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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Student recognition of interference and diffraction patterns: An eye-tracking study

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Physical Review Physics Education Research 2020年第2期16卷 020133-020133页

作者： Ana Susac Maja Planinic Andreja Bubic Lana Ivanjek Marijan Palmovic Department of Applied Physics Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 10000 Zagreb Croatia Department of Physics Faculty of Science University of Zagreb Bijenicka 32 10000 Zagreb Croatia Chair for Psychology Faculty of Humanities and Social Sciences University of Split Sinjska 2 21000 Split Croatia Faculty of Physics University of Vienna Porzellangasse 4 1090 Vienna Austria Laboratory for Psycholinguistic Research Department of Speech and Language Pathology University of Zagreb Borongajska cesta 83h 10000 Zagreb Croatia

Previous studies have demonstrated that students have difficulties in applying the wave model of light to explain single-slit diffraction and double-slit interference patterns. In this study, we investigated if students could recognize typical interference and diffraction patterns at all. Eye movements of high-school students were measured while they were identifying patterns produced by monochromatic light on a double slit, single slit, and diffraction grating, and by white light on a diffraction grating. Most students had difficulties with recognizing double-slit interference pattern and diffraction grating pattern of monochromatic light. Identification of the single-slit diffraction pattern was easier probably due to its distinguishable central maximum. The easiest task for students was recognizing the diffraction pattern of white light on an optical grating. Eye-tracking data suggested that even students who incorrectly answered this question were aware that the diffraction grating separates white light into colors. Additionally, eye tracking revealed that students who identified patterns correctly attended more the correct pattern than other options, thus corroborating previous findings. Overall, the results indicate that the recognition of interference and diffraction patterns is quite demanding for students, suggesting that more attention should be paid to observing and understanding basic wave optics phenomena.

关键词： Concepts & principles Scientific reasoning & problem solving

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RETRACTED ARTICLE: Delay sensitive aware distributed data fault recognition algorithm for distributed sensor networks

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Peer-to-Peer Networking and Applications 2019年第4期13卷 1080-1090页

作者： Kirubakaran, J. Prasanna Venkatesan, G. K. D. Kumar, K. Sampath Dhanabal, S. Baskar, K. Department of Electronics and Communication Engineering Muthayammal Engineering College Namakkal (Dt) India Department of Electronics and Communication Faculty of Engineering Karpagam Academy of Higher Education Coimbatore India School of Computing Science and Engineering Galgotias University New Delhi India Department of Computer Science and Engineering PGP College of Engineering and Technology Namakkal India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology Trichy India

In the recent past, DistributedSensor Network (DSN) placed a significant role in diverse application that benefits to transmit the information from one location to another location. During the data transmission procedure, the network exploits numerous characteristics such as energy harvesting, resilience, scalability, Delay, Life time, Throughput and Packet Delivery Ratio (PDR). The effective utilization characteristics benefits to improve the complete information transmission progression. Even though the network transmits the information effectually, the network faces numerous disputes such as network failure, node failure, lifetime issues, Throughput, delay, PDR and power consumption problem. These issues are affecting the entire data or information transmission efficiency. In addition to this, selection of path also generate problems that leads to create the security issues because the wrong path selection affects network lifetime as well chances to access the information by unauthorized access. To address these issues Delay Sensitive Aware Distributed Data fault Recognition algorithm (DSA-DDFRA) is proposed with the major objective is to manage the network lifetime, faults and choosing optimized path while making the information transaction. The algorithm is experimentally validated for optimized parameter such as Error Rate, network lifetime, delay, energy, throughput and PDR.

