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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Yunyun Wang Shaojin Liu Lianming Wang Jipeng Huang Institute of computational intelligence Northeast Normal University Changchun Jilin China School of Electrical and Computer Engineering Purdue University West Lafayette USA School of Ocean Science and Technology Hainan Tropical Ocean University Hainan Sanya China

Aiming at the problems of poor flexibility, complicated path maintenance and poor positioning performance in the current guidance technology of AGV, this paper designs and implements a new navigation system of automated guided vehicle (AGV) based on AprilTags2 auxiliary positioning. The system uses the Robot Operating System (ROS) as a platform to develop AGV navigation functions. The navigation system comprises two parts: the hardware and software layers. Firstly, hardware selection is performed after considering the actual requirements, performance, cost, and other factors in the hardware layer. Simultaneously, the AGV chassis and single-steering wheel walking mechanism are built to provide a stable and flexible operating platform for the software layer. Secondly, the software layer design includes two parts, namely the ROS navigation planning end and AprilTags2 detection. The ROS planning navigation end performs the design of four functional modules (i.e., AGV map construction, autonomous positioning, path planning, and path tracking), while the AprilTags2 detection part obtains the visual positioning pose of AGV by setting the AprilTags2 on each site and using the Kinect1 camera to detect. A more accurate AGV pose can be obtained by merging the former pose with the kinematic estimated pose. Finally, the two groups of positioning errors of the system before and after adding the AprilTags2 auxiliary positioning are tested and compared. The experimental results show that the navigation accuracy of the proposed system with AprilTags2 auxiliary positioning is significantly improved.

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A multi-level neural network for implicit causality detection in Web texts

arXiv

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arXiv 2019年

作者： Liang, Shining Zuo, Wanli Shi, Zhenkun Wang, Sen Wang, Junhu Zuo, Xianglin Key Laboratory of Symbol Computation Knowledge Engineering of Ministry of Education Changchun China College of Computer Science and Technology Jilin University Jilin China Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia School of Information and Communication Technology Griffith University QLD Australia

Mining causality from text is a complex and crucial natural language understanding task corresponding to the human cognition. Existing studies at its solution can be grouped into two primary categories: feature engineering based and neural model based methods. In this paper, we find that the former has incomplete coverage and inherent errors but provide prior knowledge;while the latter leverages context information but causal inference of which is insufficiency. To handle the limitations, we propose a novel causality detection model named MCDN to explicitly model causal reasoning process, and furthermore, to exploit the advantages of both methods. Specifically, we adopt multi-head self-attention to acquire semantic feature at word level and develop the SCRN to infer causality at segment level. To the best of our knowledge, with regards to the causality tasks, this is the first time that the Relation Network is applied. The experimental results show that: i) the proposed approach performs prominent performance on causality detection;ii) further analysis manifests the effectiveness and robustness of MCDN. © 2019, CC BY-NC-SA.

关键词： Semantics

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Potential of core-collapse supernova neutrino detection at JUNO

