???P., Wang, K., Wang, L. ???L., Wang, M., Wang, M. ???Z., Meng, Wang, Wang, S., Wang, T., Wang, T. ???J., Wang, W., Wang, W. ???H., Wang, W. ???P., Wang, X., Wang, X. ???F., Wang, X. ???L., Wang, Y., Wang, Y. ???D., Wang, Y. ???F., Wang, Y. ???H., Wang, Y. ???Q., Yaqian, Wang, Wang, Z., Wang, Z. ???Y., Ziyi, Wang, Wei, D. ???H., Weidner, F., Wen, S. ???P., White, D. ???J., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., J. ???F., Wu, L. ???H., Wu, L. ???J., Wu, Wu, X., X. ???H., Wu, Wu, Y., Y. ???J., Wu, Wu, Z., Xia, L., Xiang, T., Xiao, D., Xiao, G. ???Y., Xiao, H., Xiao, S. ???Y., Xiao, Y. ???L., Xiao, Z. ???J., Xie, C., Xie, X. ???H., Xie, Y., Xie, Y. ???G., Xie, Y. ???H., Xie, Z. ???P., Xing, T. ???Y., C. ???F., Xu, C. ???J., Xu, G. ???F., Xu, H. ???Y., Xu, Q. ???J., Xu, X. ???P., Xu, Y. ???C., Xu, Z. ???P., Xu, Yan, F., Yan, L., Yan, W. ???B., Yan, W. ???C., Yang, H. ???J., Yang, H. ???L., Yang, H. ???X., Yang, L., Yang, S. ???L., Tao, Yang, Yang, Y. ???F., Yang, Y. ???X., Yifan, Yang, Ye, M., M. ???H., Ye, Yin, J. ???H., You, Z. ???Y., B. ???X., Yu, C. ???X., Yu, Yu, G., Yu, T., X. ???D., Yu, Yuan, C. ???Z., Yuan, L., Yuan, S. ???C., Yuan, X. ???Q., Yuan, Y., Yuan, Z. ???Y., Yue, C. ???X., Zafar, A. ???A., Zeng, F. ???R., Zeng, X., Zeng, Y., Zhan, Y. ???H., Zhang, A. ???Q., Zhang, B. ???L., Zhang, B. ???X., Zhang, D. ???H., Zhang, G. ???Y., Zhang, H., Zhang, H. ???H., Zhang, H. ???Y., Zhang, J. ???L., Zhang, J. ???Q., Zhang, J. ???W., Zhang, J. ???X., Zhang, J. ???Y., Zhang, J. ???Z., Jianyu, Zhang, Jiawei, Zhang, Zhang, L. ???M., Zhang, L. ???Q., Lei, Zhang, Zhang, P., Zhang, Q. ???Y., Shuihan, Zhang, Shulei, Zhang, Zhang, X. ???D., Zhang, X. ???M., Zhang, X. ???Y., Zhang, Y., Zhang, Y. ???T., Zhang, Y. ???H., Yan, Zhang, Yao, Zhang, Zhang, Z. ???H., Zhang, Z. ???Y., Zhao, G., Zhao, J., Zhao, J. ???Y., Zhao, J. ???Z., Lei, Zhao, Ling, Zhao, Zhao, M. ???G., Zhao, Q., Zhao, S. ???J., Zhao, Y. ???B., Zhao, Y. ???X., Zhao, Z. ???G., Zhemchugov, A., Zheng, B., Zheng, J. ???P., Zheng, Y. ???H., Zhong, B., Zhong, C., Zhong, X., Zhou, H., Zhou, L. ???P., Zhou, X., Zhou, X. ???K., Zhou, X. ???R., Zhou, X. ???Y., Zhou, Y. ???Z., Zhu, J., Zhu, K., Zhu, K. ???J., Zhu, L. ???X., Zhu, S. ???H., Zhu, S. ???Q., Zhu, T. ???J., Zhu, W. ???J., Zhu, Y. ???C., Zhu, Z. ???A., Zou, B. ???S., and Zou, J. ???H.

