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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Confidence intervals uncovered: Are we ready for real-world medical imaging AI?

arXiv

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arXiv 2024年

作者： Christodoulou, Evangelia Reinke, Annika Houhou, Rola Kalinowski, Piotr Erkan, Selen Sudre, Carole H. Burgos, Ninon Boutaj, Sofiène Loizillon, Sophie Solal, Maëlys Rieke, Nicola Cheplygina, Veronika Antonelli, Michela Mayer, Leon D. Tizabi, Minu D. Jorge Cardoso, M. Simpson, Amber Jäger, Paul F. Kopp-Schneider, Annette Varoquaux, Gaël Colliot, Olivier Maier-Hein, Lena Heidelberg Div. Intelligent Medical Systems Germany AI Health Innovation Cluster Germany NCT Heidelberg a partnership between DKFZ Heidelberg University Hospital Germany DKFZ Heidelberg Helmholtz Imaging Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany DKFZ Heidelberg Interactive Machine Learning Group Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London United Kingdom Sorbonne Université Institut du Cerveau - Paris Brain Institute - ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié-Salpêtrière France NVIDIA Germany Department of Computer Science IT University of Copenhagen Denmark Centre for Medical Image Computing University College London United Kingdom School of Computing Queen’s University Canada Department of Biomedical and Molecular Sciences Queen’s University Canada Division of Biostatistics DKFZ Germany Parietal project team INRIA Saclay-Île de France France Faculty of Mathematics and Computer Science Heidelberg University Germany Medical Faculty Heidelberg University Germany

Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently, broad conclusions are simply derived from mean performance values. In this paper, we argue that this common practice is often a misleading simplification as it ignores performance variability. Our contribution is threefold. (1) Analyzing all MICCAI segmentation papers (n = 221) published in 2023, we first observe that more than 50% of papers do not assess performance variability at all. Moreover, only one (0.5%) paper reported confidence intervals (CIs) for model performance. (2) To address the reporting bottleneck, we show that the unreported standard deviation (SD) in segmentation papers can be approximated by a second-order polynomial function of the mean Dice similarity coefficient (DSC). Based on external validation data from 56 previous MICCAI challenges, we demonstrate that this approximation can accurately reconstruct the CI of a method using information provided in publications. (3) Finally, we reconstructed 95% CIs around the mean DSC of MICCAI 2023 segmentation papers. The median CI width was 0.03 which is three times larger than the median performance gap between the first and second ranked method. For more than 60% of papers, the mean performance of the second-ranked method was within the CI of the first-ranked method. We conclude that current publications typically do not provide sufficient evidence to support which models could potentially be translated into clinical practice. © 2024, CC BY.

关键词： Medical imaging

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Traffic Optimization Algorithms in Optical Networks For Real Time Traffic Analysis

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Optik 2022年

作者： Suresh Kumar. K Swati Vijay Shinde S. Venkata Lakshmi R.S. Sabeenian Koppisetti Durga Bhavani K.V.S.R. Murthy Department of Information Technology Saveetha Engineering College Tamil Nadu India Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune India Dept. of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India Department of ECE Sona College of Technology Salem Department of Management Studies Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India EEE Department & Dean (R&D) Aditya Group of Engineering Colleges

The rapid growth of metropolitan areas poses significant traffic control issues. Advanced telecommunication structures, such as the IoT (Internet of Things) technology, including wireless connections, produce enormous amounts of diverse traffic information. Conventional networking control approaches for tracking and information analysis confront certain obstacles and problems in specific systems, like reliability and efficient actual-time analysis of massive datasets. Furthermore, due to variables like gadget portability and connection diversity, the overall structure of internet activity, particularly in wireless networks, exhibits extremely complicated activity. The article aims to create a method for collecting high-quality, comprehensive information like transportation flowing strength, travel routes, and mean vehicle velocity. Simultaneously, information is gathered throughout the whole operational zone of junctions and neighboring highway segments under the city video security sensor perspective. More profound learning methods will be used within the communication domain for Networking Transport Management and Analytics (NTMA) activities such as traffic categorization and forecasting. The YOLOv3 artificial routing topology, as well as a SORT-accessible monitor, are used in our approach. Utilizing extended masking branches and improving its geometry of anchoring, overall essential efficiency in YOLO has been enhanced. We employed a technique of viewpoint conversion using parameters from actual pictures to global dimensions for computing overall vehicular velocities.

