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Anharmonicity and Universal Response of Linear Carbon Chain Mechanical Properties under Hydrostatic Pressure

Physical Review Letters 2020年第10期125卷 105501-105501页

作者： Keshav Sharma Nathalia L. Costa Yoong Ahm Kim Hiroyuki Muramatsu Newton M. Barbosa Neto Luiz G. P. Martins Jing Kong Alexandre Rocha Paschoal Paulo T. Araujo Department of Physics and Astronomy University of Alabama Tuscaloosa Alabama 35487 USA Department of Physics Federal University of Ceara 60455-760 Fortaleza Ceara Brazil Department of Polymer Engineering School of Polymer Science and Engineering and Alan G. MacDiarmid Energy Research Institute Chonnam National University 77 Yongbong-ro Buk-gu Gwangju 61186 Republic of Korea Research Institute for Supra-Materials Shinshu University 4-17-1 Wakasato Nagano Japan Institute of Natural Sciences Graduate Program in Physics Federal University of Para 66075-110 Belem PA Brazil Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

Isolated linear carbon chains (LCCs) encapsulated by multiwalled carbon nanotubes are studied under hydrostatic pressure (P) via resonance Raman scattering. The LCCs’ spectroscopic signature C band around 1850 cm−1 softens linearly with increasing P. A simple anharmonic force-constant model not only describes such softening but also shows that the LCCs’ Young’s modulus (E), Grüneisen parameter (γ), and strain (ϵ) follow universal P−1 and P2 laws, respectively. In particular, γ also presents a unified behavior for all LCCs. To the best of our knowledge, these are the first results reported on such isolated systems and the first work to explore universal P-dependent responses for LCCs’ E, ϵ, and γ.

关键词： Anharmonic lattice dynamics Compressive strength Elasticity Mechanical & acoustical properties Pressure effects Stress 1-dimensional systems Lattice models in condensed matter Pressure techniques Raman spectroscopy

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Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

arXiv

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arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

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A privacy-preserving traffic monitoring scheme via vehicular crowdsourcing

arXiv

引用

arXiv 2019年

作者： Zhang, Chuan Zhu, Liehuang Xu, Chang Du, Xiaojiang Guizani, Mohsen Beijing Eng. Res. Center of Massive Language Information Processing and Cloud Computing Application School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Computer and Information Sciences Temple University Philadelphia United States College of Engineering Qatar University

The explosive growth of vehicle amount has given rise to a series of traffic problems, such as traffic congestion, road safety, and fuel waste. Collecting vehicles' speed information is an effective way to monitor the traffic condition and avoid vehicles being congested, which however may bring threats to vehicles' location and trajectory privacy. Motivated by the fact that traffic monitoring does not need to know each individual vehicle's speed and the average speed would be sufficient, we propose a privacy-preserving traffic monitoring (PPTM) scheme to aggregate vehicles' speeds at different locations. In PPTM, the roadside unit (RSU) collects vehicles' speed information at multiple road segments, and further cooperates with a service provider to calculate the average speed information for every road segment. To preserve vehicles' privacy, both homomorphic Paillier cryptosystem and super-increasing sequence are adopted. A comprehensive security analysis indicates that the proposed PPTM can preserve vehicles' identities, speeds, locations, and trajectories privacy from being disclosed. In addition, extensive simulations are conducted to validate the effectiveness and efficiency of the proposed PPTM scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Speed

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Artificial intelligence enabled radio propagation for communications - Part I: Channel characterization and antenna-channel optimization

arXiv

引用

arXiv 2021年

作者： Huang, Chen He, Ruisi Ai, Bo Molisch, Andreas F. Lau, Buon Kiong Haneda, Katsuyuki Liu, Bo Wang, Cheng-Xiang Yang, Mi Oestges, Claude Zhong, Zhangdui The Purple Mountain Laboratories Nanjing211111 China The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China Peng Cheng Laboratory Shenzhen518055 China The Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States The Department of Electrical and Information Technology Lund University Lund22100 Sweden The Department of Radio Science and Engineering Aalto University Aalto00076 Finland The School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The Purple Mountain Laboratories Nanjing211111 China The Institute of Information and Communication Technologies Electronics and Applied Mathematics Universite Catholique de Louvain Louvain-la-Neuve 1348 Belgium

To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques. In this two-part paper, we investigate the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. It firstly provides a comprehensive overview of ML for channel characterization and ML-based antenna-channel optimization in this first part, and then it gives a state-of-the-art literature review of channel scenario identification and channel modeling in Part II. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML methods for channel data processing, propagation channel estimation, and characterization are analyzed and compared. A discussion of challenges and future research directions for ML-enabled next generation networks of the topics covered in this part rounds off the paper. © 2021, CC BY-NC-ND.