Published
2023

48. Search for hyperon ΔS=ΔQ violating decay Ξ0→Σ−e+νe

Author

Ablikim, M., Achasov, M. N., Adlarson, P., Albrecht, M., Aliberti, R., Amoroso, A., M. R., An, An, Q., Bai, X. H., Bai, Y., Bakina, O., Baldini Ferroli, R., Balossino, I., Ban, Y., Batozskaya, V., Becker, D., Begzsuren, K., Berger, N., Bertani, M., Bettoni, D., Bianchi, F., Bloms, J., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Chang, W. L., Chelkov, G., Chen, C., Chen, Chao, Chen, G., Chen, H. S., Chen, M. L., Chen, S. J., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Z. J., Cheng, W. S., Choi, S. K., Chu, X., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., S. X., Du, Egorov, P., Fan, Y. L., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Fischer, K., Fritsch, M., Fritzsch, C., C. D., Fu, Gao, H., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., P. T., Ge, Z. W., Ge, Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Greco, M., L. M., Gu, M. H., Gu, Y. T., Gu, Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, R. P., Guo, Y. P., Guskov, A., Han, T. T., Han, W. Y., Hao, X. Q., Harris, F. A., K. K., He, K. L., He, Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Himmelreich, M., Hou, G. Y., Hou, Y. R., Hou, Z. L., H. M., Hu, J. F., Hu, Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Z., Hussain, T., Hüsken, N., Imoehl, W., Irshad, M., Jackson, J., Jaeger, S., Janchiv, S., Jang, E., Jeong, J. H., Ji, Q., Q. P., Ji, X. B., Ji, X. L., Ji, Y. Y., Ji, Jia, Z. K., Jiang, H. B., Jiang, S. S., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Johansson, T., Kalantar-Nayestanaki, N., Kang, X. S., Kappert, R., Kavatsyuk, M., B. C., Ke, Keshk, I. K., Khoukaz, A., Kiuchi, R., Kliemt, R., Koch, L., Kolcu, O. B., Kopf, B., Kuemmel, M., Kuessner, M., Kupsc, A., Kühn, W., Lane, J. J., Lange, J. S., Larin, P., Lavania, A., Lavezzi, L., Lei, Z. H., Leithoff, H., Lellmann, M., Lenz, T., Li, C., C. H., Li, Cheng, Li, D. M., Li, Li, F., Li, G., Li, H., H. B., Li, H. J., Li, H. N., Li, J. Q., Li, J. S., Li, J. W., Li, Ke, Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, S. X., Li, S. Y., Li, Li, T., W. D., Li, W. G., Li, X. H., Li, X. L., Li, Xiaoyu, Li, Y. G., Li, Z. X., Li, Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Libby, J., Limphirat, A., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, C. X., Liu, D., Liu, F. H., Liu, Fang, Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. M., Liu, Huanhuan, Liu, Huihui, Liu, J. B., Liu, J. L., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. Q., Lou, X. C., F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Lu, Y., Y. P., Lu, Z. H., Lu, Luo, C. L., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., F. C., Ma, H. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, R. T., Ma, X. Y., Ma, Ma, Y., Maas, F. E., Maggiora, M., Maldaner, S., Malde, S., Malik, Q. A., Mangoni, A., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., X. H., Mo, Muchnoi, N. Yu., Nefedov, Y., Nerling, F., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pelizaeus, M., Peng, H. P., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Pogodin, S., Prasad, V., F. Z., Qi, Qi, H., H. R., Qi, Qi, M., T. Y., Qi, Qian, S., Qian, W. B., Qian, Z., Qiao, C. F., Qin, J. J., Qin, L. Q., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., S. Q., Qu, Rashid, K. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rodin, V., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Sang, H. S., Sarantsev, A., Schelhaas, Y., Schnier, C., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, X. Y., Shi, B. A., Shi, H. C., Shi, J. Y., Shi, Q. Q., Shi, R. S., Shi, X., Shi, X. D., Song, J. J., Song, W. M., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., K. X., Su, P. P., Su, Y. J., Su, Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, X., Sun, Y. J., Sun, Y. Z., Sun, Z. T., Tan, Y. H., Tan, Y. X., Tang, C. J., Tang, G. Y., Tang, J., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Uman, I., Wang, B., Wang, B. L., Wang, C. W., Wang, D. Y., Wang, F., Wang, H. J., Wang, H. P., Wang, K., Wang, L. L., Wang, M., Wang, M. Z., Wang, Meng, Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. H., Wang, W. P., Wang, X., Wang, X. F., Wang, X. L., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. Q., Wang, Yaqian, Wang, Z., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., White, D. J., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., J. F., Wu, L. H., Wu, L. J., Wu, Wu, X., X. H., Wu, Wu, Y., Y. J., Wu, Wu, Z., Xia, L., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, Q. J., Xu, X. P., Xu, Y. C., Xu, Z. P., Xu, Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yang, H. J., Yang, H. L., Yang, H. X., Yang, L., Yang, S. L., Yang, Tao, Yang, Y. F., Yang, Y. X., Yang, Yifan, Ye, M., M. H., Ye, Yin, J. H., You, Z. Y., B. X., Yu, C. X., Yu, Yu, G., Yu, T., X. D., Yu, Yuan, C. Z., Yuan, L., Yuan, S. C., Yuan, X. Q., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, X., Zeng, Y., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. H., Zhang, H. Y., Zhang, J. L., Zhang, J. Q., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, Jiawei, Zhang, L. M., Zhang, L. Q., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, Shuihan, Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Yan, Zhang, Yao, Zhang, Z. H., Zhang, Z. Y., Zhao, G., Zhao, J., Zhao, J. Y., Zhao, J. Z., Zhao, Lei, Zhao, Ling, Zhao, M. G., Zhao, Q., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, J. P., Zheng, Y. H., Zhong, B., Zhong, C., Zhong, X., Zhou, H., Zhou, L. P., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, L. X., Zhu, S. H., Zhu, S. Q., Zhu, T. J., Zhu, W. J., Zhu, Y. C., Zhu, Z. A., Zou, B. S., and Zou, J. H.