关键词： Networking Traffic Management and Analytics (NTMA) real-time surveillance traffic management and database processing

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Systems approach to concentrated solar power (CSP) technology adoption in South Africa

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AIP Conference Proceedings 2018年第1期2033卷

作者： Toyosi Craig Alan Brent Frank Duvenhage Frank Dinter 1Department of Industrial Engineering Stellenbosch University Private Bag XI Matieland 7602 South Africa 2Centre for Renewable Energy and Sustainable Energy Knowledge Centre Stellenbosch University South Africa 4Department of Engineering and Computer Science Victoria University of Wellington New Zealand 3Solar Thermal Energy Research Group Stellenbosch University South Africa

To meet its future energy demand, the government of South Africa set up an Integrated Resource Plan (IRP) with the primary objective of determining its long-term electricity demand, and indicating how this demand should be met, in terms of generation type and timing. The IRP in 2010 presented the favoured energy generation techniques in SA, and it allocated 17 800 MW (42%) of the total energy mix in 2030 to renewable energy technologies (RETs). To achieve this target, the Renewable Energy Independent Power Producer Procurement Programme (REI4P) was launched and wind, solar photovoltaic (PV) and concentrating solar power (CSP) have been favoured mostly in the bids rolled out thus far. South Africa has been identified as one of the world’s best destinations for CSP because of the available solar resources, and a total of 600 MW of CSP had been purchased in the REIPPPP bids. However, the IRP update of 2016 threw CSP out of the future energy plans, when it gave no share to new electricity generation from it beyond 2030. This has created growing uncertainty of the future of CSP in South Africa. There is therefore an urgent need for collective efforts to present a broad and detailed value proposition in terms of present and prospects of CSP, and how it can be developed and deployed in the country to foster a lower tariff, encourage adoption, and ensure its return to the IRP. The study presented in this paper used a system dynamics approach to analyse the unique, critical and complex factors that affect the deployment of CSP in South Africa, as identified by concerned policy-makers, CSP experts, and existing studies. The result from this study shows that improved support for research is the most effective way to open new methods and ways in which the CSP technologies can be deployed, which will foster further CSP adoption in South Africa.

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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Applying the FAIR Principles to computational workflows

arXiv

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arXiv 2024年

作者： Wilkinson, Sean R. Aloqalaa, Meznah Belhajjame, Khalid Crusoe, Michael R. de Paula Kinoshita, Bruno Gadelha, Luiz Garijo, Daniel Ragnar Gustafsson, Ove Johan Juty, Nick Kanwal, Sehrish Khan, Farah Zaib Köster, Johannes Gehlen, Karsten Petersvon Pouchard, Line Rannow, Randy K. Soiland-Reyes, Stian Soranzo, Nicola Sufi, Shoaib Sun, Ziheng Vilne, Baiba Wouters, Merridee A. Yuen, Denis Goble, Carole Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory Oak RidgeTN United States Department of Computer Science University of Manchester Manchester United Kingdom LAMSADE PSL Paris Dauphine University Paris France Berlin Germany Earth Sciences Barcelona Supercomputing Center Barcelona Spain Heidelberg Germany Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Australian BioCommons University of Melbourne MelbourneVIC Australia ParkvilleVIC Australia University Medicine Essen University of Duisburg-Essen Essen Germany Data Management Department Deutsches Klimarechenzentrum GmbH Hamburg Germany Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States Silverdraft Supercomputing BoiseID United States Informatics Institute University of Amsterdam Amsterdam Netherlands Earlham Institute Norwich United Kingdom Center for Spatial Information Science and Systems Department of Geography and Geoinformation Science George Mason University FairfaxVA United States Bioinformatics Group Riga Stradins University Riga Latvia School of Clinical Medicine University of New South Wales KensingtonNSW Australia Ontario Institute for Cancer Research TorontoON Canada

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative’s FAIR Workflows Working group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows. We present recommendations with commentary that reflects our discussions and justifies our choices and adaptations. These are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guidelines for adoption and fodder for discussion. The FAIR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community. © 2024, CC BY.

关键词： Application programs

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Generating Approximate Ground States of Molecules Using Quantum Machine Learning

arXiv

引用

arXiv 2022年

作者： Ceroni, Jack Stetina, Torin F. Kieferová, Mária Marrero, Carlos Ortiz Arrazola, Juan Miguel Wiebe, Nathan Xanadu TorontoONM5G 2C8 Canada Department of Mathematics University of Toronto TorontoONM5S 3E1 Canada Simons Institute for the Theory of Computing BerkeleyCA94704 United States Berkeley Quantum Information and Computation Center University of California BerkeleyCA94720 United States Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology SydneyNSW2007 Australia AI & Data Analytics Division Pacific Northwest National Laboratory RichlandWA99354 United States Department of Electrical & Computer Engineering North Carolina State University RaleighNC27607 United States Department of Computer Science University of Toronto ONM5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory RichlandWA99354 United States