Potential of core-collapse supernova neutrino detection at J...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Huang, Xin Abusleme, Angel Ahmad, Shakeel Ahmed, Rizwan Aiello, Sebastiano Akram, Muhammad An, Fengpeng An, Qi Andronico, Giuseppe Anfimov, Nikolay Antonelli, Vito Antoshkina, Tatiana Asavapibhop, Burin de André, João Pedro Athayde Marcondes Auguste, Didier Babic, Andrej Balashov, Nikita Baldini, Wander Barresi, Andrea Basilico, Davide Baussan, Eric Bellato, Marco Bergnoli, Antonio Birkenfeld, Thilo Blin, Sylvie Blum, David Blyth, Simon Bolshakova, Anastasia Bongrand, Mathieu Bordereau, Clément Breton, Dominique Brigatti, Augusto Brugnera, Riccardo Bruno, Riccardo Budano, Antonio Buscemi, Mario Busto, Jose Butorov, Ilya Cabrera, Anatael Cai, Hao Cai, Xiao Cai, Yanke Cai, Zhiyan Callegari, Riccardo Cammi, Antonio Campeny, Agustin Cao, Chuanya Cao, Guofu Cao, Jun Caruso, Rossella Cerna, Cédric Chang, Jinfan Chang, Yun Chen, Pingping Chen, Po-An Chen, Shaomin Chen, Xurong Chen, Yi-Wen Chen, Yixue Chen, Yu Chen, Zhang Cheng, Jie Cheng, Yaping Chetverikov, Alexey Chiesa, Davide Chimenti, Pietro Chukanov, Artem Claverie, Gérard Clementi, Catia Clerbaux, Barbara Di Lorenzo, Selma Conforti Corti, Daniele Dal Corso, Flavio Dalager, Olivia De La Taille, Christophe Deng, Jiawei Deng, Zhi Deng, Ziyan Depnering, Wilfried Diaz, Marco Ding, Xuefeng Ding, Yayun Dirgantara, Bayu Dmitrievsky, Sergey Dohnal, Tadeas Dolzhikov, Dmitry Donchenko, Georgy Dong, Jianmeng Doroshkevich, Evgeny Dracos, Marcos Druillole, Frédéric Du, Ran Du, Shuxian Dusini, Stefano Dvorak, Martin Enqvist, Timo Enzmann, Heike Fabbri, Andrea Fajt, Lukas Fan, Donghua Fan, Lei Fang, Jian Fang, Wenxing Fargetta, Marco Fedoseev, Dmitry Fekete, Vladko Feng, Li-Cheng Feng, Qichun Ford, Richard Fournier, Amélie Gan, Haonan Gao, Feng Garfagnini, Alberto Gavrikov, Arsenii Giammarchi, Marco Giaz, Agnese Giudice, Nunzio Gonchar, Maxim Gong, Guanghua Gong, Hui Gornushkin, Yuri Göttel, Alexandre Grassi, Marco Grewing, Christian Gromov, Vasily Gu, Minghao Gu, Xiaofei Gu, Yu Guan, Mengyun Guardone, Nunzio Gul, Maria Guo, Cong Guo, Jingyuan Guo, Wanlei Guo, Xinheng Guo, Yuhan Institute of High Energy Physics Chinese Academy of Science Beijing China Yerevan Physics Institute Yerevan Armenia Université Libre de Bruxelles Brussels Belgium Universidade Estadual de Londrina Londrina Brazil Pontificia Universidade Catolica do Rio de Janeiro Brazil Pontificia Universidad Católica de Chile Santiago Chile Universidad Tecnica Federico Santa Maria Valparaiso Chile Beijing Institute of Spacecraft Environment Engineering Beijing China Beijing Normal University Beijing China China Institute of Atomic Energy Beijing China Institute of High Energy Physics Beijing China North China Electric Power University Beijing China School of Physics Peking University Beijing China Tsinghua University Beijing China University of Chinese Academy of Sciences Beijing China Jilin University Changchun China College of Electronic Science and Engineering National University of Defense Technology Changsha China Chongqing University Chongqing China Dongguan University of Technology Dongguan China Jinan University Guangzhou China Sun Yat-Sen University Guangzhou China Harbin Institute of Technology Harbin China University of Science and Technology of China Hefei China The Radiochemistry and Nuclear Chemistry Group University of South China Hengyang China Wuyi University Jiangmen China Shandong University Jinan China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Qingdao China Institute of Modern Physics Chinese Academy of Sciences Lanzhou China Nanjing University Nanjing China Guangxi University Nanning China East China University of Science and Technology Shanghai China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai China Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological Sciences Shijiazhuang China Nankai University Tianjin China Wuhan University Wuhan China Xi’an

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on 12C nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve. This talk gives an overview of physics potential of CCSN neutrino detection in JUNO. © Copyright owned by the author(s).