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Short-term and long-term context aggregation network for video inpainting

arXiv

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arXiv 2020年

作者： Li, Ang Zhao, Shanshan Ma, Xingjun Gong, Mingming Qi, Jianzhong Zhang, Rui Tao, Dacheng Kotagiri, Ramamohanarao School of Computing and Information Systems University of Melbourne Australia UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering University of Sydney DarlingtonNSW2008 Australia School of Information Technology Deakin University Geelong Australia School of Mathematics and Statistics University of Melbourne Australia

Video inpainting aims to restore missing regions of a video and has many applications such as video editing and object removal. However, existing methods either suffer from inaccurate short-term context aggregation or rarely explore long-term frame information. In this work, we present a novel context aggregation network to effectively exploit both short-term and long-term frame information for video inpainting. In the encoding stage, we propose boundary-aware short-term context aggregation, which aligns and aggregates, from neighbor frames, local regions that are closely related to the boundary context of missing regions into the target frame5. Furthermore, we propose dynamic long-term context aggregation to globally refine the feature map generated in the encoding stage using long-term frame features, which are dynamically updated throughout the inpainting process. Experiments show that it outperforms state-of-the-art methods with better inpainting results and fast inpainting speed. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal encoding

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A new 3-D chaotic system with four quadratic nonlinear terms, its global chaos control via passive control method and circuit design 7

A new 3-D chaotic system with four quadratic nonlinear terms...

引用

7th International Conference on Global Optimization and Its Application 2018, ICoGOIA 2018

作者： Lien, C.H. Vaidyanathan, S. Sambas, A. Sampath, S. Sukono Mamat, M. Department of Marine Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Research and Development Centre Vel Tech University Chennai Tamil Nadu India Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Indonesia Department of Electronics and Communication Engineering Vel Tech University Chennai Tamil Nadu India Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Indonesia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Kuala Terengganu Malaysia

This paper reports the finding of a new three-dimensional chaotic system with four quadratic nonlinear terms. The paper starts with a detailed dynamic analysis of the properties of the system such as phase plots, Lyapunov exponents, Kaplan-Yorke dimension and equilibrium points. Our new chaotic system is obtained by modifying the dynamics of the Zhu chaotic system (2010), and it has complex chaotic properties. As an engineering application, passive control method is applied for the global chaos control of the new chaotic system. Finally, an electronic circuit implementation of the new chaotic system is designed and implemented in MultiSIM. A good qualitative agreement has been shown between the MATLAB simulations of the new chaotic system and the MultiSIM results. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Chaotic systems

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Smart IoT-based Waste Monitoring System

Smart IoT-based Waste Monitoring System

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International UNIMAS STEM engineering Conference (EnCon)

作者： Alfred Jia Yee Tan Hui En Lee Sing Muk Ng Hong Siang Chua Faculty of Engineering Computing and Science Swinburne University of Technology Kuching Malaysia Research & Development Department Sarawak Energy Berhad Kuching Malaysia

ISBN: (纸本)9781728134956

Illegal waste dumping activities are becoming more compulsive and are in fact, one of the most daunting worldwide issue today. The impacts of this activity are devastating to the environmental and human health. To prevent illegal waste dumping, sufficient waste treatment facilities, for instance roll-on/roll-off (RoRo) waste containers must be placed strategically for community use. However, the result would not be significant without an efficient waste monitoring system. Hence, an integration of IoT-based sensors and mobile apps are proposed with the objectives of providing real-time information of waste fill level, RoRo waste container geolocation and waste decomposition level to users by uploading the sensor data wirelessly to a globally accessible domain. An Arduino UNO is chosen as the microcontroller for the application. Multiple HC-SR04 ultrasonic sensors are used to measure the waste pile level at different point in the RoRo waste container, hence the fill level; NEO-6M GPS module is used to measure GPS location; MQ-3 ethanol gas sensor is used to measure ethanol gas concentration, hence the decomposition level; ESP8266 ESP-01 Wi-Fi module is used for wireless transmission of sensor data to a website. Windows 10 and Android applications are developed successfully as a platform to display the real-time information of the RoRo bins. To sum up, this research is a good approach against the illegal dumping issue as it provides the users an alternative way to dispose bulky wastes legally and conveniently.

