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Quantum State Tomography via Reduced Density Matrices

Physical Review Letters 2017年第2期118卷 020401-020401页

作者： Tao Xin Dawei Lu Joel Klassen Nengkun Yu Zhengfeng Ji Jianxin Chen Xian Ma Guilu Long Bei Zeng Raymond Laflamme State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China Institute for Quantum Computing University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Physics University of Guelph Guelph N1G 2W1 Ontario Canada Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney New South Wales 2007 Australia Department of Mathematics and Statistics University of Guelph Guelph N1G 2W1 Ontario Canada State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences 100190 Beijing China Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland USA Department of Physics and Astronomy University of Waterloo Waterloo Ontario N2L 3G1 Canada Canadian Institute for Advanced Research Toronto M5G 1Z8 Ontario Canada Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada

Quantum state tomography via local measurements is an efficient tool for characterizing quantum states. However, it requires that the original global state be uniquely determined (UD) by its local reduced density matrices (RDMs). In this work, we demonstrate for the first time a class of states that are UD by their RDMs under the assumption that the global state is pure, but fail to be UD in the absence of that assumption. This discovery allows us to classify quantum states according to their UD properties, with the requirement that each class be treated distinctly in the practice of simplifying quantum state tomography. Additionally, we experimentally test the feasibility and stability of performing quantum state tomography via the measurement of local RDMs for each class. These theoretical and experimental results demonstrate the advantages and possible pitfalls of quantum state tomography with local measurements.

关键词： Quantum entanglement Quantum information processing Quantum tomography Random matrix theory

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Analysis by Sweep Frequency Impedance (SFI) Meter of 226-Hz and 1,000-Hz Tympanometries in Neonates

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Procedia IUTAM 2017年 24卷 5-14页

作者： Michio Murakoshi Kyosuke Sano Nattikan Kanka Naoto Yoshida Shinji Hamanishi Hiromichi Kiyokawa Risako Kakuta Sreedevi Aithal Venkatesh Aithal Joseph Kei Carlie Driscoll Andrew Swanston Sachiko Mtsutani Toshimitsu Kobayashi Hiroshi Wada Department of Mechanical Engineering Kagoshima University 1-21-40 Korimoto Kagoshima 890-0065 Japan Department of Bioengineering and Robotics Tohoku University 6-6-01 Aoba-yama Sendai 980-8579 Japan Department of Mechanical Engineering Sendai National College of Technology 48 Nodayama Natori 981-1239 Japan Department of Otorhinolaryngology Head and Neck Surgery Tohoku University Graduate School of Medicine Sendai 980-8574 Japan Audiology Department Townsville Hospital and Health Service Douglas 4814 Queensland Australia Hearing Research Unit for Children School of Health and Rehabilitation Sciences University of Queensland Brisbane 4072 Queensland Australia Department of ENT Townsville Hospital and Health Service Douglas 4814 Queensland Australia Department of Otorhinolaryngology Japanese Red Cross Sendai Hospital 2-43-3 Yagi-yama Honcho Sendai 982-8501 Japan Department of Otolaryngology and Center of Otologic Surgery Sen-en Rifu Hospital 2-2-108 Aoba-dai Rifu 981-0133 Japan Department of Intelligent Information Systems Faculty of Science and Technology Tohoku Bunka Gakuen University 6-45-1 Kunimi Sendai 981-8551 Japan

Conventional 226-Hz tympanometry has been acknowledged as a reliable method for detecting middle ear dysfunction in adults and children. However, its application has proved to be inaccurate in neonates younger than 7 months of age. In the present study, therefore, tympanograms of neonates were derived based on sweep frequency impedance (SFI) data and an attempt was made to investigate the reason why conventional tympanometry is inadequate for neonates. Analysis of neonatal tympanograms revealed that the application of a conventional 226-Hz probe tone leads to either a type A or a type M tympanogram. This is possibly due to the intrinsic oscillatory behavior of the external ear canal wall caused by its resonance at around 226 Hz.