关键词： Supernovae

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A Constraint-Based Model for Virtual Machine Data Access Control in Cloud Platform

A Constraint-Based Model for Virtual Machine Data Access Con...

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2018国际计算机前沿大会（原国际青年计算机大会）

作者： Zhixin Li Lei Liu Xin Wang College of Computer Science and Technology Jilin University School of Computer Technology and Engineering Changchun Institute of Technology

Virtual machine(VM) data access control provides a cloudcomputing platform with guaranteed safety. Given that the cloud platform envi‐ronment is dynamically variable, static VM data access operational authorization is different from the dynamic cloud platform environment in state *** difference affects the safety and performance of VMs in the entire cloud platform. A constraint-based VM data access control model was proposed in this study to evaluate the influence of dynamic environmental change in a cloud plat‐form on VM data access control operation. The state information of the dynamic cloud environment was considered a constraint evaluation function. The model realized organic integration of static Bell–LaPadula model safety level and dynamic cloud platform environmental information. A safety policy of VM data access control operation was established, and the capability of the constraintbased access control model to improve the safety of VMs was verified. A model implementation framework and the main functions in combination with the proposed model were realized. The effectiveness and performance of the constraint-based VM data access control model were also evaluated. Results showed that the performance loss was within 7% when the constrained VM data access control model was used for operations, such as VM management. The test of communication intensive workload of a VM indicated that the operating time of the model was increased by approximately 4%. The constraint-based VM data access control model in cloud platform could adapt to the complex dynamic cloud platform environment and improve the safety of VMs. This study provided tech‐nical and theoretical bases for VM data access control in cloud platform.

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stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs

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Nature communications 2025年第1期16卷 5317页

作者： Chunman Zuo Junjie Xia Yupeng Xu Ying Xu Pingting Gao Jing Zhang Yan Wang Luonan Chen School of Life Sciences Sun Yat-sen University Guangzhou China. zuochm@***. Institute of Artificial Intelligence Donghua University Shanghai China. zuochm@***. Shanghai Collaborative Innovation Center of Endoscopy Fudan University Shanghai China. zuochm@***. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China. zuochm@***. Institute of Artificial Intelligence Donghua University Shanghai China. System Biology Lab for Metabolic Reprogramming Department of Human Genetics and Cell Biology School of Medicine Southern University of Science and Technology Shenzhen China. Shanghai Collaborative Innovation Center of Endoscopy Fudan University Shanghai China. Department of Pathology Changzheng Hospital Secondary Military Medical University Shanghai China. College of Computer Science and Technology Jilin University Changchun China. School of Mathematical Sciences Shanghai Jiao Tong University Shanghai China. lnchen@***. School of AI Shanghai Jiao Tong University Shanghai China. lnchen@***. Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai China. lnchen@***. Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Chinese Academy of Sciences Hangzhou China. lnchen@***. West China Biomedical Big Data Center Med-X Center for Informatics West China Hospital Sichuan University Chengdu China. lnchen@***.

Spatial multi-slice multi-omics (SMSMO) integration has transformed our understanding of cellular niches, particularly in tumors. However, challenges like data scale and diversity, disease heterogeneity, and limited sample population size, impede the derivation of clinical insights. Here, we propose stClinic, a dynamic graph model that integrates SMSMO and phenotype data to uncover clinically relevant niches. stClinic aggregates information from evolving neighboring nodes with similar-profiles across slices, aided by a Mixture-of-Gaussians prior on latent features. Furthermore, stClinic directly links niches to clinical manifestations by characterizing each slice with attention-based geometric statistical measures, relative to the population. In cancer studies, stClinic uses survival time to assess niche malignancy, identifying aggressive niches enriched with tumor-associated macrophages, alongside favorable prognostic niches abundant in B and plasma cells. Additionally, stClinic identifies a niche abundant in SPP1+ MTRNR2L12+ myeloid cells and cancer-associated fibroblasts driving colorectal cancer cell adaptation and invasion in healthy liver tissue. These findings are supported by independent functional and clinical data. Notably, stClinic excels in label annotation through zero-shot learning and facilitates multi-omics integration by relying on other tools for latent feature initialization.