关键词： Machine learning

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Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

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Computing and Software for Big science 2024年第1期8卷 1-36页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Hussain, P.S. Jeitler, M. Krammer, N. Li, A. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Mechelen, P. Van Bols, E.S. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. Faham, H. El Lowette, S. Makarenko, I. Müller, D. Sahasransu, A.R. Tavernier, S. Tytgat, M. Onsem, G. P. Van Putte, S. Van Vannerom, D. Clerbaux, B. Das, A.K. De Lentdecker, G. Favart, L. Gianneios, P. Hohov, D. Jaramillo, J. Khalilzadeh, A. Khan, F.A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Thomas, L. Bemden, M. Vanden Velde, C. Vander Vanlaer, P. De Coen, M. Dobur, D. Hong, Y. Knolle, J. Lambrecht, L. Mestdach, G. Amarilo, K. Mota Rendón, C. Samalan, A. Skovpen, K. Bossche, N. Van Den Linden, J. van der Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sh. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. Tran, T.T. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. De Oliveira, T. Menezes Moraes, A. Teles, P. Rebello Soeiro, M. Júnior, W. L. Aldá Pereira, M. Alves Gallo Filho, M. Barroso Ferreira Malbouisson, H. Brandao Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. De Souza, S. Fonseca De Souza, R. Gomes Martins, J. Herrera, C. Mora Mundim, L. Nogima, H. Pinheiro, J.P. Santoro, A. Sznajder, A. Thiel, M. Pereira, A. Vilela Bernardes, C.A. Calligaris, L. Tomei, T. R. Fernandez Perez Gregores, E.M. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Javaid, T. Yuan, L. Hu, Z. Liu, J. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Sharma, R. Song, J.N. Tao, J. Wang, C. Wang, J. Wang, Z. Zhang, H. Agapitos Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-sen University Guangzhou China University of Science and Technology of China Hefei China Nanjing Normal University Nanjing China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University Shanghai China Zhejiang University Hangzhou Zhejiang China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia Faculty of Science University of Split Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysic

Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors. © The Author(s) 2024.

关键词： CMS Machine learning Offline and computing

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Image reconstruction with low-rankness and self-consistency of k-space data in parallel MRI

arXiv

引用

arXiv 2019年

作者： Zhang, Xinlin Guo, Di Huang, Yiman Chen, Ying Wang, Liansheng Huang, Feng Qu, Xiaobo Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen361005 China School of Computer and Information Engineering Fujian Provincial University Key Laboratory of Internet of Things Application Technology Xiamen University of Technology Xiamen361024 China Department of Computer Science School of Information Science and Engineering Xiamen University Xiamen361005 China Neusoft Medical System Shanghai200241 China

Parallel magnetic resonance imaging has served as an effective and widely adopted technique for accelerating scans. The advent of sparse sampling offers aggressive acceleration, allowing flexible sampling and better reconstruction. Nevertheless, faithfully reconstructing the image from limited data still poses a challenging task. Recent low-rank reconstruction methods exhibit superiority in providing a high-quality image. However, none of them employ the routinely acquired calibration data for improving image quality in parallel magnetic resonance imaging. In this work, an image reconstruction approach named STDLR-SPIRiT was proposed to explore the simultaneous two-directional low-rankness (STDLR) in the k-space data and to mine the data correlation from multiple receiver coils with the iterative self-consistent parallel imaging reconstruction (SPIRiT). The reconstruction problem was then solved with a singular value decomposition-free numerical algorithm. Experimental results of phantom and brain imaging data show that the proposed method outperforms the state-of-the-art methods in terms of suppressing artifacts and achieving the lowest error. Moreover, the proposed method exhibits robust reconstruction even when the auto-calibration signals are limited in parallel imaging. Overall the proposed method can be exploited to achieve better image quality for accelerated parallel magnetic resonance imaging. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Systematic literature review on corruption prevention efforts towards sustainable economy in Indonesia: ICT perspective 2

Systematic literature review on corruption prevention effort...

引用

2nd International Conference on Eco Engineering Development 2018, ICEED 2018

作者： Noerlina Kristin, D.M. Dewanti, R. Sasmoko Mursitama, T.N. Muqsith, A.M. Fajrianti, S.P. Krishti, N.S. Makalew, B.A. Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Primary Teacher Education Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia Research Interest Group in Educational Technology Bina Nusantara University Jakarta11480 Indonesia International Relations Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Psychology Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia Mobile Application and Technology Program School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Corruption is an extraordinary crime and must be eradicated not only as fast as possible, but also as efficient and as effective as possible as it hinders the process of achieving sustainable economy. In current modern society, information technology exists as a multidisciplinary tool to solve lingering problems that could not be solved by traditional efforts. This paper aims to provide knowledge regarding identified efforts to fight corruption specifically in Indonesia region. Systematic Literature Review (SLR) methodology is used as the methodology, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow is used as framework to identify relevant records. Scopus database is used as data source. A total of 10 records are labelled as relevant to this research, and the findings are synthesized. It was found from the records that the main areas of interest for ICT research for corruption prevention in Indonesia are (1) information transparency, (2) operational enhancement, (3) success evaluation, (4) security and fraud, and (4) decision support. The findings are expected to assist future research in term of providing valuable insights on corruption prevention using ICT in Indonesia. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Crime

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Separation of mitochondrial diseased cells based on organelle-level difference using a dep microfluidic system 23

Separation of mitochondrial diseased cells based on organell...

