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Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events

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Computing and Software for Big Science 2022年第1期6卷 1-35页

作者： Aad, G. Abbott, B. Abbott, D.C. Abud, A. Abed Abeling, K. Abhayasinghe, D.K. Abidi, S.H. AbouZeid, O.S. Abraham, N.L. Abramowicz, H. Abreu, H. Abulaiti, Y. Hoffman, A. C. Abusleme Acharya, B.S. Achkar, B. Adam, L. Bourdarios, C. Adam Adamczyk, L. Adamek, L. Adelman, J. Adiguzel, A. Adorni, S. Adye, T. Affolder, A.A. Afik, Y. Agapopoulou, C. Agaras, M.N. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J.A. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ai, X. Aielli, G. Akatsuka, S. Akbiyik, M. Åkesson, T.P.A. Akilli, E. Akimov, A.V. Khoury, K. Al Alberghi, G.L. Albert, J. Verzini, M. J. Alconada 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. Camelia, E. Alunno Estevez, M. Alvarez Alviggi, M.G. Coutinho, Y. Amaral Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Santos, S. P. Amor Dos Amoroso, S. Amrouche, C.S. Anastopoulos, C. Andari, N. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Andrei, V. Anelli, C.R. 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. Pozo, J. A. Aparisi Aparo, M.A. Bella, L. Aperio Aranzabal, N. Ferraz, V. Araujo Arcangeletti, C. Arce, A.T.H. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Armstrong, A. Arnaez, O. Arnold, H. Tame, Z. P. Arrubarrena 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. Austrup, V.A. Avolio, G. Ayoub, M.K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Backman, F. Bagnaia, P. Bahrasemani, H. Bailey, A.J. Bailey, V.R. Baines, J.T. Bakalis, C. Baker, O.K. Bakker, P.J. Bakos, E. Gupta, D. Bakshi Balaji, S. Balasubramanian, R. Baldin, E.M. Balek, P. Balli, F. Balunas, W.K. Balz, J. Banas, E. Bandieramonte, M. Bandyopadhyay, A. Barak, L. Barbe, W.M. Ba Department of Physics University of Adelaide Adelaide Australia Physics Department SUNY Albany Albany NY United States Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Istanbul Aydin University Application and Research Center for Advanced Studies Istanbul Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Univ. 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 Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan 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 and University of California Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Ei

The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy. © 2022, The Author(s).

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Prediction of soft proton intensities in the near-Earth space using machine learning

arXiv

引用

arXiv 2021年

作者： Kronberg, Elena A. Hannan, Tanveer Huthmacher, Jens Münzer, Marcus Peste, Florian Zhou, Ziyang Berrendorf, Max Faerman, Evgeniy Gastaldello, Fabio Ghizzardi, Simona Escoubet, Philippe Haaland, Stein Smirnov, Artem Sivadas, Nithin Allen, Robert C. Tiengo, Andrea Ilie, Raluca Munich Theresienstr. 41 Munich80333 Germany Institute of Informatics LMU Munich Oettingenstraße 67 MunichD-80538 Germany Department of Statistics LMU Munich Ludwigstraße 33 MunichD-80539 Germany Milano via A. Corti 12 MilanoI-20133 Italy European Space Research and Technology Centre Keplerlaan 1 Noordwjik2201 AZ Netherlands Birkeland Centre for Space Science University of Bergen Allégaten 55 BergenNO-5007 Norway Max Planck Institute for Solar System Research Justus-von-Liebig-Weg 3 Göttingen37077 Germany German Research Centre for Geosciences Albert-Einstein-Straße 42-46 PotsdamD-14473 Germany Department of Electrical and Computer Engineering Boston University 8 Saint Mary's Street BostonMA02134 United States Johns Hopkins University Applied Physics Lab 11100 Johns Hopkins Road LaurelMD20723 United States Scuola Universitaria Superiore IUSS Pavia piazza della Vittoria 15 PaviaI-27100 Italy INFN Sezione di Pavia via A. Bassi 6 PaviaI-27100 Italy University of Illinois at Urbana-Champaign ECEB 306 N. Wright Street 5054 UrbanaIL61801 United States