Published
2023

49. Evidence for the decay

Author

Ablikim, M., Achasov, M. ???N., Adlarson, P., Albrecht, M., Aliberti, R., Amoroso, A., M. ???R., An, An, Q., Bai, Y., Bakina, O., Baldini Ferroli, R., Balossino, I., Ban, Y., Batozskaya, V., Becker, D., Begzsuren, K., Berger, N., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bloms, J., Bortone, A., Boyko, I., Briere, R. ???A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. ???F., Cao, N., Cetin, S. ???A., Chang, J. ???F., Chang, W. ???L., Che, G. ???R., Chelkov, G., Chen, C., Chao, Chen, Chen, G., Chen, H. ???S., Chen, M. ???L., Chen, S. ???J., Chen, S. ???M., Chen, T., Chen, X. ???R., Chen, X. ???T., Chen, Y. ???B., Chen, Z. ???J., Cheng, W. ???S., Choi, S. ???K., Chu, X., Cibinetto, G., Cossio, F., Cui, J. ???J., Dai, H. ???L., Dai, J. ???P., Dbeyssi, A., de Boer, R. ???E., Dedovich, D., Deng, Z. ???Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, Y., Dong, J., Dong, L. ???Y., Dong, M. ???Y., Dong, X., S. ???X., Du, Duan, Z. ???H., Egorov, P., Fan, Y. ???L., Fang, J., Fang, S. ???S., Fang, W. ???X., Fang, Y., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. ???Q., Feng, J. ???H., Fischer, K., Fritsch, M., Fritzsch, C., C. ???D., Fu, Gao, H., Gao, Y. ???N., Yang, Gao, Garbolino, S., Garzia, I., P. ???T., Ge, Z. ???W., Ge, Geng, C., Gersabeck, E. ???M., Gilman, A., Goetzen, K., Gong, L., Gong, W. ???X., Gradl, W., Greco, M., L. ???M., Gu, M. ???H., Gu, Y. ???T., Gu, Guan, C. ???Y., Guo, A. ???Q., Guo, L. ???B., Guo, R. ???P., Guo, Y. ???P., Guskov, A., Han, W. ???Y., Hao, X. ???Q., Harris, F. ???A., K. ???K., He, K. ???L., He, Heinsius, F. ???H., Heinz, C. ???H., Heng, Y. ???K., Herold, C., Hou, G. ???Y., Hou, Y. ???R., Hou, Z. ???L., H. ???M., Hu, J. ???F., Hu, Hu, T., Hu, Y., Huang, G. ???S., Huang, K. ???X., Huang, L. ???Q., Huang, X. ???T., Huang, Y. ???P., Huang, Z., Hussain, T., H??sken, N., Imoehl, W., Irshad, M., Jackson, J., Jaeger, S., Janchiv, S., Jang, E., Jeong, J. ???H., Ji, Q., Q. ???P., Ji, X. ???B., Ji, X. ???L., Ji, Y. ???Y., Ji, Jia, Z. ???K., Jiang, S. ???S., Jiang, X. ???S., Jiang, Y., Jiao, J. ???B., Jiao, Z., Jin, S., Jin, Y., Jing, M. ???Q., Johansson, T., Kalantar-Nayestanaki, N., Kang, X. ???S., Kappert, R., Kavatsyuk, M., B. ???C., Ke, Keshk, I. ???K., Khoukaz, A., Kiuchi, R., Kliemt, R., Koch, L., Kolcu, O. ???B., Kopf, B., Kuemmel, M., Kuessner, M., Kupsc, A., K??hn, W., Lane, J. ???J., Lange, J. ???S., Larin, P., Lavania, A., Lavezzi, L., Lei, T. ???T., Lei, Z. ???H., Leithoff, H., Lellmann, M., Lenz, T., Li, C., C. ???H., Li, Cheng, Li, D. ???M., Li, Li, F., Li, G., Li