关键词： Sensors Monitoring Containers Geology Wireless fidelity Global navigation satellite system Image sensors

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Post-lockdown abatement of COVID-19 by fast periodic switching

arXiv

引用

arXiv 2020年

作者： Bin, M. Cheung, P. Crisostomi, E. Ferraro, P. Lhachemi, H. Murray-Smith, R. Myant, C. Parisini, Thomas Shorten, R. Stein, S. Stone, L. Department of Electrical and Electronic Engineering Imperial College London United Kingdom Dyson School of Design Engineering Imperial College London United Kingdom School of Electrical & Electronic Engineering University College Dublin Ireland School of Computing Science University of Glasgow Department of Engineering and Architecture University of Trieste Italy Department of Electrical and Electronic Engineering University College Dublin George S. Wise Faculty of Life Sciences Tel Aviv University

COVID-19 abatement strategies have risks and uncertainties which could lead to repeating waves of infection. We show - as proof of concept grounded on rigorous mathematical evidence - that periodic, high-frequency alternation of into, and out-of, lockdown effectively mitigates second-wave effects, while allowing continued, albeit reduced, economic activity. Periodicity confers (i) predictability, which is essential for economic sustainability, and (ii) robustness, since lockdown periods are not activated by uncertain measurements over short time scales. In turn - while not eliminating the virus - this fast switching policy is sustainable over time, and it mitigates the infection until a vaccine or treatment becomes available, while alleviating the social costs associated with long lockdowns. Typically, the policy might be in the form of 1-day of work followed by 6-days of lockdown every week (or perhaps 2 days working, 5 days off) and it can be modified at a slow-rate based on measurements filtered over longer time scales. Our results highlight the potential efficacy of high frequency switching interventions in post lockdown mitigation. All code is available on Github1 A software tool has also been developed so that interested parties can explore the proof-of-concept system. Copyright © 2020, The Authors. All rights reserved.

关键词： Viruses

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Ultrahigh-energy γ-ray emission associated with the tail of a bow-shock pulsar wind nebula

arXiv

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arXiv 2025年

作者： Cao, Zhen Aharonian, F. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cai, C.M. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.B. Chen, X.J. Chen, Y. Cheng, N. Cheng, Y.D. Chu, M.C. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Diao, Y.X. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Ge, T.T. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hannuksela, O.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, X.Y. He, Y. Hernández-Cadena, S. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, S.C. Huang, C. Huang, D.H. Huang, J.J. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Y.Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, H.B. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kaci, S. Kang, M.M. Karpikov, I. Khangulyan, D. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, L. Li, R.L. Li, S.D. Li, T.Y. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.R. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, X. Liu, Y. Liu, Y. Liu, Y.N. Lou, Y.Q. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mou, G.B. Mu, H.J. Nan, Y.C. Neronov, A. Ng, K.C.Y. Ni, M.Y. Nie, L. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China University of Science and Technology of China Anhui Hefei230026 China Yerevan State University 1 Alek Manukyan Street Yerevan0025 Armenia Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Institute for Nuclear Research of Russian Academy of Sciences Moscow117312 Russia School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Key Laboratory of Cosmic Rays [Tibet University Ministry of Education Tibet Lhasa850000 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China China School of Physics Guangzhou China Sun Yat-sen University Zhuhai519000 China Guangdo

In this study, We present a comprehensive analysis of an unidentified point-like ultra-high-energy (UHE) gamma-ray source, designated as 1LHAASO J1740+0948u, situated in the vicinity of the middle-aged pulsar PSR J1740+1000. The detection significance reaches 17.1σ (9.4σ) above 25 TeV (100 TeV).The energy spectrum of the source extends up to 300 TeV and is well-described by a log-parabola function with N0 = (1.93 ±0.23) ×10−16 TeV−1 cm−2 s−1, α = 2.14±0.27, and β = 1.20±0.41 at E0 = 30 TeV. The associated pulsar, PSR J1740+1000, resides at a high Galactic latitude and powers a bow-shock pulsar wind nebula (BSPWN) with an extended X-ray tail. The best-fit position of the gamma-ray source appears to be shifted by 0.2◦ with respect to the pulsar position. As the (i) currently identified pulsar halos do not demonstrate such offsets, and (ii) centroid of the gamma-ray emission is approximately located at the extension of the X-ray tail, we speculate that the UHE γ-ray emission may originate from re-accelerated electron/positron pairs that are advected away in the bow-shock tail. © 2025, CC BY-NC-ND.

