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Dynamics of multiplayer games on complex networks using territorial interactions

Physical Review E 2019年第3期99卷 032306-032306页

作者： Pedro H. T. Schimit Karan Pattni Mark Broom Informatics and Knowledge Management Graduate Program Universidade Nove de Julho Rua Vergueiro 235/249 CEP 01504-000 São Paulo São Paulo Brazil Department of Mathematical Sciences University of Liverpool Mathematical Sciences Building Liverpool L69 7ZL United Kingdom Department of Mathematics City University of London Northampton Square London EC1V 0HB United Kingdom

The modeling of evolution in structured populations has been significantly advanced by evolutionary graph theory, which incorporates pairwise relationships between individuals on a network. More recently, a new framework has been developed to allow for multiplayer interactions of variable size in more flexible and potentially changing population structures. While the theory within this framework has been developed and simple structures considered, there has been no systematic consideration of a large range of different population structures, which is the subject of this paper. We consider a large range of underlying graphical structures for the territorial raider model, the most commonly used model in the new structure, and consider a variety of important properties of our structures with the aim of finding factors that determine the fixation probability of mutants. We find that the graphical temperature and the average group size, as previously defined, are strong predictors of fixation probability, while all other properties considered are poor predictors, although the clustering coefficient is a useful secondary predictor when combined with either temperature or group size. The relationship between temperature or average group size and fixation probability is sometimes, however, nonmonotonic, with a directional reverse occurring around the temperature associated with what we term “completely mixed” populations in the case of the hawk-dove game, but not the public goods game.

关键词： Ecology & evolution Evolutionary dynamics Network evolution Network structure

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Revised JNLPBA corpus: A revised version of biomedical NER corpus for relation extraction task

arXiv

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arXiv 2019年

作者： Huang, Ming-Siang Lai, Po-Ting Tsai, Richard Tzong-Han Hsu, Wen-Lian Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang Ming University Taipei Taiwan Department of Computer Science National Tsing-Hua University Hsinchu Taiwan Intelligent Agent Systems Laboratory Institute of Information Science Academia Sinica Taipei Taiwan Intelligent Information Service Research Laboratory Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The advancement of biomedical named entity recognition (BNER) and biomedical relation extraction (BRE) researches promotes the development of text mining in biological domains. As a cornerstone of BRE, robust BNER system is required to identify the mentioned NEs in plain texts for further relation extraction stage. However, the current BNER corpora, which play important roles in these tasks, paid less attention to achieve the criteria for BRE task. In this study, we present Revised JNLPBA corpus, the revision of JNLPBA corpus, to broaden the applicability of a NER corpus from BNER to BRE task. We preserve the original entity types including protein, DNA, RNA, cell line and cell type while all the abstracts in JNLPBA corpus are manually curated by domain experts again basis on the new annotation guideline focusing on the specific NEs instead of general terms. Simultaneously, several imperfection issues in JNLPBA are pointed out and made up in the new corpus. To compare the adaptability of different NER systems in Revised JNLPBA and JNLPBA corpora, the F1-measure was measured in three open sources NER systems including BANNER, Gimli and NERSuite. In the same circumstance, all the systems perform average 10% better in Revised JNLPBA than in JNLPBA. Moreover, the cross-validation test is carried out which we train the NER systems on JNLPBA/Revised JNLPBA corpora and access the performance in both protein-protein interaction extraction (PPIE) and biomedical event extraction (BEE) corpora to confirm that the newly refined Revised JNLPBA is a competent NER corpus in biomedical relation application. The revised JNLPBA corpus is freely available at ***/BNER/Content/Revised_***. Copyright © 2019, The Authors. All rights reserved.

关键词： Proteins

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Tau pathology is associated with neuropsychiatric symptoms in a cohort of individuals of predominantly Mexican descent at risk for autosomal-dominant Alzheimer’s disease

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Alzheimer's & Dementia 2023年第S10期19卷

作者： Nancy E Ortega Judy Pa Yonggang Shi Abhay P Sagare Berislav V Zlokovic Helena C Chui John M Ringman Neurosciences Graduate Program University of California San Diego La Jolla CA USA La Jolla CA USA Alzheimer’s Disease Cooperative Study (ADCS) University of California San Diego La Jolla CA USA La Jolla CA USA Department of Neurology Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Los Angeles CA USA Zilkha Neurogenetic Institute Keck School of Medicine University of Southern California Los Angeles CA USA University of Southern California Los Angeles CA USA Alzheimer’s Disease Research Center Keck School of Medicine University of Southern California Los Angeles CA USA Department of Neurology Keck School of Medicine University of Southern California Los Angeles CA USA