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3D Point Cloud Surface Reconstruction Based on Divide-and-Conquer Method in Laser Scanner

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Journal of Physics: Conference Series 2020年第1期1544卷

作者： Yuan-Zhi Lyu Min Guo Ou Sha Hong-Yu Zhang Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Science NO. 3888 South Nanhu Road Changchun Jilin Province CHN 86-*** 86-0431-81295016 86-*** 86-*** College of computer science and engineering Changchun University of Technology NO. 2055 Yanan Street Changchun Jilin Province CHN 86-*** 86-0431-81295016 86-*** 86-***

As to the problem of surface reconstruction in 3D laser scanner, we propose a divide-and-conquer based method, and realize the continuing growth of the reconstructed surface through comprehensive analysis of the correlations of scanning line point cloud data. First of all, filter the scanning line point cloud data, then confirm the relationship between the newly-insert point and the generated surface by using the local search algorithm based on adaptive point cloud segmentation, and finally, classify the newly-inserted points as sparse points, density points, growth points and flyback points by using the given maximum and minimum distance thresholds, and then the specific surface reconstruction is performed according to the point's classification. The experimental results show that the local search algorithm greatly reduces the calculation of the insert location of the point cloud compared with the global traversal search algorithm; the proposed method can process the flyback points, enhancing the repairability and the expandability of the reconstructed 3D surface compared with the traditional method. Furthermore, the quality of the reconstructed 3D surface, the generating speed and the occupation of storage space can all be controlled by adjusting the maximum and minimum distance thresholds according to actual demand.

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CenterNet3D: An anchor free object detector for point cloud

arXiv

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arXiv 2020年

作者： Wang, Guojun Wu, Jian Tian, Bin Teng, Siyu Chen, Long Cao, Dongpu The State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130022 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China The Department of Computer Science Hong Kong Baptist University Hong Kong The School of Data and Computer Science Sun Yat-Sen University Guangdong Guangzhou510275 China The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Accurate and fast 3D object detection from point clouds is a key task in autonomous driving. Existing one-stage 3D object detection methods can achieve real-time performance, however, they are dominated by anchor-based detectors which are inefficient and require additional post-processing. In this paper, we eliminate anchors and model an object as a single point-the center point of its bounding box. Based on the center point, we propose an anchor-free CenterNet3D network that performs 3D object detection without anchors. Our CenterNet3D uses keypoint estimation to find center points and directly regresses 3D bounding boxes. However, because inherent sparsity of point clouds, 3D object center points are likely to be in empty space which makes it difficult to estimate accurate boundaries. To solve this issue, we propose an extra corner attention module to enforce the CNN backbone to pay more attention to object boundaries. Besides, considering that one-stage detectors suffer from the discordance between the predicted bounding boxes and corresponding classification confidences, we develop an efficient keypoint-sensitive warping operation to align the confidences to the predicted bounding boxes. Our proposed CenterNet3D is non-maximum suppression free which makes it more efficient and simpler. We evaluate CenterNet3D on the widely used KITTI dataset and more challenging nuScenes dataset. Our method outperforms all state-of-the-art anchor-based one-stage methods and has comparable performance to two-stage methods as well. It has an inference speed of 20 FPS and achieves the best speed and accuracy trade-off. Our source code will be released at https://***/wangguojun2018/CenterNet3d. Copyright © 2020, The Authors. All rights reserved.