关键词： Neonates Middle ear External ear canal wall Conventional tympanometry 226 Hz High-frequency tympanometry 1,000 Hz Sweep frequency impedance (SFI) meter

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Author Correction: The SEQC2 epigenomics quality control (EpiQC) study

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Genome biology 2021年第1期22卷 350页

作者： Jonathan Foox Jessica Nordlund Claudia Lalancette Ting Gong Michelle Lacey Samantha Lent Bradley W Langhorst V K Chaithanya Ponnaluri Louise Williams Karthik Ramaswamy Padmanabhan Raymond Cavalcante Anders Lundmark Daniel Butler Christopher Mozsary Justin Gurvitch John M Greally Masako Suzuki Mark Menor Masaki Nasu Alicia Alonso Caroline Sheridan Andreas Scherer Stephen Bruinsma Gosia Golda Agata Muszynska Paweł P Łabaj Matthew A Campbell Frank Wos Amanda Raine Ulrika Liljedahl Tomas Axelsson Charles Wang Zhong Chen Zhaowei Yang Jing Li Xiaopeng Yang Hongwei Wang Ari Melnick Shang Guo Alexander Blume Vedran Franke Inmaculada Ibanez de Caceres Carlos Rodriguez-Antolin Rocio Rosas Justin Wade Davis Jennifer Ishii Dalila B Megherbi Wenming Xiao Will Liao Joshua Xu Huixiao Hong Baitang Ning Weida Tong Altuna Akalin Yunliang Wang Youping Deng Christopher E Mason Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA. The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA. Department of Medical Sciences and Science for Life Laboratory Uppsala University Uppsala Sweden. EATRIS ERIC- European Infrastructure for Translational Medicine De Boelelaan 1118 1081 HZ Amsterdam The Netherlands. BRCF Epigenomics Core University of Michigan Medicine Ann Arbor MI 48109 USA. Department of Quantitative Health Sciences University of Hawaii John A. Burns School of Medicine Honolulu HI 96813 USA. Tulane University New Orleans LA 70118 USA. AbbVie Genomics Research Center 1 N. Waukegan Rd North Chicago IL 60036 USA. New England Biolabs Ipswich MA 01938 USA. Albert Einstein College of Medicine Bronx NY 10461 USA. Division of Hematology/Oncology Department of Medicine Epigenomics Core Facility Weill Cornell Medicine New York NY USA. Institute for Molecular Medicine Finland (FIMM) University of Helsinki Helsinki Finland. Illumina Inc Madison WI 53705 USA. Faculty of Biochemistry Biophysics and Biotechnology Jagiellonian University Krakow Poland. Małopolska Centre of Biotechnology Jagiellonian University Krakow Poland. New York Genome Center New York NY 10013 USA. Center for Genomics School of Medicine Loma Linda University Loma Linda CA 92350 USA. Department of Allergy and Clinical Immunology State Key Laboratory of Respiratory Disease Guangzhou Institute of Respiratory Health the First Affiliated Hospital of Guangzhou Medical University Guangzhou Guangdong China. Department of Neurology The Second Affiliated Hospital of Zhengzhou University Zhengzhou 450014 China. Development of Medicine the University of Chicago Chicago IL 60637 USA. Department of Neurology the Second Affiliated Hospital of Zhengzhou University Zhengzhou 450014 China. Bioinformatics and Omics Data Science Platform Berlin Institute f

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Equivalency of the diagnostic accuracy of the PHQ-8 and PHQ-9: a systematic review and individual participant data meta-analysis (vol 50, pg 1368, 2020)

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PSYCHOLOGICAL MEDICINE 2020年第16期50卷 2816-2816页

