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Biomedical image analysis competitions: The state of current participation practice

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

Research of problems of computer networks expert systems

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IOP Conference Series: Materials Science and engineering 2021年第1期1047卷

作者： A L Zolkin A N Losev D V Gridina T G Aygumov Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics L.Tolstogo Street 23 Samara 443010 Russia Department of Applied Informatics Federal State Budgetary Educational Institution of Higher Education 'Russian Timiryazev State Agrarian University' Timiryazevskaya Street 49 Moscow 127550 Russia Department of legal and customs regulation of foreign trade Moscow Automobile and road construction State Technical University 64 Leningradsky Prospekt Moscow 125319 Russia Department of computer engineering software and automated systems Dagestan State Technical University I. Shamyl Street 70 Makhachkala 367000 Russia

The article is dedicated to the issues of development of expert systems in data-processing networks during web programming. The dependence of the processing, storage and transmission of information and web applications on the uniqueness and problematics of expert systems creating is observed. The process of development of a software control block of content (engine) as the own control system is considered in details; vulnerabilities and a set of necessary functions inherent in the specified engine are explored. Each new developed software engine can be exposed to unauthorized access and content theft, in this regard, sometimes it is worth considering the percentage of uniqueness of the development methodology and code. In this case, the control block (engine) must be written using its own unique variables and file names, which will help secure the site from third-party interference and will not require the purchase of more expensive hosting for its placement. In addition, the article investigates the problems associated with the threat posed by malicious programs in computer networks, such as direct DDOS attacks, identifies the features of such programs, and also provides ways to combat them.

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Ten quick tips for deep learning in biology

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

Incremental ELMVIS for unsupervised learning

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arXiv 2019年

作者： Akusok, Anton Eirola, Emil Miche, Yoan Oliver, Ian Björk, Kaj-Mikael Gritsenko, Andrey Baek, Stephen Lendasse, Amaury Arcada University of Applied Sciences Helsinki Finland Nokia Solutions and Networks Group Espoo Finland Department of Computer Science Aalto University Finland Risklab at Arcada University of Applied Sciences Finland Department of Mechanical and Industrial Engineering University of Iowa Iowa City United States Iowa Informatics Initiative University of Iowa Iowa City United States

An incremental version of the ELMVIS+ method is proposed in this paper. It iteratively selects a few best fitting data samples from a large pool, and adds them to the model. The method keeps high speed of ELMVIS+ while allowing for much larger possible sample pools due to lower memory requirements. The extension is useful for reaching a better local optimum with greedy optimization of ELMVIS, and the data structure can be specified in semi-supervised optimization. The major new application of incremental ELMVIS is not to visualization, but to a general dataset processing. The method is capable of learning dependencies from non-organized unsupervised data - either reconstructing a shuffled dataset, or learning dependencies in complex high-dimensional space. The results are interesting and promising, although there is space for improvements. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

Differential evolution and deterministic chaotic series: A detailed study

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Differential evolution and deterministic chaotic series: A d...

作者： Senkerik, Roman Viktorin, Adam Zelinka, Ivan Pluhacek, Michal Kadavy, Tomas Oplatkova, Zuzana Kominkova Bhateja, Vikrant Satapathy, Suresh Chandra Tomas Bata University in Zlin Faculty of Applied Informatics Department of Informatics and Artificial Intelligence Nam T.G. Masaryka 5555 Zlin760 01 Czech Republic VŠB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Computer Science 17. listopadu 15 Ostrava-Poruba708 33 Czech Republic Department of Electronics and Communication Engineering LucknowU.P India Kalinga Institute of Industrial Technology School of Computer Engineering BhubaneswarOdisha India

This research represents a detailed insight into the modern and popular hybridization of deterministic chaotic dynamics and evolutionary computation. It is aimed at the influence of chaotic sequences on the performance of four selected Differential Evolution (DE) variants. The variants of interest were: original DE/Rand/1/ and DE/Best/1/ mutation schemes, simple parameter adaptive jDE, and the recent state of the art version SHADE. Experiments are focused on the extensive investigation of the different randomization schemes for the selection of individuals in DE algorithm driven by the nine different two-dimensional discrete deterministic chaotic systems, as the chaotic pseudorandom number generators. The performances of DE variants and their chaotic/non-chaotic versions are recorded in the one-dimensional settings of 10D and 15 test functions from the CEC 2015 benchmark, further statistically analyzed. © 2018, Brno University of Technology. All rights reserved.