The spatial distribution of energetic protons contributes towards the understanding of magnetospheric dynamics. Based upon 17 years of the Cluster/RAPID observations, we have derived machine learning-based models to predict the proton intensities at energies from 28 to 1,885 keV in the 3D terrestrial magnetosphere at radial distances between 6 and 22 RE. We used the satellite location and indices for solar, solar wind and geomagnetic activity as predictors. The results demonstrate that the neural network (multi-layer perceptron regressor) outperforms baseline models based on the k-Nearest Neighbors and historical binning on average by ∼80% and ∼33%, respectively. The average correlation between the observed and predicted data is about 56%, which is reasonable in light of the complex dynamics of fast-moving energetic protons in the magnetosphere. In addition to a quantitative analysis of the prediction results, we also investigate parameter importance in our model. The most decisive parameters for predicting proton intensities are related to the location: ZGSE direction and the radial distance. Among the activity indices, the solar wind dynamic pressure is the most important. The results have a direct practical application, for instance, for assessing the contamination particle background in the X-Ray telescopes for X-ray astronomy orbiting above the radiation belts. To foster reproducible research and to enable the community to build upon our work we publish our complete code, the data, as well as weights of trained models. Further description can be found in the GitHub project at https://***/Tanveer81/deep horizon. © 2021, CC BY-NC-ND.

关键词： Forecasting

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Science with the Global Cosmic-ray Observatory (GCOS)

arXiv

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arXiv 2023年

作者： Batista, Rafael Alves Ahlers, M. Alves Batista, R. Assis, P. Battisti, M. Bellido, J.A. Bhatnagar, S. Bismark, K. Bister, T. Bohacova, M. Caccianiga, L. Caruso, R. Carvalho, W. Castellina, A. Colalillo, R. Convenga, F. Dawson, B. De Mitri, I. de Palma, F. Deval, L. di Matteo, A. DuVernois, M. Engel, R. Eser, J. Fitoussi, T. Flaggs, B. Fujii, T. Garzelli, M.V. Gkika, V. Hamal, P. Hariharan, B. Haungs, A. Hnatyk, B. Hörandel, J. Horváth, P. Hrabovsky, M. Huege, T. Ikeda, D. Isar, P.G. James, R. Johnsen, J. Kampert, K.-H. Kleifges, M. Koundal, P. Liu, J. Longo, F. Mandat, D. Mariş, I.C. Matthews, J. Mayotte, E. Medina-Tanco, G. Mulrey, K. Muzio, M.S. Nellen, L. Niechciol, M. Ogio, S. Oikonomou, F. Ostrowski, M. Pont, B. Porcelli, A. Rautenberg, J. Rieger, F. Risse, M. Roth, M. Sako, T. Salamida, F. Santangelo, A. Santos, E. Sarazin, F. Saveliev, A. Schimp, M. Schmidt, D. Schröder, F. Sergijenko, O. Sigl, G. Soldin, D. Spiczak, G. Tameda, Y. Unger, M. van Vliet, A. Vorobiov, S. Wibig, T. Wundheiler, B. Yushkov, A. Zotov, M. Instituto de Física Teórica UAM-CSIC C/Nicolás Cabrera 13-15 Madrid28049 Spain Departamento de Física Teórica Universidad Autónoma de Madrid M-15 Madrid28049 Spain Niels Bohr Institute Copenhagen Denmark Instituto de Física Teórica UAM-CSIC Madrid Spain Universidad Autónoma de Madrid Departamento de Física Teórica Madrid Spain Universidade de Lisboa Instituto Superior Técnico Lisboa Portugal Universidade de Lisboa Laboratório de Instrumentação e Física Experimental de Partículas Lisboa Portugal Université de Paris Astroparticule et Cosmologie Paris France The University of Adelaide Physics Department Adelaide Australia Dayalbagh Educational Institute Department of Physics and Computer Science Agra India Institute for Experimental Particle Physics Karlsruhe Germany Radboud University Institute for Mathematics Astrophysics and Particle Physics Nijmegen Netherlands Amsterdam Netherlands Institute of Physics The Czech Academy of Sciences Prague Czech Republic Sezione di Milano Milan Italy Department of Physics and Astronomy "E. Majorana" University of Catania Catania Italy Sezione di Catania Catania Italy Sezione di Torino Turin Italy Turin Italy Università degli Studi di Napoli "Federico II" Naples Italy Sezione di Napoli Naples Italy Università dell'Aquila Dipartimento di Scienze Fisiche e Chimiche L'Aquila Italy Laboratori Nazionali del Gran Sasso L'Aquila Italy Gran Sasso Science Institute L'Aquila Italy Università del Salento Dipartimento di Matematica e Fisica "E. De Giorgi" Lecce Italy Institute for Astroparticle Physics Karlsruhe Germany University of Wisconsin-Madison Department of Physics MadisonWI United States University of Wisconsin-Madison Wisconsin IceCube Particle Astrophysics Center MadisonWI United States The University of Chicago Department of Astronomy and Astrophysics ChicagoIL United States University of Delaware Bartol Research Institute NewarkDE United States Osaka Metropolitan University Gradu