作者： Wu, Yin Levis, Brooke Riehm, Kira E. Saadat, Nazanin Levis, Alexander W. Azar, Marleine Rice, Danielle B. Boruff, Jill Cuijpers, Pim Gilbody, Simon Ioannidis, John P. A. Kloda, Lorie A. McMillan, Dean Patten, Scott B. Shrier, Ian Ziegelstein, Roy C. Akena, Dickens H. Arroll, Bruce Ayalon, Liat Baradaran, Hamid R. Baron, Murray Bombardier, Charles H. Butterworth, Peter Carter, Gregory Chagas, Marcos H. Chan, Juliana C. N. Cholera, Rushina Conwell, Yeates de Manvan Ginkel, Janneke M. Fann, Jesse R. Fischer, Felix H. Fung, Daniel Gelaye, Bizu Goodyear-Smith, Felicity Greeno, Catherine G. Hall, Brian J. Harrison, Patricia A. Harter, Martin Hegerl, Ulrich Hides, Leanne Hobfoll, Stevan E. Hudson, Marie Hyphantis, Thomas Inagaki, Masatoshi Jette, Nathalie Khamseh, Mohammad E. Kiely, Kim M. Kwan, Yunxin Lamers, Femke Liu, Shen-Ing Lotrakul, Manote Loureiro, Sonia R. Lowe, Bernd McGuire, Anthony Mohd-Sidik, Sherina Munhoz, Tiago N. Muramatsu, Kumiko Osorio, Flavia L. Patel, Vikram Pence, Brian W. Persoons, Philippe Picardi, Angelo Reuter, Katrin Rooney, Alasdair G. Santos, Ina S. Shaaban, Juwita Sidebottom, Abbey Simning, Adam Stafford, Lesley Sung, Sharon Tan, Pei Lin Lynnette Turner, Alyna van Weert, Henk C. White, Jennifer Whooley, Mary A. Winkley, Kirsty Yamada, Mitsuhiko Benedetti, Andrea Thombs, Brett D. [a1]Lady Davis Institute for Medical Research Jewish General Hospital Montréal Québec Canada [a2]Department of Psychiatry McGill University Montréal Québec Canada [a3]Department of Epidemiology Biostatistics and Occupational Health McGill University Montréal Québec Canada [a4]Department of Psychology McGill University Montréal Québec Canada [a5]Schulich Library of Physical Sciences Life Sciences and Engineering McGill University Montreal Quebec Canada [a6]Department of Clinical Neuro and Developmental Psychology Amsterdam Public Health Research Institute Vrije Universiteit Amsterdam the Netherlands [a7]Hull York Medical School and the Department of Health Sciences University of York Heslington York UK [a8]Department of Medicine Department of Health Research and Policy Department of Biomedical Data Science Department of Statistics Stanford University Stanford California USA [a9]Library Concordia University Montréal Québec Canada [a10]Department of Community Health Sciences University of Calgary Calgary Alberta Canada [a11]Hotchkiss Brain Institute and O’Brien Institute for Public Health University of Calgary Calgary Alberta Canada [a12]Department of Medicine Johns Hopkins University School of Medicine Baltimore Maryland USA [a13]Department of Psychiatry Makerere University College of Health Sciences Kampala Uganda [a14]Department of General Practice and Primary Health Care University of Auckland Auckland New Zealand [a15]Louis and Gabi Weisfeld School of Social Work Bar Ilan University Ramat Gan Israel [a16]Endocrine Research Center Institute of Endocrinology and Metabolism Iran University of Medical Sciences Tehran Iran [a17]Ageing Clinical & Experimental Research Team Institute of Applied Health Sciences School of Medicine Medical Sciences and Nutrition University of Aberdeen Aberdeen Scotland UK [a18]Department of Medicine McGill University Montréal Québec Canada [a19]Department of Rehabilitation Medicine University of Washington S

BackgroundItem 9 of the Patient Health Questionnaire-9 (PHQ-9) queries about thoughts of death and self-harm, but not suicidality. Although it is sometimes used to assess suicide risk, most positive responses are not associated with suicidality. The PHQ-8, which omits Item 9, is thus increasingly used in research. We assessed equivalency of total score correlations and the diagnostic accuracy to detect major depression of the PHQ-8 and *** conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic ***16 742 participants (2097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0.996 (95% confidence interval 0.996 to 0.996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0.02 (−0.06 to 0.00) and more specific by 0.01 (0.00 to 0.01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0.00 to 0.05 (0.03 at cutoff 10), and specificity was within 0.01 for all cutoffs (0.00 to 0.01).ConclusionsPHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.