, H., H. ???B., Li, H. ???J., Li, H. ???N., Li, J. ???Q., Li, J. ???S., Li, J. ???W., Li, Ke, Li, L. ???J., Li, L. ???K., Li, Lei, Li, M. ???H., Li, P. ???R., Li, S. ???X., Li, S. ???Y., Li, Li, T., W. ???D., Li, W. ???G., Li, X. ???H., Li, X. ???L., Li, Xiaoyu, Li, Y. ???G., Li, Z. ???X., Li, Z. ???Y., Li, Liang, C., Liang, H., Liang, Y. ???F., Liang, Y. ???T., Liao, G. ???R., Liao, L. ???Z., Libby, J., Limphirat, A., Lin, C. ???X., Lin, D. ???X., Lin, T., Liu, B. ???J., Liu, C., Liu, C. ???X., Liu, D., Liu, F. ???H., Fang, Liu, Feng, Liu, Liu, G. ???M., Liu, H., Liu, H. ???B., Liu, H. ???M., Huanhuan, Liu, Huihui, Liu, Liu, J. ???B., Liu, J. ???L., Liu, J. ???Y., Liu, K., Liu, K. ???Y., Liu, Ke, Liu, L., Liu, Lu, Liu, M. ???H., Liu, P. ???L., Liu, Q., Liu, S. ???B., Liu, T., Liu, W. ???K., Liu, W. ???M., Liu, X., Liu, Y., Liu, Y. ???B., Liu, Z. ???A., Liu, Z. ???Q., Lou, X. ???C., F. ???X., Lu, H. ???J., Lu, J. ???G., Lu, X. ???L., Lu, Lu, Y., Y. ???P., Lu, Z. ???H., Lu, Luo, C. ???L., Luo, M. ???X., Luo, T., Luo, X. ???L., Lyu, X. ???R., Lyu, Y. ???F., F. ???C., Ma, H. ???L., Ma, L. ???L., Ma, M. ???M., Ma, Q. ???M., Ma, R. ???Q., Ma, R. ???T., Ma, X. ???Y., Ma, Ma, Y., Maas, F. ???E., Maggiora, M., Maldaner, S., Malde, S., Malik, Q. ???A., Mangoni, A., Mao, Y. ???J., Mao, Z. ???P., Marcello, S., Meng, Z. ???X., Messchendorp, J. ???G., Mezzadri, G., Miao, H., Min, T. ???J., Mitchell, R. ???E., X. ???H., Mo, Muchnoi, N. ???Yu., Nefedov, Y., Nerling, F., Nikolaev, I. ???B., Ning, Z., Nisar, S., Niu, Y., Olsen, S. ???L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. ???P., Pelizaeus, M., Peng, H. ???P., Peters, K., Ping, J. ???L., Ping, R. ???G., Plura, S., Pogodin, S., Prasad, V., F. ???Z., Qi, Qi, H., H. ???R., Qi, Qi, M., T. ???Y., Qi, Qian, S., Qian, W. ???B., Qian, Z., Qiao, C. ???F., Qin, J. ???J., Qin, L. ???Q., Qin, X. ???P., Qin, X. ???S., Qin, Z. ???H., Qiu, J. ???F., S. ???Q., Qu, Rashid, K. ???H., Redmer, C. ???F., Ren, K. ???J., Rivetti, A., Rodin, V., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. ???N., Sarantsev, A., Schelhaas, Y., Schnier, C., Schoenning, K., Scodeggio, M., Shan, K. ???Y., Shan, W., Shan, X. ???Y., Shangguan, J. ???F., Shao, L. ???G., Shao, M., Shen, C. ???P., Shen, H. ???F., Shen, X. ???Y., Shi, B. ???A., Shi, H. ???C., Shi, J. ???Y., Shi, q. ???q., Shi, R. ???S., Shi, X., Song, J. ???J., Song, W. ???M., Song, Y. ???X., Sosio, S., Spataro, S., Stieler, F., P. ???P., Su, Y. ???J., Su, Sun, G. ???X., Sun, H., Sun, H. ???K., Sun, J. ???F., Sun, L., Sun, S. ???S., Sun, T., Sun, W. ???Y., Sun, Y. ???J., Sun, Y. ???Z., Sun, Z. ???