The potential energy surface (PES) of molecules with respect to their nuclear positions is a primary tool in understanding chemical reactions from first principles. However, obtaining this information is complicated by the fact that sampling a large number of ground states over a high-dimensional PES can require a vast number of state preparations. In this work, we propose using a generative quantum machine learning model to prepare quantum states at arbitrary points on the PES. The model is trained using quantum data consisting of ground-state wavefunctions associated with different classical nuclear coordinates. Our approach uses a classical neural network to convert the nuclear coordinates of a molecule into quantum parameters of a variational quantum circuit. The model is trained using a fidelity loss function to optimize the neural network parameters. We show that gradient evaluation is efficient and numerically demonstrate our method’s ability to prepare wavefunctions on the PES of hydrogen chains, water, and beryllium hydride. In all cases, we find that a small number of training points are needed to achieve very high overlap with the groundstates in practice. From a theoretical perspective, we further prove limitations on these protocols by showing that if we were able to learn across an avoided crossing using a small number of samples, then we would be able to violate Grover’s lower bound. Additionally, we prove lower bounds on the amount of quantum data needed to learn a locally optimal neural network function using arguments from quantum Fisher information. This work further identifies that quantum chemistry can be an important use case for quantum machine learning. © 2022, CC BY.

关键词： Molecules

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Chemically inspired Erdős-Rényi oriented hypergraphs

arXiv

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arXiv 2023年

作者： Garcia-Chung, Angel Bermúdez-Montaña, Marisol Stadler, Peter F. Jost, Jürgen Restrepo, Guillermo Tecnológico de Monterrey Escuela de Ingeniería y Ciencias Estado de México52926 Mexico Max Planck Institute for Mathematics in the Sciences Leipzig Germany Interdisciplinary Center for Bioinformatics Universität Leipzig Leipzig Germany Bioinformatics Group Department of Computer Science Universität Leipzig Leipzig Germany Institute for Theoretical Chemistry University of Vienna Vienna Austria The Santa Fe Institute Santa FeNM United States Facultad de Ciencias Universidad Nacional de Colombia Bogotá Colombia Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Leipzig Germany School of Applied Sciences and Engineering EAFIT University Medellín Colombia

High-order structures have been recognised as suitable models for systems going beyond the binary relationships for which graph models are appropriate. Despite their importance and surge in research on these structures, their random cases have been only recently become subjects of interest. One of these high-order structures is the oriented hypergraph, which relates couples of subsets of an arbitrary number of vertices. Here we develop the Erdős-Rényi model for oriented hypergraphs, which corresponds to the random realisation of oriented hyperedges of the complete oriented hypergraph. A particular feature of random oriented hypergraphs is that the ratio between their expected number of oriented hyperedges and their expected degree or size is 3/2 for large number of vertices. We highlight the suitability of oriented hypergraphs for modelling large collections of chemical reactions and the importance of random oriented hypergraphs to analyse the unfolding of chemistry. © 2023, CC BY-SA.

关键词： Graph theory

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Galactic transient sources with the Cherenkov Telescope Array Observatory

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Monthly Notices of the Royal Astronomical Society 2025年第1期540卷 205–238页