Background Neuropsychiatric symptoms are common in Alzheimer’s disease (AD), causing significant suffering and diminished quality of life for both individuals and caregivers. The goal of this study was to investigate the relationship between tau pathology in vivo and neuropsychiatric symptoms within a cohort of individuals of predominantly Mexican ancestry at risk for fully penetrant autosomal dominant AD (ADAD) caused by mutations in PSEN1 and APP genes (Figure 1). Method Participants from the Estudio de la Enfermedad de Alzheimer en Jalisciences (EEAJ) cohort were selected based on availability of demographic variables, mutation carrier status, tau PET scans (flortaucipir), and completion of the neuropsychiatric inventory (NPI). Linear regression models were used to investigate whether regional tau PET standard uptake value ratio (SUVR) predicted total score on the NPI. Models were adjusted for age, sex, and mutation carrier status. Participants were characterized as carriers if they possessed one of the following ADAD mutations: PSEN1 A431E, PSEN1 I180F, or APP V717I. Analyses were done with and without outliers. The 12-item total NPI score was used for all analyses. The primary brain regions of interest (i.e., medial orbitofrontal, caudal anterior cingulate, rostral anterior cingulate, parahippocampal, precuneus, and lateral occipital) were selected based on previous findings. Additional analyses were conducted only on individuals who were carriers of 1 of the ADAD mutations. Result The sample consisted of 31 participants (45% women, mean age±SD = 37.1±11.7 years, 71% ADAD mutation carriers [81% PSEN1 A431E, 16% PSEN1 I180F, and 3% APP V717I carriers]). A significant relationship was found between regional tau pathology and neuropsychiatric symptoms for all six brain regions (Table 1), all p’s < 0.01. Within all regression models, regional tau pathology explained between 24% to 49% of the variance in neuropsychiatric symptoms. Sub-set analyses completed on the A

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Advances of machine learning in molecular modeling and simulation

arXiv

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arXiv 2019年

作者： Haghighatlari, Mojtaba Hachmann, Johannes Department of Chemical and Biological Engineering University at Buffalo State University of New York BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Graduate Program University at Buffalo State University of New York BuffaloNY14260 United States New York State Center of Excellence in Materials Informatics BuffaloNY14203 United States

In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical supervised learning approach for chemical problems, we showcase areas and state-of-the-art examples of their deployment. In this context, we discuss how machine learning relates to, supports, and augments more traditional physics-based approaches in computational research. We conclude by outlining challenges and future research directions that need to be addressed in order to make machine learning a mainstream chemical engineering tool. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

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

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

关键词： Learning algorithms

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MAGIC observation of BL Lacertae flaring period in 2020 38

MAGIC observation of BL Lacertae flaring period in 2020

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38th International Cosmic Ray Conference, ICRC 2023