T., Tan, Y. ???H., Tan, Y. ???X., Tang, C. ???J., Tang, G. ???Y., Tang, J., Tao, L. ???Y., Tao, Q. ???T., Tat, M., Teng, J. ???X., Thoren, V., Tian, W. ???H., Tian, Y., Uman, I., Wang, B., Wang, B. ???L., Wang, C. ???W., Wang, D. ???Y., Wang, F., Wang, H. ???J., Wang, H. ???P., Wang, K., Wang, L. ???L., Wang, M., Wang, M. ???Z., Meng, Wang, Wang, S., Wang, T., Wang, T. ???J., Wang, W., Wang, W. ???H., Wang, W. ???P., Wang, X., Wang, X. ???F., Wang, X. ???L., Wang, Y., Wang, Y. ???D., Wang, Y. ???F., Wang, Y. ???H., Wang, Y. ???Q., Yaqian, Wang, Wang, Z., Wang, Z. ???Y., Ziyi, Wang, Wei, D. ???H., Weidner, F., Wen, S. ???P., White, D. ???J., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., J. ???F., Wu, L. ???H., Wu, L. ???J., Wu, Wu, X., X. ???H., Wu, Wu, Y., Y. ???J., Wu, Wu, Z., Xia, L., Xiang, T., Xiao, D., Xiao, G. ???Y., Xiao, H., Xiao, S. ???Y., Xiao, Y. ???L., Xiao, Z. ???J., Xie, C., Xie, X. ???H., Xie, Y., Xie, Y. ???G., Xie, Y. ???H., Xie, Z. ???P., Xing, T. ???Y., C. ???F., Xu, C. ???J., Xu, G. ???F., Xu, H. ???Y., Xu, Q. ???J., Xu, X. ???P., Xu, Y. ???C., Xu, Z. ???P., Xu, Yan, F., Yan, L., Yan, W. ???B., Yan, W. ???C., Yang, H. ???J., Yang, H. ???L., Yang, H. ???X., Yang, L., Tao, Yang, Yang, Y. ???F., Yang, Y. ???X., Yifan, Yang, Ye, M., M. ???H., Ye, Yin, J. ???H., You, Z. ???Y., B. ???X., Yu, C. ???X., Yu, Yu, G., Yu, T., X. ???D., Yu, Yuan, C. ???Z., Yuan, L., Yuan, S. ???C., Yuan, X. ???Q., Yuan, Y., Yuan, Z. ???Y., Yue, C. ???X., Zafar, A. ???A., Zeng, F. ???R., Zeng, X., Zeng, Y., Zhai, X. ???Y., Zhan, Y. ???H., Zhang, A. ???Q., Zhang, B. ???L., Zhang, B. ???X., Zhang, D. ???H., Zhang, G. ???Y., Zhang, H., Zhang, H. ???H., Zhang, H. ???Y., Zhang, J. ???L., Zhang, J. ???Q., Zhang, J. ???W., Zhang, J. ???X., Zhang, J. ???Y., Zhang, J. ???Z., Jianyu, Zhang, Jiawei, Zhang, Zhang, L. ???M., Zhang, L. ???Q., Lei, Zhang, Zhang, P., Zhang, Q. ???Y., Shuihan, Zhang, Shulei, Zhang, Zhang, X. ???D., Zhang, X. ???M., Zhang, X. ???Y., Zhang, Y., Zhang, Y. ???T., Zhang, Y. ???H., Yan, Zhang, Yao, Zhang, Zhang, Z. ???H., Zhang, Z. ???L., Zhang, Z. ???Y., Zhao, G., Zhao, J., Zhao, J. ???Y., Zhao, J. ???Z., Lei, Zhao, Ling, Zhao, Zhao, M. ???G., Zhao, S. ???J., Zhao, Y. ???B., Zhao, Y. ???X., Zhao, Z. ???G., Zhemchugov, A., Zheng, B., Zheng, J. ???P., Zheng, Y. ???H., Zhong, B., Zhong, C., Zhong, X., Zhou, H., Zhou, L. ???P., Zhou, X., Zhou, X. ???K., Zhou, X. ???R., Zhou, X. ???Y., Zhou, Y. ???Z., Zhu, J., Zhu, K., Zhu, K. ???J., Zhu, L. ???X., Zhu, S. ???H., Zhu, S. ???Q., Zhu, T. ???J., Zhu, W. ???J., Zhu, Y. ???C., Zhu, Z. ???A., Zou, J. ???H., and Zu, J.