The Global Cosmic-ray Observatory (GCOS) is a proposed large-scale observatory for studying ultra-high-energy cosmic particles, including ultra-high-energy cosmic rays (UHECRs), photons, and neutrinos. Its primary goal is to characterise the properties of the highest-energy particles in Nature with unprecedented accuracy, and to identify their elusive sources. With an aperture at least a ten-fold larger than existing observatories, this next-generation facility should start operating after 2030, when present-day detectors will gradually cease their activities. Here we briefly review the scientific case motivating GCOS. We present the status of the project, preliminary ideas for its design, and some estimates of its capabilities. © 2023, CC BY-NC-ND.

关键词： Observatories

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An environment for sustainable research software in Germany and beyond: Current state, open challenges, and call for action

arXiv

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arXiv 2020年

作者： Anzt, Hartwig Bach, Felix Druskat, Stephan Löffler, Frank Loewe, Axel Renard, Bernhard Y. Seemann, Gunnar Struck, Alexander Achhammer, Elke Aggarwal, Piush Appel, Franziska Bader, Michael Brusch, Lutz Busse, Christian Chourdakis, Gerasimos Dabrowski, Piotr W. Ebert, Peter Flemisch, Bernd Friedl, Sven Fritzsch, Bernadette Funk, Maximilian D. Gast, Volker Goth, Florian Grad, Jean-Noël Hermann, Sibylle Hohmann, Florian Janosch, Stephan Kutra, Dominik Linxweiler, Jan Muth, Thilo Peters-Kottig, Wolfgang Rack, Fabian Raters, Fabian H.C. Rave, Stephan Reina, Guido Reißig, Malte Ropinski, Timo Schaarschmidt, Joerg Seibold, Heidi Thiele, Jan P. Uekerman, Benjamin Unger, Stefan Weeber, Rudolf Germany Innovative Computing Lab University of Tennessee KnoxvilleTN United States Department of English Studies Friedrich Schiller University Jena Germany Germany Department of Computer Science Humboldt-Universität zu Berlin Germany Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany Michael Stifel Center Jena Germany Center for Computation and Technology Louisiana State University Baton Rouge United States Germany Robert Koch Institute Berlin Germany Hasso Plattner Institute Faculty of Digital Engineering University of Potsdam Germany Institute for Experimental Cardiovascular Medicine University Heart Centre Freiburg Bad Krozingen Germany Faculty of Medicine University of Freiburg Freiburg Germany Matters of Activity. Image Space Material. Cluster of Excellence at Humboldt-Universität zu Berlin Germany Technische Universität München Germany Language Technology Lab Universität Duisburg-Essen Germany Germany Department of Informatics Technical University of Munich Germany Technische Universität Dresden Germany Deutsches Krebsforschungszentrum Heidelberg Germany Scientific Computing in Computer Science Technical University Munich Germany School of Computing Communication and Business Hochschule für Technik und Wirtschaft Berlin Germany Center for Bioinformatics Saar Saarland Informatics Campus Germany Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Germany Berlin Institute of Health Germany Scientific Computing Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Bremerhaven Germany Max-Planck-Gesellschaft e.V. Institut für Theoretische Physik und Astrophysik Universität Würzburg Germany Institut für Computerphysik University of Stuttgart Germany University Library University of Stuttgart Germany Universität Bremen Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany European Molecular Biology Laboratory Heidelb

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin. Copyright © 2020, The Authors. All rights reserved.

关键词： Software design

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Image feature point matching for indoor positioning

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2017 International Conference on Image Processing, computer Vision, and Pattern Recognition, IPCV 2017

作者： Chuang, Chi-Hung Chen, Ying-Nong Fan, Kuo-Chin Dept. of Applied Informatics Fo Guang University ILan Taiwan Institute of Computer Science and Information Engineering National Central University Chung-Li Taiwan

ISBN: (纸本)1601324642

With the development of technology and the popularity of smart phones, people have become more considerate about the convenience in life of the disabilities and thus add lots of assistive equipment. Portable equipment such as smart phones and tablet are daily necessities for most of the people. However, there are few applications that are designed for the visual impairments and the elders. The proposed system can be used indoor to get the location of the user and plan the path. Via matching the image features, it will recognize where the user is. The purpose of this is to help the visual impairments or the elders to navigate to their destinations. In addition, it can also use robot to get the position by matching image features. The proposed system uses images captured from smart phones and are compared with database, then it will return the most similar position back to the user. User then can obtain some useful information of the surroundings such as the location of toilet or elevator, which will help user planning the path to destinations. Scale-invariant feature transform (SIFT) is used in this thesis. Via image features matching with pre-established scenes image features database. FLANN (Fast Library for Approximate Nearest Neighbors) is applied to build randomized k-d trees. The k-d tree can create an index for SIFT’s descriptor, which can speed up feature matching. Experimental results in the proposed method can achieve a good feasbilitu. CSREA Press ©