关键词： Autonomous vehicles

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JUNO Sensitivity to Invisible Decay Modes of Neutrons

arXiv

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arXiv 2024年

作者： Abusleme, Angel Adam, Thomas Adamowicz, Kai Ahmad, Shakeel Ahmed, Rizwan Aiello, Sebastiano An, Fengpeng An, Qi Andronico, Giuseppe Anfimov, Nikolay Antonelli, Vito Antoshkina, Tatiana deAndré, João Pedro Athayde Marcondes Auguste, Didier Bai, Weidong Balashov, Nikita Baldini, Wander Barresi, Andrea Basilico, Davide Baussan, Eric Bellato, Marco Beretta, Marco Bergnoli, Antonio Bick, Daniel Bieger, Lukas Biktemerova, Svetlana Birkenfeld, Thilo Blake, Iwan Blyth, Simon Bolshakova, Anastasia Bongrand, Mathieu Breton, Dominique Brigatti, Augusto Brugnera, Riccardo Bruno, Riccardo Budano, Antonio Busto, Jose Cabrera, Anatael Caccianiga, Barbara Cai, Hao Cai, Xiao Cai, Yanke Cai, Zhiyan Callier, Stéphane Calvez, Steven Cammi, Antonio Campeny, Agustin Cao, Chuanya Cao, Guofu Cao, Jun Caruso, Rossella Cerna, Cédric Cerrone, Vanessa Chang, Jinfan Chang, Yun Chatrabhuti, Auttakit Chen, Chao Chen, Guoming Chen, Pingping Chen, Shaomin Chen, Xin Chen, Yiming Chen, Yixue Chen, Yu Chen, Zelin Chen, Zhangming Chen, Zhiyuan Chen, Zikang Cheng, Jie Cheng, Yaping Cheng, Yu Chin Chepurnov, Alexander Chetverikov, Alexey Chiesa, Davide Chimenti, Pietro Chin, Yen-Ting Chou, Po-Lin Chu, Ziliang Chukanov, Artem Claverie, Gérard Clementi, Catia Clerbaux, Barbara Molla, Marta Colomer Di Lorenzo, Selma Conforti Coppi, Alberto Corti, Daniele Csakli, Simon Cui, Chenyang Corso, Flavio Dal Dalager, Olivia Datta, Jaydeep De La Taille, Christophe Deng, Zhi Deng, Ziyan Ding, Xiaoyu Ding, Xuefeng Ding, Yayun Dirgantara, Bayu Dittrich, Carsten Dmitrievsky, Sergey Dohnal, Tadeas Dolzhikov, Dmitry Donchenko, Georgy Dong, Jianmeng Doroshkevich, Evgeny Dou, Wei Dracos, Marcos Druillole, Frédéric Du, Ran Du, Shuxian Duan, Yujie Dugas, Katherine Dusini, Stefano Duyang, Hongyue Eck, Jessica Enqvist, Timo Fabbri, Andrea Fahrendholz, Ulrike Fan, Lei Fang, Jian Fang, Wenxing Fedoseev, Dmitry Feng, Li-Cheng Feng, Qichun Ferraro, Federico Fournier, Amélie Fritsch, Fritsch Gan, Haonan Gao, Feng Gao, Feng Garfagnini, Alberto Gavrikov, Arsenii Giammarchi, Marco Gi Yerevan Physics Institute Yerevan Armenia Université Libre de Bruxelles Brussels Belgium Universidade Estadual de Londrina Londrina Brazil Pontificia Universidade Catolica do Rio de Janeiro Rio de Janeiro Brazil ANID Chile Pontificia Universidad Católica de Chile Santiago Chile Beijing Institute of Spacecraft Environment Engineering Beijing China Beijing Normal University Beijing China China Institute of Atomic Energy Beijing China Institute of High Energy Physics Beijing China North China Electric Power University Beijing China School of Physics Peking University Beijing China Tsinghua University Beijing China University of Chinese Academy of Sciences Beijing China Jilin University Changchun China College of Electronic Science and Engineering National University of Defense Technology Changsha China Chengdu University of Technology Chengdu China Chongqing University Chongqing China Dongguan University of Technology Dongguan China Jinan University Guangzhou China Sun Yat-Sen University Guangzhou China Harbin Institute of Technology Harbin China University of Science and Technology of China Hefei China The Radiochemistry and Nuclear Chemistry Group University of South China Hengyang China Wuyi University Jiangmen China Shandong University Jinan China Key Laboratory of Particle Physics and Particle Irradi- ation Ministry of Education Shandong University Qingdao China Nanjing University Nanjing China Guangxi University Nanning China East China University of Science and Technology Shanghai China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai China Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological Sciences Shijiazhuang China Nankai University Tianjin China Wuhan University Wuhan China Xi’an Jiaotong University Xi’an China Xiamen University Xiamen China School of Physics and Microelectronics Zhengzhou University Z