作者： Imazawa, R. Strišković, J. Jormanainen, J. Truzzi, S. Lindfors, E. Dominis Prester, D. Bonnoli, G. Fukazawa, Y. Suda, Y. Abe, H. Abe, S. Abhir, J. Acciari, V.A. Agudo, I. Aniello, T. Ansoldi, S. Antonelli, L.A. Arbet Engels, A. Arcaro, C. Artero, M. Asano, K. Baack, D. Babić, A. Baquero, A. Barres de Almeida, U. Barrio, J.A. Batković, I. Baxter, J. Becerra González, J. Bednarek, W. Bernardini, E. Bernardos, M. Bernete, J. Berti, A. Besenrieder, J. Bigongiari, C. Biland, A. Blanch, O. Bošnjak, Ž. Burelli, I. Busetto, G. Campoy-Ordaz, A. Carosi, A. Carosi, R. Carretero-Castrillo, M. Castro-Tirado, A.J. Ceribella, G. Chai, Y. Chilingarian, A. Cifuentes, A. Cikota, S. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. D’Amico, G. D’Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. Del Popolo, A. Delfino, M. Delgado, J. Delgado Mendez, C. Depaoli, D. Di Pierro, F. Di Venere, L. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsaesser, D. Emery, G. Escudero, J. Fariña, L. Fattorini, A. Foffano, L. Font, L. Fröse, S. Fukami, S. Fukazawa, Y. García López, R.J. Garczarczyk, M. Gasparyan, S. Gaug, M. Giesbrecht Paiva, J.G. Giglietto, N. Giordano, F. Gliwny, P. Godinović, N. Grau, R. Green, D. Green, J.G. Hadasch, D. Hahn, A. Hassan, T. Heckmann, L. Herrera, J. Hrupec, D. Hütten, M. Imazawa, R. Inada, T. Iotov, R. Ishio, K. Jiménez Martínez, I. Jormanainen, J. Kerszberg, D. Kluge, G.W. Kobayashi, Y. Kouch, P.M. Kubo, H. Kushida, J. Láinez Lezáun, M. Lamastra, A. Lelas, D. Leone, F. Lindfors, E. Linhoff, L. Lombardi, S. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Loporchio, S. Lorini, A. Lyard, E. Machado de Oliveira Fraga, B. Majumdar, P. Makariev, M. Maneva, G. Mang, N. Manganaro, M. Mangano, S. Mannheim, K. Mariotti, M. Martínez, M. Martínez-Chicharro, M. Mas-Aguilar, A. Mazin, D. Menchiari, S. Mender, S. Mićanović, S. Miceli, D. Miener, T. Miranda, J.M. Mirzoyan, R. Molero González, M. Molina, E. Mondal, H.A. Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan Josip Juraj Strossmayer University of Osijek Department of Physics Osijek31500 Croatia Finnish Centre for Astronomy with ESO University of Turku TurkuFI-20014 Finland Università di Siena INFN Pisa SienaI-53100 Italy INFN Sezione di Bari Dipartimento Interateneo di Fisica dell’Università e del Politecnico di Bari BariI-70125 Italy RomeI-00136 Italy The University of Tokyo Chiba Kashiwa277-8582 Japan ETH Zürich ZürichCH-8093 Switzerland Instituto de Astrofísica de Canarias Dpto. de Astrofísica Universidad de La Laguna Tenerife La LagunaE-38200 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Università di Udine INFN Trieste UdineI-33100 Italy Max-Planck-Institut für Physik MünchenD-80805 Germany Università di Padova INFN PadovaI-35131 Italy Barcelona BellaterraE-08193 Spain Technische Universität Dortmund DortmundD-44221 Germany Zagreb10000 Croatia IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain URCA RJ Rio de Janeiro22290-180 Brazil University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz90-236 Poland Centro de Investigaciones Energéticas Medioambientales y Tecnológicas MadridE-28040 Spain Departament de Física CERES-IEEC Universitat Autònoma de Barcelona BellaterraE-08193 Spain Università di Pisa INFN Pisa PisaI-56126 Italy Universitat de Barcelona ICCUB IEEC-UB BarcelonaE-08028 Spain Armenian MAGIC Group A. Alikhanyan National Science Laboratory Yerevan0036 Armenia Department for Physics and Technology University of Bergen Norway INFN MAGIC Group INFN Sezione di Catania Dipartimento di Fisica e Astronomia University of Catania CataniaI-95123 Italy INFN MAGIC Group INFN Sezione di Torino Università degli Studi di Torino TorinoI-10125 Italy INFN MAGIC Group INF

BL Lacertae (BL Lac) is one of the low-frequency peaked BL Lac object (LBL). Since August 2020, BL Lac has shown a historically high activity in HE (high-energy, 0.1 GeV 100 GeV) gamma-ray range, BL Lac was detected in several nights during an especially bright flare simultaneous with a HE flare. In the long-term light curves, X-ray and HE gamma-ray flux overall show correlation, but sometimes only HE gamma-ray was bright. These multi-wavelength (MWL) correlations may relate to the emission mechanisms and the origin of the observed flaring activity. In this contribution we will present some of the preliminary results on the MWL campaign of BL Lac in September 2020, with a focus on the X-ray and gamma-ray activity, based on the data collected by Swift-XRT, MAGIC and Fermi-LAT telescopes. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

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The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2

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四川生理科学杂志 2022年第7期44卷 1299-1299页