关键词： Nearest neighbor search

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2020年 945卷 v-vii页

作者： Kulczycki, Piotr Kacprzyk, Janusz Kóczy, László T. Mesiar, Radko Wisniewski, Rafal Faculty of Physics and Applied Computer Science AGH University of Science and Technology Krakow Poland Systems Research Institute Polish Academy of Sciences Warsaw Poland Systems Research Institute Polish Academy of Sciences Warsaw Poland Faculty of Engineering Sciences Széchenyi István University Győr Hungary Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Department of Electronic Systems Aalborg University Aalborg Denmark Faculty of Civil Engineering Slovak University of Technology Bratislava Slovakia

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Enterprise Architecture for the Sensing Enterprise a Research Framework

Enterprise Architecture for the Sensing Enterprise a Researc...

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International Conference of Information and Communication Technology (ICoICT)

作者： Erda Guslinar Perdana Husni S Sastramihardja Iping Supriana Suwardi School of Applied Science Institut Teknologi Bandung (ITB) Bandung Indonesia Faculty of Computer Science Esa Unggul University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology (ITB) Bandung Indonesia

ISBN: (纸本)9781538645734

Internet of Things (IoT) will change many things, open up opportunities and enabling something that was not possible before. IoT provides sensing capability so that enterprise has better global context awareness. This is the Sensing Enterprise, an enterprise that obtains multidimensional information from physical or virtual objects in a connected environment. This sensing capability is expected to increase the capacity and capability of the enterprise in responding to sustainability challenges. This paper proposes a research framework as an alternative guide for researchers to produce various artifacts or theories to realize The Sensing Enterprise for sustainability achievement. The method used in developing the framework is modification/adaptation of transdisciplinary research model and information system research model. This framework has three main area consist of scientific base, enterprise architecture research process and sustainability achievement.

关键词： Sustainable development Sensors computer architecture Internet of Things Standards Information systems Adaptation models

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A new colluded adversarial VNet embeddings attack in cloud 18

A new colluded adversarial VNet embeddings attack in cloud

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18th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2017

作者： Liu, I-Hsien Fang, Tay-Jiun Li, Jung-Shian Sun, Meng-Wei Liu, Chuan-Gang Institute of Computer and Communication Engineering National Cheng Kung University Tainan Taiwan Department of Applied Informatics and Multimedia Chia-Nan University of Pharmacy and Science Tainan Taiwan

ISBN: (纸本)9781538631515

Nowadays, network virtualization has been widely investigated in order to prevent Internet ossification, and develop future emerging network applications flexibly. However, prior work by Pignolet et al. shows the possible attacking methodology with which the attackers can disclose the whole cloud topology while deploying virtual networks in cloud named Topology Disclosure Attack. In this attack model, the attacker pretends to deploy virtual networks in cloud by issuing the graph requests to service provider. And the service provider responds the requests to the attacker after examining his/her topology resources. With this request/reply model, Pignolet et al. believe this attack eventually infers the targeted topology. However, one vital reason leads this attack to the failure- too many virtual requests from one adversary in a time. This paper tries to provide a new topology disclosure attack model, which multiple attackers launch attacks at the same time with the assistance of proposed Query-Trie and network tomography technique. Hence, in this paper, we propose much more possible attack model in cloud and this topic also encourages the network researchers to develop resistance mechanism against it in the future. © 2017 IEEE.