作者： Abe, K Abe, S Abhir, J Abhishek, A Acero, F Acharyya, A Adam, R Aguasca-Cabot, A Agudo, I Aguirre-Santaella, A Alfaro, J Alfaro, R Alvarez-Crespo, N Alves Batista, R Amans, J-P Amato, E Ambrosi, G Ambrosino, F Angüner, E O Antonelli, L A Aramo, C Arcaro, C Arnesen, T T H Asano, K Ascasibar, Y Aschersleben, J Ashkar, H Augusto Stuani, L Baack, D Backes, M Balazs, C Balbo, M Baquero Larriva, A Barbosa Martins, V Barres de Almeida, U Barrio, J A Barrios-Jiménez, L Batković, I Batzofin, R Baxter, J Becerra González, J Becker Tjus, J Belmont, R Benbow, W Bernete, J Bernlöhr, K Berti, A Bertucci, B Beshley, V Bhattacharjee, P Bhattacharyya, S Bigongiari, C Bissaldi, E Blanch, O Blazek, J Bocchino, F Boisson, C Bolmont, J Bonnoli, G Bonollo, A Bordas, P Bosnjak, Z Bottacini, E Böttcher, M Bradascio, F Bronzini, E Brown, A M Brunelli, G Bulgarelli, A Burelli, I Burger-Scheidlin, C Burmistrov, L Burton, M Buscemi, M Cailleux, J Campoy-Ordaz, A Cantlay, B K Capasso, G Caproni, A Capuzzo-Dolcetta, R Caraveo, P Caroff, S Carosi, A Carosi, R Carquin, E Carrasco, M-S Cascone, E Cassol, F Castaldini, L Castrejon, N Castro-Tirado, A J Cerasole, D Ceribella, G Cerruti, M Chadwick, P M Chaty, S Chen, A W Chernyakova, M Chiavassa, A Chudoba, J Chytka, L Cicciari, G M Cifuentes, A Coimbra Araujo, C H Colapietro, M Conforti, V Contreras, J L Cortina, J Costa, A Costantini, H Cotter, G Cristofari, P Cuevas, O Curtis-Ginsberg, Z D’Aì, A D’Amico, G D’Ammando, F Dai, S Dazzi, F de Bony de Lavergne, M De Caprio, V De Cesare, G De Frondat Laadim, F de Gouveia Dal Pino, E M De Lotto, B De Lucia, M de Martino, D de Menezes, R de Naurois, M de Ona Wilhelmi, E de Souza, V del Peral, L del Valle, M V Delgado, C Dell’aiera, M Della Valle, M della Volpe, D Depaoli, D Devin, J Di Girolamo, T Di Piano, A Di Pierro, F Di Tria, R Di Venere, L Diebold, S Dignam, C Dima, R Dinesh, A Djuvsland, J Dominik, R M Dominis Prester, D Donini, A Dorner, D Dörner, J Doro, M Dubos, C Ducci, L Dwarkadas, V V Ebr, J Eckner, C Egberts, K Einecke, S Elsässer, D Emery Department of Physics Tokai University 4-1-1 Kita-Kaname Hiratsuka Kanagawa 259-1292 Japan Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Kashiwa Chiba 277-8582 Japan ETH Zürich Institute for Particle Physics and Astrophysics Otto-Stern-Weg 5 CH-8093 Zürich Switzerland INFN and Università degli Studi di Siena Dipartimento di Scienze Fisiche della Terra e dell’Ambiente (DSFTA) Sezione di Fisica Via Roma 56 I-53100 Siena Italy Université Paris-Saclay Université Paris Cité CEA CNRS AIM F-91191 Gif-sur-Yvette Cedex France FSLAC IRL 2009 CNRS/IAC E-38205 La Laguna Tenerife Spain University of Alabama Tuscaloosa Department of Physics and Astronomy Gallalee Hall Box 870324 Tuscaloosa AL 35487-0324 USA Université Côte d’Azur Observatoire de la Côte d’Azur CNRS Laboratoire Lagrange UF 06304 Nice France Laboratoire Leprince-Ringuet CNRS/IN2P3 École Polytechnique Institut Polytechnique de Paris F-91120 Palaiseau France Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Martí i Franquès 1 E-08028 Barcelona Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n E-18008 Granada Spain Institute for Computational Cosmology and Department of Physics Durham University South Road Durham DH1 3LE UK Pontificia Universidad Católica de Chile Av. Libertador Bernardo O’Higgins 340 8331150 Santiago Chile Universidad Nacional Autónoma de México Delegación Coyoacán 04510 Ciudad de México Mexico IPARCOS-UCM Instituto de Física de Partículas y del Cosmos and EMFTEL Department Universidad Complutense de Madrid E-28040 Madrid Spain Instituto de Física Teórica UAM/CSIC and Departamento de Física Teórica Universidad Autónoma de Madrid c/ Nicolás Cabrera 13-15 Campus de Cantoblanco UAM E-28049 Madrid Spain LUTH GEPI and LERMA Observatoire de Paris Université PSL Université Paris Cité CNRS 5 place Jules Janssen F-92190 Meudon France INAF – Osservatorio

A wide variety of Galactic sources show transient emission at soft and hard X-ray energies: low- and high-mass X-ray binaries containing compact objects, isolated neutron stars exhibiting extreme variability as magnetars as well as pulsar-wind nebulae. Although most of them can show emission up to MeV and/or GeV energies, many have not yet been detected in the TeV domain by Imaging Atmospheric Cherenkov Telescopes. In this paper, we explore the feasibility of detecting new Galactic transients with the Cherenkov Telescope Array Observatory (CTAO) and the prospects for studying them with Target of Opportunity observations. We show that CTAO will likely detect new sources in the TeV regime, such as the massive microquasars in the Cygnus region, low-mass X-ray binaries with low-viewing angle, flaring emission from the Crab pulsar-wind nebula or other novae explosions, among others. Since some of these sources could also exhibit emission at larger time-scales, we additionally test their detectability at longer exposures. We finally discuss the multiwavelength synergies with other instruments and large astronomical facilities.

关键词： binaries: general stars: magnetars novae cataclysmic variables pulsars: general gamma-rays: general

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