作者： Jonathan E Pekar Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA 92093 USA Department of Biomedical Informatics University of California San Diego La Jolla CA 92093 USA Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Department of Immunology and Microbiology The Scripps Research Institute La Jolla CA 92037 USA Department of Computer Science and Engineering University of California San Diego La Jolla CA 92093 USA Department of Mathematics University of California San Diego La Jolla CA 92093 USA Department of Ecology and Evolutionary Biology University of Arizona Tucson AZ 85721 USA W. Harry Feinstone Department of Molecular Microbiology and Immunology Johns Hopkins Bloomberg School of Public Health Baltimore MD 21205 USA New York City Public Health Laboratory New York City Department of Health and Mental Hygiene New York NY 11101 USA Department of Microbiology Institute for Viral Diseases Biosafety Center College of Medicine Korea University Seoul South Korea BK21 Graduate Program Department of Biomedical Sciences Korea University College of Medicine Seoul 02841 Republic of Korea National Public Health Laboratory National Centre for Infectious Diseases Singapore Malaysia Genome and Vaccine Institute Jalan Bangi 43000 Kajang Selangor Malaysia Department of Medicine University of California San Diego La Jolla CA 92093 USA Department of Microbiology and Immunology School of Medicine Tulane University New Orleans LA 70112 USA Zalgen Labs Frederick MD 21703 USA Global Virus Network (GVN) Baltimore MD 21201 USA Sydney Institute for Infectious Diseases School of Life and Environmental Sciences and School of Medical Sciences The University of Sydney Sydney NSW 2006 Australia Institute of Evolutionary Biology University of Edinburgh King’s Buildings Edinburgh EH9 3FL UK Department of Biomathematics David Geffen School of Medicine Un

Understanding the circumstances that lead to pandemics is important for their prevention. Here, we analyze the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted A and B. Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October-8 December), while the separate introduction of lineage A likely occurred within weeks of this event.

关键词： epidemiology likely acute

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Correction: Mitochondrial DNA variation in Alzheimer's disease reveals a unique microprotein called SHMOOSE

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Molecular psychiatry 2023年 2023 Jan 20页

作者： Brendan Miller Su-Jeong Kim Hemal H Mehta Kevin Cao Hiroshi Kumagai Neehar Thumaty Naphada Leelaprachakul Regina Gonzalez Braniff Henry Jiao Joan Vaughan Jolene Diedrich Alan Saghatelian Thalida E Arpawong Eileen M Crimmins Nilüfer Ertekin-Taner Meral A Tubi Evan T Hare Meredith N Braskie Léa Décarie-Spain Scott E Kanoski Francine Grodstein David A Bennett Lu Zhao Arthur W Toga Junxiang Wan Kelvin Yen Pinchas Cohen Leonard Davis School of Gerontology University of Southern California Los Angeles CA USA. Neuroscience Graduate Program University of Southern California Los Angeles CA USA. Clayton Foundation Laboratories for Peptide Biology The Salk Institute for Biological Studies La Jolla CA USA. Mayo Clinic Department of Neuroscience Jacksonville FL USA. Imaging Genetics Center Institute for Neuroimaging and Informatics University of Southern California Los Angeles CA USA. Department of Neurology University of Southern California Los Angeles CA USA. Human and Evolutionary Biology Section Department of Biological Sciences Dornsife College of Letters Arts and Sciences University of Southern California Los Angeles CA USA. Rush Alzheimer's Disease Center Rush University Medical Center Chicago IL USA. Laboratory of Neuro Imaging USC Stevens Neuroimaging and Informatics Institute Keck School of Medicine of University of Southern California Los Angeles CA USA. Leonard Davis School of Gerontology University of Southern California Los Angeles CA USA. hassy@usc.edu.

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Physics Performance of the Large-Sized Telescope prototype of the Cherenkov Telescope Array 37