关键词： Pulsars

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RETRACTED ARTICLE: Prewitt Logistic Deep Recurrent Neural Learning for Face Log Detection by Extracting Features from Images

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Arabian Journal for science and engineering 2021年第2期48卷 2589-2589页

作者： Krishnan Nair, Sreekumar Chinnappan, Sathiya Kumar Dubey, Anil Kumar Subburaj, Arjun Subramaniam, Shanthi Balasubramaniam, Vivekanandam Sengan, Sudhakar Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computational Intelligence School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Computer Science and Engineering ABES Engineering College Ghaziabad Uttar Pradesh India Tranxit Technology Solutions Private Limited Chennai India Department of Computer Science and Engineering Kongu Engineering College Perundurai India Faculty of Computer Science and Multimedia Lincoln University College Kota Bharu Malaysia Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

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Broadband γ-ray spectrum of supernova remnant Cassiopeia A

arXiv

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arXiv 2025年

作者： Cao, Zhen Aharonian, F. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cai, C.M. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.B. Chen, X.J. Chen, Y. Cheng, N. Cheng, Y.D. Chu, M.C. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Diao, Y.X. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Ge, T.T. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hannuksela, O.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, X.Y. He, Y. Hernández-Cadena, S. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, S.C. Huang, C. Huang, D.H. Huang, J.J. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Y.Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, H.B. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kaci, S. Kang, M.M. Karpikov, I. Khangulyan, D. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, L. Li, R.L. Li, S.D. Li, T.Y. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.R. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, X. Liu, Y. Liu, Y. Liu, Y.N. Lou, Y.Q. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mou, G.B. Mu, H.J. Nan, Y.C. Neronov, A. Ng, K.C.Y. Ni, M.Y. Nie, L. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Qi, M.Y. Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China University of Science and Technology of China Anhui Hefei230026 China Yerevan State University 1 Alek Manukyan Street Yerevan0025 Armenia Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Institute for Nuclear Research of Russian Academy of Sciences Moscow117312 Russia School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Key Laboratory of Cosmic Rays [Tibet University Ministry of Education Tibet Lhasa850000 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University 519000 Zhuhai Guangdong Guangzhou510275 China Institute of Frontier

The core-collapse supernova remnant (SNR) Cassiopeia A (Cas A) is one of the brightest galactic radio sources with an angular radius of ∼ 2.5′. Although no extension of this source has been detected in the γ-ray band, using more than 1000 days of LHAASO data above ∼ 0.8 TeV, we find that its spectrum is significantly softer than those obtained with Imaging Air Cherenkov Telescopes (IACTs) and its flux near ∼ 1 TeV is about two times higher. In combination with analyses of more than 16 years of Fermi-LAT data covering 0.1 GeV − 1 TeV, we find that the spectrum above 30 GeV deviates significantly from a single power-law, and is best described by a smoothly broken power-law with a spectral index of 1.90 ± 0.15stat (3.41 ± 0.19stat) below (above) a break energy of 0.63 ± 0.21stat TeV. Given differences in the angular resolution of LHAASO-WCDA and IACTs, TeV γray emission detected with LHAASO may have a significant contribution from regions surrounding the SNR illuminated by particles accelerated earlier, which, however, are treated as background by IACTs. Detailed modelling can be used to constrain acceleration processes of TeV particles in the early stage of SNR evolution. Copyright © 2025, The Authors. All rights reserved.

关键词： Cosmic rays

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