We explore the decay of bound neutrons into invisible particles (e.g., n → 3ν or nn → 2ν) in the JUNO liquid scintillator detector, which do not produce an observable signal. The invisible decay includes two decay modes: n → inv and nn → inv. The invisible decays of s-shell neutrons in 12C will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino ν¯e, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are τ/B(n → inv) > 5.0 × 1031 yr and τ/B(nn → inv) > 1.4 × 1032 yr. Copyright © 2024, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Focus measure based on the image moments 15

Focus measure based on the image moments

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15th IEEE International Conference on Mechatronics and Automation, ICMA 2018

作者： Guo, Liqiang Liu, Lian Sun, Haijiang School of Computer Science and Technology Huaiyin Normal University Huai'an Jiangsu Province China School of Physics and Electronic Electrical Engineering Huaiyin Normal University Huai'an Jiangsu Province China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academic Sciences Changchun Jilin Province China

ISBN: (纸本)9781538660720

Focus measure plays an important role in the passive optical imaging systems. However, most of existing focus measure is prone to be affected under the noisy conditions. In this study, we proposed a novel focus measure by using the blocked image moment functions and variance statistics. We first divide the image into 8 × 8 non-overlapping blocks, and compute the Chebyshev moment T11 for each image blocks, then use the statistical method to construct the focus measure. The experimental results under different noisy conditions indicate the robustness of the proposed focus measure. © 2018 IEEE.

关键词： Noise measurement Frequency measurement Chebyshev approximation Robustness Indexes Cameras Image quality

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Potential to identify neutrino mass ordering with reactor antineutrinos at JUNO