关键词： Depression diagnostic accuracy individual participant data meta-analysis meta-analysis PHQ-8 PHQ-9 screening systematic review

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

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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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Predictive models for severe sepsis in adult ICU patients

Predictive models for severe sepsis in adult ICU patients

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2015 systems and Information engineering Design Symposium, SIEDS 2015

作者： Guillén, Joseph Liu, Jiankun Furr, Margaret Wang, Tianyao Strong, Stephen Moore, Christopher C. Flower, Abigail Barnes, Laura E. Data Science Institute University of Virginia United States School of Medicine University of Virginia United States Division of Infectious Diseases and International Health Department of Medicine University of Virginia United States Faculty Department of Systems and Information Engineering University of Virginia United States

ISBN: (纸本)9781479918324

Intensive Care Unit (ICU) patients have significant morbidity and mortality, often from complications that arise during the hospital stay. Severe sepsis is one of the leading causes of death among these patients. Predictive models have the potential to allow for earlier detection of severe sepsis and ultimately earlier intervention. However, current methods for identifying and predicting severe sepsis are biased and inadequate. The goal of this work is to identify a new framework for the prediction of severe sepsis and identify early predictors utilizing clinical laboratory values and vital signs collected in adult ICU patients. We explore models with logistic regression (LR), support vector machines (SVM), and logistic model trees (LMT) utilizing vital signs, laboratory values, or a combination of vital and laboratory values. When applied to a retrospective cohort of ICU patients, the SVM model using laboratory and vital signs as predictors identified 339 (65%) of the 3,446 patients as developing severe sepsis correctly. Based on this new framework and developed models, we provide a recommendation for the use in clinical decision support in ICU and non-ICU environments. © 2015 IEEE.

关键词： Forecasting

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Novel Efficient Technologies in Europe for Axle Bearing Condition Monitoring – the MAXBE Project

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Transportation Research Procedia 2016年 14卷 635-644页

作者： Cecília Vale Cristiana Bonifácio Jorge Seabra Rui Calçada Nadia Mazzino Massara Elisa Stefano Terribile Davide Anguita Emanuele Fumeo Carlos Saborido Tom Vanhonacker Edwin De Donder Michael Laeremans Frederik Vermeulen Diarmuid Grimes Construct-LESE Faculty of Engineering of University of Porto Rua Dr. Roberto Frias 4200-465 Porto Portugal Ansaldo STS S.p.A. Via Paolo Mantovani 3-5 16151 Genoa Italy Department of Computer Science Bioengineering Robotics and Systems Engineering (DIBRIS) Polytechnic School of University of Genoa Via all’Opera Pia 13 16145 Genoa Italy COMSA EMTE Edificio Numancia 1 Viriat 47 08014 Barcelona Spain Dynamics Structures and Systems International Jules Vandenbemptlaan 71 3001 Leuven Belgium Vlaamse Vervoermaatschappij De Lijn Grotehondstraat 58 2018 Antwerp Belgium I-MOSS N.V. Corbielaan 5 3060 Bertem Belgium Insight Center for Data Analytics Department of Computer Science University College Cork Cork Ireland

Axle bearing damage with possible catastrophic failures can cause severe disruptions or even dangerous derailments, potentially causing loss of human life and leading to significant costs for railway infrastructure managers and rolling stock operators. Consequently the axle bearing damage process has safety and economic implications on the exploitation of railways systems. Therefore it has been the object of intense attention by railway authorities as proved by the selection of this topic by the European Commission in calls for research proposals. The MAXBE Project ( http://***/ ), a EU-funded project, appears in this context and its main goal is to develop and to demonstrate innovative and efficient technologies which can be used for the onboard and wayside condition monitoring of axle bearings. The MAXBE (interoperable monitoring, diagnosis and maintenance strategies for axle bearings) project focuses on detecting axle bearing failure modes at an early stage by combining new and existing monitoring techniques and on characterizing the axle bearing degradation process. The consortium for the MAXBE project comprises 18 partners from 8 member states, representing operators, railway administrations, axle bearing manufactures, key players in the railway community and experts in the field of monitoring, maintenance and rolling stock. The University of Porto is coordinating this research project that kicked-off in November 2012 and it is completed on October 2015. Both on-board and wayside systems are explored in the project since there is a need for defining the requirement for the onboard equipment and the range of working temperatures of the axle bearing for the wayside systems. The developed monitoring systems consider strain gauges, high frequency accelerometers, temperature sensors and acoustic emission. To get a robust technology to support the decision making of the responsible stakeholders synchronized measurements from onboard and wayside monito