关键词： Topology

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Rich dynamics and anticontrol of extinction in a prey-predator system

arXiv

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arXiv 2019年

作者： Danca, Marius-F Fečkan, Michal Kuznetsov, Nikolay Cheng, Guanrong Dept. of Mathematics and Computer Science Avram Iancu University of Cluj- Napoca Romania Romanian Institute of Science and Technology Cluj-Napoca Romania Dept. of Mathematical Analysis and Numerical Mathematics Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Mathematical Institute Slovak Academy of Sciences Dept. of Applied Cybernetics Saint-Petersburg State University Russia Dept. of Mathematical Information Technology University of Jyväskylä Finland Dept. of Electronic Engineering City University of Hong Kong Hong Kong

This paper reveals some new and rich dynamics of a two-dimensional prey-predator system and to anticontrol the extinction of one of the species. For a particular value of the bifurcation parameter, one of the system variable dynamics is going to extinct, while another remains chaotic. To prevent the extinction, a simple anticontrol algorithm is applied so that the system orbits can escape from the vanishing trap. As the bifurcation parameter increases, the system presents quasiperiodic, stable, chaotic and also hyperchaotic orbits. Some of the chaotic attractors are Kaplan-Yorke type, in the sense that the sum of its Lyapunov exponents is positive. Also, atypically for undriven discrete systems, it is numerically found that, for some small parameter ranges, the system seemingly presents strange nonchaotic attractors. It is shown both analytically and by numerical simulations that the original system and the anticontrolled system undergo several Neimark-Sacker bifurcations. Beside the classical numerical tools for analyzing chaotic systems, such as phase portraits, time series and power spectral density, the’0-1’ test is used to differentiate regular attractors from chaotic attractors. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectral density

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Search for magnetic monopoles and stable particles with high electric charges in (Formula presented.) TeV pp collisions with the ATLAS detector

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Journal of High Energy Physics 2023年第11期2023卷 112页

作者： Aad, G. Abbott, B. Abeling, K. Abicht, N.J. Abidi, S.H. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Acharya, B.S. Adam Bourdarios, C. Adamczyk, L. Addepalli, S.V. Addison, M.J. Adelman, J. Adiguzel, A. Adye, T. Affolder, A.A. Afik, Y. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ahuja, S. Ai, X. Aielli, G. Aikot, A. Ait Tamlihat, M. Aitbenchikh, B. Aizenberg, I. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Akiyama, D. Akolkar, N.N. Al Khoury, K. Alberghi, G.L. Albert, J. Albicocco, P. Albouy, G.L. Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, F. Algren, M. Alhroob, M. Ali, B. Ali, H.M.J. Ali, S. Alibocus, S.W. Aliev, M. Alimonti, G. Alkakhi, W. Allaire, C. Allbrooke, B.M.M. Allen, J.F. Allendes Flores, C.A. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Alvarez Estevez, M. Alvarez Fernandez, A. Alves Cardoso, M. Alviggi, M.G. Aly, M. Amaral Coutinho, Y. Ambler, A. Amelung, C. Amerl, M. Ames, C.G. Amidei, D. Amor Dos Santos, S.P. Amos, K.R. Ananiev, V. Anastopoulos, C. 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. Anulli, F. Aoki, M. Aoki, T. Aparisi Pozo, J.A. Aparo, M.A. Aperio Bella, L. Appelt, C. Apyan, A. Aranzabal, N. Arbiol Val, S.J. Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Arnaez, O. Arnold, H. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Assahsah, J. Assamagan, K. Astalos, R. Atashi, S. Atkin, R.J. Atkinson, M. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Auriol, A.D. Austrup, V.A. Avolio, G. Axiotis, K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Bagnaia, P. Bahmani, M. Bailey, A.J. Bailey, V.R. Baines, J.T. Baines, L. Baker, O.K. Bakos, E. Bakshi Gupta, D. Balakrishnan, V. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. Balli, F. Baltes, L.M. Balunas, W.K. Balz, J. Banas, E. Bandieramonte, M. Bandyopadh 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 APC Université Paris Cité CNRS/IN2P3 Paris 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 Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan 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 School of Science Shenzhen Campus of Sun Yat-sen University Shenzhen 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 University of California 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 Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziant

We present a search for magnetic monopoles and high-electric-charge objects using LHC Run (Formula presented.) TeV proton-proton collisions recorded by the ATLAS detector. A total integrated luminosity of 138 fb⁻¹ was collected by a specialized trigger. No highly ionizing particle candidate was observed. Considering the Drell-Yan and photon-fusion pair production mechanisms as benchmark models, cross-section upper limits are presented for spin-0 and spin-1/2 magnetic monopoles of magnetic charge 1g_D and 2g_D and for high-electric-charge objects of electric charge 20 ≤ |z| ≤ 100, for masses between 200 GeV and 4000 GeV. The search improves by approximately a factor of three the previous cross-section limits on the Drell-Yan production of magnetic monopoles and high-electric charge objects. Also, the first ATLAS limits on the photon-fusion pair production mechanism of magnetic monopoles and high-electric-charge objects are obtained. © The Author(s) 2023.

关键词： Beyond Standard Model Exotics Hadron-Hadron Scattering

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