arXiv

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arXiv 2024年

作者： Abusleme, Angel Adam, Thomas Ahmad, Shakeel Ahmed, Rizwan Aiello, Sebastiano Akram, Muhammad Aleem, Abid An, Fengpeng An, Qi Andronico, Giuseppe Anfimov, Nikolay Antonelli, Vito Antoshkina, Tatiana Asavapibhop, Burin de André, João Pedro Athayde Marcondes Auguste, Didier Bai, Weidong Balashov, Nikita Baldini, Wander Barresi, Andrea Basilico, Davide Baussan, Eric Bellato, Marco Beretta, Marco Bergnoli, Antonio Bick, Daniel Bieger, Lukas Biktemerova, Svetlana Birkenfeld, Thilo Morton-Blake, Iwan Blum, David Blyth, Simon Bolshakova, Anastasia Bongrand, Mathieu Bordereau, Clément Breton, Dominique Brigatti, Augusto Brugnera, Riccardo Bruno, Riccardo Budano, Antonio Busto, Jose Cabrera, Anatael Caccianiga, Barbara Cai, Hao Cai, Xiao Cai, Yanke Cai, Zhiyan Callier, Stéphane Cammi, Antonio Campeny, Agustin Cao, Chuanya Cao, Guofu Cao, Jun Caruso, Rossella Cerna, Cédric Cerrone, Vanessa Chan, Chi Chang, Jinfan Chang, Yun Chatrabhuti, Auttakit Chen, Chao Chen, Guoming Chen, Pingping Chen, Shaomin Chen, Yixue Chen, Yu Chen, Zhangming Chen, Zhiyuan Chen, Zikang Cheng, Jie Cheng, Yaping Cheng, Yu Chin Chepurnov, Alexander Chetverikov, Alexey Chiesa, Davide Chimenti, Pietro Chin, Yen-Ting Chu, Ziliang Chukanov, Artem Claverie, Gérard Clementi, Catia Clerbaux, Barbara Molla, MartaColomer Di Lorenzo, Selma Conforti Coppi, Alberto Corti, Daniele Csakli, Simon Corso, Flavio Dal Dalager, Olivia Datta, Jaydeep De LaTaille, Christophe Deng, Zhi Deng, Ziyan Ding, Xiaoyu Ding, Xuefeng Ding, Yayun Dirgantara, Bayu Dittrich, Carsten Dmitrievsky, Sergey Dohnal, Tadeas Dolzhikov, Dmitry Donchenko, Georgy Dong, Jianmeng Doroshkevich, Evgeny Dou, Wei Dracos, Marcos Druillole, Frédéric Du, Ran Du, Shuxian Dugas, Katherine Dusini, Stefano Duyang, Hongyue Eck, Jessica Enqvist, Timo Fabbri, Andrea Fahrendholz, Ulrike Fan, Lei Fang, Jian Fang, Wenxing Fargetta, Marco Fedoseev, Dmitry Fei, Zhengyong Feng, Li-Cheng Feng, Qichun Ferraro, Federico Fournier, Amélie Gan, Haonan Gao, Feng Garfagnini, Alberto Gavrikov, Arsenii Giammarchi, Marco Giudice, Yerevan Physics Institute Yerevan Armenia Université Libre de Bruxelles Brussels Belgium Universidade Estadual de Londrina Londrina Brazil Pontificia Universidade Catolica do Rio de Janeiro Rio de Janeiro Brazil ANID Chile Pontificia Universidad Católica de Chile Santiago Chile Beijing Institute of Spacecraft Environment Engineering Beijing China Beijing Normal University Beijing China China Institute of Atomic Energy Beijing China Institute of High Energy Physics Beijing China North China Electric Power University Beijing China School of Physics Peking University Beijing China Tsinghua University Beijing China University of Chinese Academy of Sciences Beijing China Jilin University Changchun China College of Electronic Science and Engineering National University of Defense Technology Changsha China Chongqing University Chongqing China Dongguan University of Technology Dongguan China Jinan University Guangzhou China Sun Yat-Sen University Guangzhou China Harbin Institute of Technology Harbin China University of Science and Technology of China Hefei China The Radiochemistry and Nuclear Chemistry Group University of South China Hengyang China Wuyi University Jiangmen China Shandong University Jinan China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Qingdao China Nanjing University Nanjing China Guangxi University Nanning China East China University of Science and Technology Shanghai China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai China Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological Sciences Shijiazhuang China Nankai University Tianjin China Wuhan University Wuhan China Xi’an Jiaotong University Xi’an China Xiamen University Xiamen China School of Physics and Microelectronics Zhengzhou University Zhengzhou China Institute of Physics National Ya

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment under construction in South China. This paper presents an updated estimate of JUNO’s sensitivity to neutrino mass ordering using the reactor antineutrinos emitted from eight nuclear reactor cores in the Taishan and Yangjiang nuclear power plants. This measurement is planned by studying the fine interference pattern caused by quasi-vacuum oscillations in the oscillated antineutrino spectrum at a baseline of 52.5 km and is completely independent of the CP violating phase and neutrino mixing angle θ23. The sensitivity is obtained through a joint analysis of JUNO and Taishan Antineutrino Observatory (TAO) detectors utilizing the best available knowledge to date about the location and overburden of the JUNO experimental site, local and global nuclear reactors, JUNO and TAO detector responses, expected event rates and spectra of signals and backgrounds, and systematic uncertainties of analysis inputs. We find that a 3σ median sensitivity to reject the wrong mass ordering hypothesis can be reached with an exposure to approximately 6.5 years × 26.6 GW thermal power. © 2024, CC BY.

关键词： Nuclear power plants

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