关键词： axle bearings condition monitoring system diagnosis condition-based maintenance

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Exploration of Image Search Results Quality Assessment

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IEEE Transactions on Big data 2015年第3期1卷 95-108页

作者： Tian, Xinmei Lu, Yijuan Stender, Nate Yang, Linjun Tao, Dacheng CAS Key Laboratory of Technology Geo-Spatial Information Processing and Application Systems University of Science and Technology of China Hefei230027 China Department of Computer Science Texas State University San MarcosTX78666 United States Department of Computer Science University of Nebraska-Lincoln LincolnNE68588 United States Microsoft Corporation RedmondWA98052-7329 United States Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Image retrieval plays an increasingly important role in our daily lives. There are many factors which affect the quality of image search results, including chosen search algorithms, ranking functions, and indexing features. Applying different settings for these factors generates search result lists with varying levels of quality. However, no setting can always perform optimally for all queries. Therefore, given a set of search result lists generated by different settings, it is crucial to automatically determine which result list is the best in order to present it to users. This paper aims to solve this problem and makes four main innovations. First, a preference learning model is proposed to quantitatively study and formulate the best image search result list identification problem. Second, a set of valuable preference learning related features is proposed by exploring the visual characters of returned images. Third, a query-dependent preference learning model is further designed for building a more precise and query-specific model. Fourth, the proposed approach has been tested on a variety of applications including reranking ability assessment, optimal search engine selection, and synonymous query suggestion. Extensive experimental results on three image search datasets demonstrate the effectiveness and promising potential of the proposed method. © 2015 IEEE.

关键词： Image retrieval

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DIAS: A disassemble-assemble framework for highly sparse text clustering

DIAS: A disassemble-assemble framework for highly sparse tex...

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SIAM International Conference on data Mining 2015, SDM 2015

作者： Liu, Hongfu Wu, Junjie Tao, Dacheng Zhang, Yuchao Fu, Yun Department of Electrical and Computer Engineering College of Engineering College of Computer and Information Science Northeastern University Boston United States School of Economics and Management Beihang University Beijing China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia Beijing Institute of System Engineering Beijing China

ISBN: (纸本)9781510811522

Upon extensive studies, text clustering remains a critical challenge in data mining community. Even by various techniques proposed to overcome some of these challenges, there still exist problems when dealing with weakly related or even noisy features. In response to this, we propose a Dlssemble-ASsemble (DIAS) framework for text clustering. DIAS employs simple random feature sampling to disassemble high-dimensional text data and gains diverse structural knowledge. This also does good to avoiding the bulk of noisy features. Then the multi-view knowledge is assembled by weighted Information-theoretic Consensus Clustering (IC-C) in order to gain a high-quality consensus partitioning. Extensive experiments on eight real-world text data sets demonstrate the advantages of DIAS over other widely used methods. In particular, DIAS shows strengths in learning from very weak basic partitionings. In addition, it is the natural suitability to distributed computing that makes DIAS become a promising candidate for big text clustering. Copyright © SIAM.

关键词： Distributed computer systems

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Energy calibration of CALET onboard the international space station