Physics Performance of the Large-Sized Telescope prototype o...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Abe, H. Aguasca, A. Agudo, I. Antonelli, L.A. Aramo, C. Armstrong, T. Artero, M. Asano, K. Ashkar, H. Aubert, P. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Batkovic, I. Becerra González, J. Bernardos, M.I. Berti, A. Biederbeck, N. Bigongiari, C. Blanch, O. Bonnoli, G. Bordas, P. Bose, D. Bulgarelli, A. Burelli, I. Buscemi, M. Cardillo, M. Caroff, S. Carosi, A. Cassol, F. Cerruti, M. Chai, Y. Cheng, K. Chikawa, M. Chytka, L. Contreras, J.L. Cortina, J. Costantini, H. Dalchenko, M. De Angelis, A. de Bony de Lavergne, M. Deleglise, G. Delgado, C. Delgado Mengual, J. della Volpe, D. Depaoli, D. Di Pierro, F. Di Venere, L. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsässer, D. Emery, G. Escudero, J. Fiasson, A. Foffano, L. Fonseca, M.V. Freixas Coromina, L. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Green, D. Grespan, P. Gunji, S. Hackfeld, J. Hadasch, D. Hahn, A. Hassan, T. Hayashi, K. Heckmann, L. Heller, M. Herrera Llorente, J. Hirotani, K. Hoffmann, D. Horns, D. Houles, J. Hrabovsky, M. Hrupec, D. Hui, D. Hütten, M. Inada, T. Inome, Y. Iori, M. Ishio, K. Iwamura, Y. Jacquemont, M. Jimenez Martinez, I. Jouvin, L. Jurysek, J. Kagaya, M. Karas, V. Katagiri, H. Kataoka, J. Kerszberg, D. Kobayashi, Y. Kong, A. Kubo, H. Kushida, J. Lamanna, G. Lamastra, A. Le Flour, T. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Luque-Escamilla, P.L. Majumdar, P. Makariev, M. Mandat, D. Manganaro, M. Mannheim, K. Mariotti, M. Marquez, P. Marsella, G. Martí, J. Martinez, O. Martínez, G. Martínez, M. Marusevec, P. Mas, A. Maurin, G. Mazin, D. Mestre Guillen, E. Micanovic, S. Miceli, D. Miener, T. Miranda, J.M. Miranda, L.D.M. Mirzoyan, R. Mizuno, T. Molina, E. Montaruli, T. Monteiro, I. Moralejo, A. Morcuende, D. Moretti, E. Morselli, A. Mrakovcic, K. Murase, K. Nagai, A. Nakamori, T. Nickel, L. Nieto, D. Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Kashiwa Chiba277-8582 Japan Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Spain Instituto de Astrofísica de Andalucía-CSIC Granada18080 Spain INAF Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy Aix Marseille Univ CNRS IN2P3 CPPM France 08193 Spain IRFU CEA Université Paris-Saclay France LAPP Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS-IN2P3 9 Chemin de Bellevue - BP 110 AnnecyCedex 74941 France Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan EMFTEL Department IPARCOS Universidad Complutense de Madrid MadridE-28040 Spain INAF Osservatorio di Astrofisica e Scienza Spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil INFN Sezione di Padova PadovaI-35131 Italy Università degli Studi di Padova Via Marzolo 8 Padova35131 Italy Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Spain Max-Planck-Institut für Physik München80805 Germany Department of Physics TU Dortmund University Otto-Hahn-Str. 4 Dortmund44221 Germany Saha Institute of Nuclear Physics INFN Sezione di Trieste Università degli Studi di Udine Italy INFN Sezione di Catania Italy Italy University of Geneva Département de Physique Nucléaire et Corpusculaire Switzerland Palacky University Olomouc Faculty of Science Czech Republic CIEMAT Port d'Informació Científica Spain INFN Sezione di Torino via P. Giuria 1 Torino10125 Italy Dipartimento di Fisica Universitá degli Studi di Torino Italy INFN Sezione di Bari Università di Bari Italy University of Rijeka Department of Physics Croatia Institute for Theoretical Physics and Astrophysics Universität Würzburg Germany Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Japan INFN Sezione di Bari Politecnico di B

The Large-Sized Telescope (LST) prototype of the future Cherenkov Telescope Array (CTA) is located at the Northern site of CTA, on the Canary Island of La Palma. It is designed to provide optimal performance in the lowest part of the energy range covered by CTA, observing gamma rays down to energies of tens of GeV. The LST prototype started performing astronomical observations in November 2019 during the commissioning of the telescope and it has been taking data since then. In this contribution, we will present the tuning of the characteristics of the telescope in the Monte Carlo (MC) simulations to describe the data obtained, the estimation of its angular and energy resolution, and an evaluation of its sensitivity, both with simulations and with observations of the Crab Nebula. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Telescopes

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Commissioning of the camera of the first Large Size Telescope of the Cherenkov Telescope Array 37