关键词： Chaotic systems

Causes and alternative treatments of neurological conditions due to environmental toxins: An overview

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AIP Conference Proceedings 2021年第1期2401卷

作者： Mansour Youseffi Muhammad Mahadi Bin Abdul Jamil Naseem Ghazali Luca Parisi Farideh Javid Mohd Helmy Abd Wahab Syed Zulkarnain Syed Idrus Nur Adilah Abd Rahman 1Biomedical and Electronics Engineering Department Faculty of Engineering and Informatics University of Bradford Bradford BD7 1DP United Kingdom 2Department of Electronic Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia (UTHM) Johore Malaysia 3Royal Blackburn Hospital Blackburn BB2 3HH United Kingdom 4Computer Science Department Faculty of Engineering and Informatics University of Bradford Bradford BD7 1DP United Kingdom 5School of Applied Sciences Pharmacy University of Huddersfield Queensgate Huddersfield HD1 3DH United Kingdom 6Faculty of Applied and Human Sciences Universiti Malaysia Perlis (UniMAP) Perlis Malaysia

It is fairly obvious that we live in a toxic world and our brains are poisoned continuously, and this constant low level of exposure over time is slowly damaging our neurons and brain cells. The worse news is that this harmful toxic environment is not going to stop anytime in the foreseeable future and environmental toxins are a leading cause of neurological illnesses. It is also well established that oxidative stress contributes to various diseases including diabetes, heart disease, inflammatory conditions, high blood pressure, and neurological diseases such as Alzheimer’s and Parkinson’s. It is therefore inevitable that exposure to such conditions including heavy metals, EMF radiation, bacteria and viruses will require a strong immune system to defeat such harmful toxins, and that almost 80 percent of the immune system is located in the gut. The main causes of neurological diseases are already known and the standard drug treatments (e.g., Aricept and Namenda for Alzheimer’s) slow the progression of such terrible diseases, but they do not address the root cause of memory loss. Glutathione is known as body’s master antioxidant being effective against oxidative stress and inflammation. Along with other natural antioxidants in the body, it works to defeat the harmful effects of free radicals which cause damage to cells and tissue leading to various health conditions. In this review, the main factors, including environmental toxins, causing neurological illnesses like Alzheimer’s and Parkinson’s, and natural ways of prevention and treatment protocols have been discussed. The proposed treatment protocol could be lifesaving and is supported by the extensively published literature.

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Use of the modern information technologies for continuous monitoring of transport infrastructure

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IOP Conference Series: Materials Science and engineering 2021年第1期1047卷

作者： A L Zolkin E A Domracheva A N Losev Yu M Avdeev Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics L.Tolstogo Street 23 Samara 443010 Russia Telecommunication systems and networks department State University of Telecommunications Solomenskaya Street 7 Kiev 03110 Ukraine Department of Applied Informatics Federal State Budgetary Educational Institution of Higher Education 'Russian Timiryazev State Agrarian University' Timiryazevskaya Street 49 Moscow 127550 Russia Department of Urban Cadastre and Geodesy of the Engineering and Construction Institute Vologda State University Lenin Street 15 Vologda 160000 Russia

The concepts of universality and general applicability of information networks as well as the issues of using networks in specific directions are considered in the article on the example of the convergence of transport infrastructure and information networks. An analysis of modern information networks has been carried out and the following three main directions have been identified: infrastructure as a service, platform as a service, software as a service. The article examines the issues of automation of the customer relationship management by introducing a CRM system in order to improve transport support and logistics services provided by companies at the competitive market of logistics services. The basic principles of convergence of information networks and transport infrastructure are presented on the example of a transport and logistics system. The issues of use of the modern information technologies for continuous monitoring of transport infrastructure are considered.

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BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023

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arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Haldar, Debanjan Jiang, Zhifan Zapaishchykova, Anna Pavaine, Julija Shah, Lubdha M. Jones, Blaise V. Sheth, Nakul Prabhu, Sanjay P. McAllister, Aaron S. Tu, Wenxin Nandolia, Khanak K. Rodriguez, Andres F. Shaikh, Ibraheem Salman Sanchez-Montano, Mariana Lai, Hollie Anne Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anderson, Hannah Anwar, Syed Muhammed Aristizabal, Alejandro Bagheri, Sina Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Eddy, James Ezhov, Ivan Familiar, Ariana M. Farahani, Keyvan Gandhi, Deep Gottipati, Anurag Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Elaine, Elaine Karargyris, Alexandros Kassem, Hasan Khalili, Neda Kofler, Florian LaBella, Dominic Van Leemput, Koen Li, Hongwei B. Maleki, Nazanin Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Pati, Sarthak Piraud, Marie Poussaint, Tina Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Sheller, MIcah Shinohara, Russell Takeshi Viswanathan, Karthik Wang, Chunhao Wiestler, Benedikt Wiggins, Walter F. Davatzikos, Christos Storm, Phillip B. Bornhorst, Miriam Packer, Roger Hummel, Trent de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Ware, Jeffrey B. Kann, Benjamin H. Rood, Brian Resnick, Adam Bakas, Spyridon Vossough, Arastoo Linguraru, Marius George The Children’s Hospital of Philadelphia PA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Neurosurgery Thomas Jefferson University Hospital PA United States Dana-Farber Brigham Cancer Center Boston Children’s Hospital BostonMA United States Department of Paediatric Radiology Royal Manchester Children’s Hospital Manchester University Hospitals NHS Foundation Trust University of Manchester Manchester United Kingdom University of Utah UT United States Cincinnati Children’s Hospital Medical Center OH United States Department of Radiology The Children’s Hospital of Philadelphia PA United States Division of Neuroradiology Department of Radiology Boston Children’s Hospital Harvard Medical School BostonMA United States Department of Radiology Nationwide Children’s Hospital OH United States College of Arts and Sciences University of Pennsylvania PA United States Department of Diagnostic and Interventional Radiology All India Institute of Medical Sciences Rishikesh India Department of Diagnostic & Interventional Imaging Neuroradiology Section The University of Texas Health HoustonTX United States Beth Israel Deaconess Medical Center Harvard Medical School MA United States Unidad de Patologia Clinica:Guadalajara Jalisco Mexico Department of Radiology Children’s Health Orange County CA United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States MLCommons United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC United States Mayo Clinic MN

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTS-PEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors. © 2024, CC BY.