arXiv

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arXiv 2017年

作者： Asaoka, Y. Akaike, Y. Komiya, Y. Miyata, R. Torii, S. Adriani, O. Asano, K. Bagliesi, M.G. Bigongiari, G. Binns, W.R. Bonechi, S. Bongi, M. Brogi, P. Buckley, J.H. Cannady, N. Castellini, G. Checchia, C. Cherry, M.L. Collazuol, G. Di Felice, V. Ebisawa, K. Fuke, H. Guzik, T.G. Hams, T. Hareyama, M. Hasebe, N. Hibino, K. Ichimura, M. Ioka, K. Ishizaki, W. Israel, M.H. Javaid, A. Kasahara, K. Kataoka, J. Kataoka, R. Katayose, Y. Kato, C. Kawanaka, N. Kawakubo, Y. Kitamura, H. Krawczynski, H.S. Krizmanic, J.F. Kuramata, S. Lomtadze, T. Maestro, P. Marrocchesi, P.S. Messineo, A.M. Mitchell, J.W. Miyake, S. Mizutani, K. Moiseev, A.A. Mori, K. Mori, M. Mori, N. Motz, H.M. Munakata, K. Murakami, H. Nakagawa, Y.E. Nakahira, S. Nishimura, J. Okuno, S. Ormes, J.F. Ozawa, S. Pacini, L. Palma, F. Papini, P. Penacchioni, A.V. Rauch, B.F. Ricciarini, S. Sakai, K. Sakamoto, T. Sasaki, M. Shimizu, Y. Shiomi, A. Sparvoli, R. Spillantini, P. Stolzi, F. Takahashi, I. Takayanagi, M. Takita, M. Tamura, T. Tateyama, N. Terasawa, T. Tomida, H. Tsunesada, Y. Uchihori, Y. Ueno, S. Vannuccini, E. Wefel, J.P. Yamaoka, K. Yanagita, S. Yoshida, A. Yoshida, K. Yuda, T. Research Institute for Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan JEM Mission Operations and Integration Center Human Spaceflight Technology Directorate Japan Aerospace Exploration Agency 2-1-1 Sengen Tsukuba Ibaraki305-8505 Japan Universities Space Research Association 7178 Columbia Gateway Drive ColumbiaMD21046 United States CRESST Astroparticle Physics Laboratory NASA GSFC GreenbeltMD20771 United States School of Advanced Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan University of Florence Via Sansone 1 Sesto Fiorentino50019 Italy Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa Chiba277-8582 Japan University of Siena Rettorato via Banchi di Sotto 55 Siena53100 Italy Department of Physics Washington University One Brookings Drive St. LouisMO63130-4899 United States Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton RougeLA70803 United States Department of Physics and Astronomy University of Padova Via Marzolo 8 Padova35131 Italy University of Rome Tor Vergata Via della Ricerca Scientifica 1 Rome00133 Italy Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo Sagamihara Kanagawa252-5210 Japan Department of Physics University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States St. Marianna University School of Medicine 2-16-1 Sugao Miyamae-ku Kawasaki Kanagawa216-8511 Japan Kanagawa University 3-27-1 Rokkakubashi Kanagawa Yokohama Kanagawa221-8686 Japan Faculty of Science and Technology Graduate School of Science and Technology Hirosaki University 3 Bunkyo Hirosaki Aomori036-8561 Japan Yukawa Institute for Theoretical Physics Kyoto University Kitashirakawa Oiwakecho Sakyo Kyoto606-8502 Japan National Institute of Polar Research 10-3 Midori-cho Tachikawa Tokyo190-8518 Japan Facu

In August 2015, the CALorimetric Electron Telescope (CALET), designed for long exposure observations of high energy cosmic rays, docked with the International Space Station (ISS) and shortly thereafter began to collect data. CALET will measure the cosmic ray electron spectrum over the energy range of 1 GeV to 20 TeV with a very high resolution of 2% above 100 GeV, based on a dedicated instrument incorporating an exceptionally thick 30 radiationlength calorimeter with both total absorption and imaging (TASC and IMC) units. Each TASC readout channel must be carefully calibrated over the extremely wide dynamic range of CALET that spans six orders of magnitude in order to obtain a degree of calibration accuracy matching the resolution of energy measurements. These calibrations consist of calculating the conversion factors between ADC units and energy deposits, ensuring linearity over each gain range, and providing a seamless transition between neighboring gain ranges. This paper describes these calibration methods in detail, along with the resulting data and associated accuracies. The results presented in this paper show that a sufficient accuracy was achieved for the calibrations of each channel in order to obtain a suitable resolution over the entire dynamic range of the electron spectrum measurement. Copyright © 2017, The Authors. All rights reserved.

关键词： Calorimeters

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