Published
2023

50. Study of e+e− → γϕJ/ψ from √s = 4.600 to 4.951 GeV

Author

Ablikim, M., Achasov, M. N., Adlarson, P., Albrecht, M., Aliberti, R., Amoroso, A., M. R., An, An, Q., Bai, Y., Bakina, O., Baldini Ferroli, R., Balossino, I., Ban, Y., Batozskaya, V., Becker, D., Begzsuren, K., Berger, N., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bloms, J., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Chang, W. L., Che, G. R., Chelkov, G., Chen, C., Chen, Chao, Chen, G., Chen, H. S., Chen, M. L., Chen, S. J., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Z. J., Cheng, W. S., Choi, S. K., Chu, X., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., S. X., Du, Duan, Z. H., Egorov, P., Fan, Y. L., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Fischer, K, Fritsch, M., Fritzsch, C., C. D., Fu, Gao, H., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., P. T., Ge, Z. W., Ge, Geng, C., Gersabeck, E. M., Gilman, A, Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Greco, M., L. M., Gu, M. H., Gu, Y. T., Gu, Guan, C. Y, Guo, A. Q., Guo, L. B., Guo, R. P., Guo, Y. P., Guskov, A., Han, W. Y., Hao, X. Q., Harris, F. A., K. K., He, K. L., He, Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Hou, G. Y., Hou, Y. R., Hou, Z. L., H. M., Hu, J. F., Hu, Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Z., Hussain, T., Hüsken, N, Imoehl, W., Irshad, M., Jackson, J., Jaeger, S., Janchiv, S., Jang, E., Jeong, J. H., Ji, Q., Q. P., Ji, X. B., Ji, X. L., Ji, Y. Y., Ji, Jia, Z. K., Jiang, P. C., Jiang, S. S., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kappert, R., Kavatsyuk, M., B. C., Ke, Keshk, I. K., Khoukaz, A., Kiuchi, R., Kliemt, R., Koch, L., Kolcu, O. B., Kopf, B., Kuemmel, M., Kuessner, M., Kupsc, A., Kühn, W., Lane, J. J., Lange, J. S., Larin, P., Lavania, A., Lavezzi, L., Lei, T. T., Lei, Z. H., Leithoff, H., Lellmann, M., Lenz, T., Li, C., Li, YU CHEN, Cheng, Li, D. M., Li, Li, F., Li, G., Li, H., H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. Q., Li, J. S., Li, J. W., Li, Ke, Li, L. J, Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, S. X., Li, S. Y., Li, Li, T., W. D., Li, W. G., Li, X. H., Li, X. L., Li, Xiaoyu, Li, Y. G., Li, Z. X., Li, Z. Y., Li, Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Libby, J., Limphirat, A., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, C., Liu, C. X., Liu, D., Liu, F. H., Liu, Fang, Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. M., Liu, Huanhuan, Liu, Huihui, Liu, J. B., Liu, J. L., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. Q., Lou, X. C., F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Lu, Y., Y. P., Lu, Z. H., Lu, Luo, C. L., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., F. C., Ma, H. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, R. T., Ma, X. Y., Ma, Ma, Y., Maas, F. E., Maggiora, M., Maldaner, S., Malde, S., Malik, Q. A., Mangoni, A., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., X. H., Mo, Muchnoi, N. Yu., Nefedov, Y., Nerling, F., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Pogodin, S., Prasad, V., F. Z., Qi, Qi, H., H. R., Qi, Qi, M., T. Y., Qi, Qian, S., Qian, W. B., Qian, Z., Qiao, C. F., Qin, J. J., Qin, L. Q., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., S. Q., Qu, Rashid, K. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rodin, V., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Sarantsev, A., Schelhaas, Y., Schnier, C., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H. C., Shi, J. Y., Shi, q. q., Shi, R. S., Shi, X., Song, J. J., Song, W. M., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., P. P., Su, Y. J., Su, Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y. J., Sun, Y. Z., Sun, Z. T., Tan, Y. X., Tang, C. J., Tang, G. Y., Tang, J., Tao, L. Y, Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Uman, I., Wang, B., Wang, B. L., Wang, C. W., Wang, D. Y., Wang, F., Wang, H. J., Wang, H. P., Wang, K., Wang, L. L., Wang, M., Wang, Meng, Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. H., Wang, W. P., Wang, X., Wang, X. F., Wang, X. L., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. Q., Wang, Yaqian, Wang, Z., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., White, D. J., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., J. F., Wu, L. H., Wu, L. J., Wu, Wu, X., X. H., Wu, Wu, Y., Y. J, Wu, Wu, Z., Xia, L., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, Q. J., Xu, X. P., Xu, Y. C., Xu, Z. P., Xu, Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yang, H. J., Yang, H. L., Yang, H. X., Yang, Tao, Yang, Y. F., Yang, Y. X., Yang, Yifan, Ye, M., M. H., Ye, Yin, J. H., You, Z. Y., B. X., Yu, C. X., Yu, Yu, G., Yu, T., X. D., Yu, Yuan, C. Z., Yuan, L., Yuan, S. C., Yuan, X. Q., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, X., Zeng, Y., Zhai, X. Y., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. H., Zhang, H. Q., Zhang, H. Y., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, Jiawei, Zhang, L. M., Zhang, L. Q., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, Shuihan, Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Yan, Zhang, Yao, Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhao, G., Zhao, J., Zhao, J. Y., Zhao, J. Z., Zhao, Lei, Zhao, Ling, Zhao, M. G., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, J. P., Zheng, Y. H., Zhong, B., Zhong, C., Zhong, X., Zhou, H., Zhou, L. P., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, L. X., Zhu, S. H., Zhu, S. Q., Zhu, T. J., Zhu, W. J., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
Exotics, Subatomär fysik, Nuclear and High Energy Physics, Astronomi, astrofysik och kosmologi, e(+)-e(-) Experiments, Subatomic Physics, Quarkonium, Astronomy, Astrophysics and Cosmology, Spectroscopy
Abstract

Using data samples with an integrated luminosity of 6.4 fb−1 collected by the BESIII detector operating at the BEPCII storage ring, the process of e+e− → γϕJ/ψ is studied. The processes of e+e− → ϕχc1,c2, χc1,c2 → γJ/ψ are observed with a significance of more than 10σ. The $$ \sqrt{s} $$ s -dependent cross section of e+e− → ϕχc1,c2 is measured between 4.600 and 4.951 GeV, and evidence of a resonance structure is found for the first time in the ϕχc2 process. We also search for the processes of e+e− → γX(4140), γX(4274) and γX(4500) via the γϕJ/ψ final state, but no obvious structures are found. The upper limits on the production cross section times the branching fraction for these processes at the 90% confidence level are reported.

Published
2023

Catalog

Books, media, physical & digital resources
See catalog results