关键词： Pediatrics

Search for high-mass Wγ and Zγ resonances using hadronic W/Z boson decays from 139 fb⁻¹ of pp collisions at √s = 13 TeV with the ATLAS detector

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Journal of High Energy Physics 2023年第7期2023卷 125页

作者： Aad, G. Abbott, B. Abbott, D.C. Abed Abud, A. Abeling, K. Abhayasinghe, D.K. Abidi, S.H. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Abusleme Hoffman, A.C. Acharya, B.S. Achkar, B. Adam, L. Adam Bourdarios, C. Adamczyk, L. Adamek, L. Addepalli, S.V. Adelman, J. Adiguzel, A. Adorni, S. Adye, T. Affolder, A.A. Afik, Y. Agapopoulou, C. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J.A. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ai, X. Aielli, G. Aizenberg, I. Akatsuka, S. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Al Khoury, K. Alberghi, G.L. Albert, J. Albicocco, P. Alconada Verzini, M.J. Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Alhroob, M. Ali, B. Ali, S. Aliev, M. Alimonti, G. Allaire, C. Allbrooke, B.M.M. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Alunno Camelia, E. Alvarez Estevez, M. Alviggi, M.G. Amaral Coutinho, Y. Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Amor Dos Santos, S.P. Amoroso, S. Amos, K.R. Amrouche, C.S. Ananiev, V. Anastopoulos, C. Andari, N. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antipov, E. Antonelli, M. Antrim, D.J.A. Anulli, F. Aoki, M. Aparisi Pozo, J.A. Aparo, M.A. Aperio Bella, L. Aranzabal, N. Araujo Ferraz, V. Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Armstrong, A. Arnaez, O. Arnold, H. Arrubarrena Tame, Z.P. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Asimakopoulou, E.M. Asquith, L. Assahsah, J. Assamagan, K. Astalos, R. Atkin, R.J. Atkinson, M. Atlay, N.B. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Austrup, V.A. Avner, G. Avolio, G. Ayoub, M.K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Bagnaia, P. Bahrasemani, H. Bailey, A.J. Bailey, V.R. Baines, J.T. Bakalis, C. Baker, O.K. Bakker, P.J. Bakos, E. Bakshi Gupta, D. Balaji, S. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. Balli, F Department of Physics University of Adelaide Adelaide Australia Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Université Savoie Mont Blanc CNRS/IN2P3 Annecy France High Energy Physics Division Argonne National Laboratory Argonne IL United States Department of Physics University of Arizona Tucson AZ United States Department of Physics University of Texas at Arlington Arlington TX United States Physics Department National and Kapodistrian University of Athens Athens Greece Physics Department National Technical University of Athens Zografou Greece Department of Physics University of Texas at Austin Austin TX United States Bahcesehir University Faculty of Engineering and Natural Sciences Istanbul Turkey Istanbul Bilgi University Faculty of Engineering and Natural Sciences Istanbul Turkey Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziantep University Gaziantep Turkey Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain Institute of High Energy Physics Chinese Academy of Sciences Beijing China Physics Department Tsinghua University Beijing China Department of Physics Nanjing University Nanjing China University of Chinese Academy of Science (UCAS) Beijing China Institute of Physics University of Belgrade Belgrade Serbia Department for Physics and Technology University of Bergen Bergen Norway Physics Division Lawrence Berkeley National Laboratory Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern Bern Switzerland School of Physics and Astronomy University of Birmingham Birmingham United Kingdom Facultad de Ciencias y Centro de Investigaciónes

A search for high-mass charged and neutral bosons decaying to Wγ and Zγ final states is presented in this paper. The analysis uses a data sample of s = 13 TeV proton-proton collisions with an integrated luminosity of 139 fb⁻¹ collected by the ATLAS detector during LHC Run 2 operation. The sensitivity of the search is determined using models of the production and decay of spin-1 charged bosons and spin-0/2 neutral bosons. The range of resonance masses explored extends from 1.0 TeV to 6.8 TeV. At these high resonance masses, it is beneficial to target the hadronic decays of the W and Z bosons because of their large branching fractions. The decay products of the high-momentum W/Z bosons are strongly collimated and boosted-boson tagging techniques are employed to improve the sensitivity. No evidence of a signal above the Standard Model backgrounds is observed, and upper limits on the production cross-sections of these bosons times their branching fractions to Wγ and Zγ are derived for various boson production models. [Figure not available: see fulltext.]. © 2023, The Author(s).

关键词： Hadron-Hadron Scattering