Commissioning of the camera of the first Large Size Telescop...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Saito, T. Delgado, C. Blanch, O. Artero, M. Barrio, J.A. Cassol, F. Diaz, C. Hadasch, D. Hoffmann, D. Houles, J. Inome, Y. Iori, M. Jouvin, L. Kerszberg, D. Kobayashi, Y. Kubo, H. Martinez, G. Mazin, D. Moretti, E. Nakamori, T. Nozaki, S. Oka, T. Okumura, A. Palatiello, M. Polo, M. Prast, J. Sakurai, S. Sunada, Y. Takahashi, M. Takeishi, R. Tejedor, L.A. Yamamoto, T. Yoshida, T. Abe, H. Aguasca, A. Agudo, I. Antonelli, L.A. Aramo, C. Armstrong, T. Asano, K. Ashkar, H. Aubert, P. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Batkovic, I. Becerra González, J. Bernardos, M.I. Berti, A. Biederbeck, N. Bigongiari, C. Bonnoli, G. Bordas, P. Bose, D. Bulgarelli, A. Burelli, I. Buscemi, M. Cardillo, M. Caroff, S. Carosi, A. Cerruti, M. Chai, Y. Cheng, K. Chikawa, M. Chytka, L. Contreras, J.L. Cortina, J. Costantini, H. Dalchenko, M. De Angelis, A. de Bony de Lavergne, M. Deleglise, G. Delgado Mengual, J. della Volpe, D. Depaoli, D. Di Pierro, F. Di Venere, L. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsässer, D. Emery, G. Escudero, J. Fiasson, A. Foffano, L. Fonseca, M.V. Freixas Coromina, L. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Green, D. Grespan, P. Gunji, S. Hackfeld, J. Hahn, A. Hassan, T. Hayashi, K. Heckmann, L. Heller, M. Herrera Llorente, J. Hirotani, K. Horns, D. Hrabovsky, M. Hrupec, D. Hui, D. Hütten, M. Inada, T. Ishio, K. Iwamura, Y. Jacquemont, M. Jimenez Martinez, I. Jurysek, J. Kagaya, M. Karas, V. Katagiri, H. Kataoka, J. Kong, A. Kushida, J. Lamanna, G. Lamastra, A. Le Flour, T. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Luque-Escamilla, P.L. Majumdar, P. Makariev, M. Mandat, D. Manganaro, M. Mannheim, K. Mariotti, M. Marquez, P. Marsella, G. Martí, J. Martinez, O. Martínez, M. Marusevec, P. Mas, A. Maurin, G. Mestre Guillen, E. Micanovic, S. Miceli, D. Miener, T. Miranda, J.M. ICRR Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Chiba Kashiwa277-8582 Japan CIEMAT Avda. Complutense 40 Madrid28040 Spain The Barcelona Institute of Science and Technology Bellaterra Campus UAB Barcelona08193 Spain EMFTEL Department IPARCOS Universidad Complutense de Madrid Madrid28040 Spain Aix-Marseille Université CNRS IN2P3 CPPM 163 Avenue de Luminy MarseilleCedex 09 13288 France INFN Sezione di Roma La Sapienza P.le Aldo Moro 2 Roma00185 Italy Division of Physics and Astronomy Graduate School of Science Kyoto University Sakyo-ku Kyoto606-8502 Japan Department of Physics Yamagata University Yamagata990-8560 Japan ISEE Nagoya University Chikusa-ku Nagoya464-8602 Japan Graduate School of Science and Engineering Saitama University 255 Simo-Ohkubo Sakura-ku Saitama338-8570 Japan CNRS Laboratoire d'Annecy de Physique des Particules IN2P3 Univ. Grenoble Alpes Univ. Savoie Mont Blanc Annecy74000 France Department of Physics Konan University Hyogo Kobe658-8501 Japan Faculty of Science Ibaraki University Ibaraki Mito310-8512 Japan Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Spain Instituto de Astrofísica de Andalucía CSIC Spain INAF - Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy IRFU CEA Université Paris-Saclay France Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil INFN Sezione di Padova Università degli Studi di Padova Italy Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Spain Max-Planck-Institut für Physik Germany Department of Physics TU Dortmund University Germany Saha Institute of Nuclear Physics India INFN Sezione di Trieste Università degli Studi di Udine Italy INFN Sezio

The first Large Size Telescope (LST-1) of the Cherenkov Telescope Array has been operational since October 2018 at La Palma, Spain. We report on the results obtained during the camera commissioning. The noise level of the readout is determined as a 0.2 p.e. level. The gain of PMTs are well equalized within 2% variation, using the calibration flash system. The effect of the night sky background on the signal readout noise as well as the PMT gain estimation are also well evaluated. Trigger thresholds are optimized for the lowest possible gamma-ray energy threshold and the trigger distribution synchronization has been achieved within 1 ns precision. Automatic rate control realizes the stable observation with 1.5% rate variation over 3 hours. The performance of the novel DAQ system demonstrates a less than 10% dead time for 15 kHz trigger rate even with sophisticated online data correction. © 2022 Sissa Medialab Srl. All rights reserved.

关键词： Cameras

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