引用

23rd International Conference on Miniaturized Systems for Chemistry and Life sciences, MicroTAS 2019

作者： Chi, Pei-Yin Chuang, Ting-Wei Chu, Tzu-Tsai Kuo, Chia-Tzu Wu, Yu-Ting Farmehini, Vahid Shieh, Dar-Bin Tseng, Fan-Gang Wei, Yau-Huei Swami, Nathan Chou, Chia-Fu Nano Science and Technology Program TIGP Academia Sinica Taipei Taiwan Institute of Physics Academia Sinica Taipei Taiwan Department of Engineering and System Science National Tsing Hua University Hsinchu Taiwan Center for Mitochondrial Medicine Free Radical Research Changhua Christian Hospital Changhua Taiwan Department of Electrical and Computer Engineering University of Virginia Charlottesville United States Institute of Oral Medicine School of Medicine National Cheng-Kung University Tainan Taiwan

ISBN: (纸本)9781733419000

It is imperative to develop tools to characterize cells with the diseased and normal mitochondria to monitor the healthiness of cells and take preventive measures for potential mitochondrial disease development. Here, we adopt insulator dielectrophoresis (i-DEP), by utilizing the intrinsic dielectric response of a cell to an external AC electric field to differentiate cells, containing partially diseased and normal mitochondria. Taking advantage of dielectrophoresis, DEP, principle, we developed a quick diagnostic method to detect mitochondrial disease in living cells, which is not readily achievable by conventional cell cytometry. © 2019 CBMS-0001.

关键词： Electrophoresis

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A Touch of Indonesian Local Wisdom for the Concept of User Interface (UI) Design of Nusantara Traditional Wastra Digital Repository (NTWDR)

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Mita Purbasari Sasmoko Octaviana Sylvia Caroline Rombe Sonya R. Manalu Visual Communication Design Department Graphic Design and New Media Program BINUS Northumbria School of Design Bina Nusantara University Jakarta Indonesia Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia Primary Teacher Education Department Faculty of Humanities Bina Nusantara University Jakarta Indonesia Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

This paper aims to create a concept for the UI (User Interface) design for the NTWDR (Nusantara Traditional Wastra Digital Repository) with a touch of Indonesian local wisdom focusing on the implementation of Visual Communication Design and the implementation of eight golden rules of UI web-based and mobile applications to the NTWDR (Nusantara Traditional Wastra Digital Repository). The research is essential as kick-off research to the UX (User Experience) research that will be conducted after the UI (User Interface) is done. The research adapted the Design Thinking method of IDEO and Stanford ***: Empathize, Interpretation, Ideation, Experimentation, and Test. It was designed with a simple sitemap that easy to follow and directly to the core of data. The uses of Nusantara colors and the interweaving of the intangible culture values of each NTW (Nusantara Traditional Wastra) are visual concepts behind each page of the NTWDR UI (Nusantara Traditional Wastra Digital Repository User Interface) design. The Result is the enchanting visual design representing Nusantara's diversity that attracts to be experienced and surfed.

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Search for W′ bosons decaying to a top and a bottom quark in leptonic final states in proton-proton collisions at s = 13 TeV

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Journal of High Energy Physics 2024年第5期2024卷 46页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Van Mechelen, P. Bols, E.S. D’hondt, J. Dansana, S. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Makarenko, I. Müller, D. Sahasransu, A.R. Tavernier, S. Tytgat, M. Van Putte, S. Vannerom, D. Clerbaux, B. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Khalilzadeh, A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Hong, Y. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Van Den Bossche, N. Wezenbeek, L. Benecke, A. Bruno, G. Caputo, C. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. Tran, T.T. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. Menezes De Oliveira, T. Moraes, A. Rebello Teles, P. Soeiro, M. Aldá Júnior, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Fonseca De Souza, S. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Guo, Q. Javaid, T. Mittal, M. Yuan, L. Bauer, G. Hu, Z. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Monti, F. Sharma, R. Song, J.N. Tao, J. Wang, C. Wang, J. Wang, Z. Zhang, H. Agapitos, A. Ban, Y. Lev Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics

A search for W′ bosons decaying to a top and a bottom quark in final states including an electron or a muon is performed with the CMS detector at the LHC. The analyzed data correspond to an integrated luminosity of 138 fb⁻¹ of proton-proton collisions at a center-of-mass energy of 13 TeV. Good agreement with the standard model expectation is observed and no evidence for the existence of the W′ boson is found over the mass range examined. The largest observed deviation from the standard model expectation is found for a W′ boson mass (mW_′) hypothesis of 3.8 TeV with a relative decay width of 1%, with a local (global) significance of 2.6 (2.0) standard deviations. Upper limits on the production cross sections of W′ bosons decaying to a top and a bottom quark are set. Left- and right-handed W′ bosons with mW_′ below 3.9 and 4.3 TeV, respectively, are excluded at the 95% confidence level, under the assumption that the new particle has a narrow decay width. Limits are also set for relative decay widths up to 30%. © The Author(s) 2024.

关键词： Beyond Standard Model Hadron-Hadron Scattering Vector Boson Production

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