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Overview of KAGRA : Data transfer and management

Progress of Theoretical and Experimental Physics 2023年第10期2023卷

作者： Akutsu, T Ando, M Arai, K Arai, Y Araki, S Araya, A Aritomi, N Asada, H Aso, Y Bae, S Bae, Y Baiotti, L Bajpai, R Barton, M A Cannon, K Cao, Z Capocasa, E Chan, M Chen, C Chen, K Chen, Y Chiang, C-Y Chu, H Chu, Y-K Eguchi, S Enomoto, Y Flaminio, R Fujii, Y Fujikawa, Y Fukunaga, M Fukushima, M Gao, D Ge, G Ha, S Hagiwara, A Haino, S Han, W-B Hasegawa, K Hattori, K Hayakawa, H Hayama, K Himemoto, Y Hiranuma, Y Hirata, N Hirose, E Hong, Z Hsieh, B Huang, G-Z Huang, H-Y Huang, P Huang, Y-C Huang, Y Hui, D C Y Ide, S Ikenoue, B Imam, S Inayoshi, K Inoue, Y Ioka, K Ito, K Itoh, Y Izumi, K Jeon, C Jin, H-B Jung, K Jung, P Kaihotsu, K Kajita, T Kakizaki, M Kamiizumi, M Kanda, N Kang, G Kawaguchi, K Kawai, N Kawasaki, T Kim, C Kim, J Kim, J C Kim, W S Kim, Y-M Kimura, N Kita, N Kitazawa, H Kobayashi, Y Kojima, Y Kokeyama, K Komori, K Kong, A K H Kotake, K Kozakai, C Kozu, R Kumar, R Kume, J Kuo, C Kuo, H-S Kuromiya, Y Kuroyanagi, S Kusayanagi, K Kwak, K Lee, H K Lee, H W Lee, R Leonardi, M Li, K L Lin, L C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H L Liu, G C Luo, L-W Majorana, E Marchio, M Michimura, Y Mio, N Miyakawa, O Miyamoto, A Miyazaki, Y Miyo, K Miyoki, S Mori, Y Morisaki, S Moriwaki, Y Nagano, K Nagano, S Nakamura, K Nakano, H Nakano, M Nakashima, R Nakayama, Y Narikawa, T Naticchioni, L Negishi, R Quynh, L Nguyen Ni, W-T Nishizawa, A Nozaki, S Obuchi, Y Ogaki, W Oh, J J Oh, K Oh, S H Ohashi, M Ohashi, T Ohishi, N Ohkawa, M Ohta, H Okutani, Y Okutomi, K Oohara, K Ooi, C Oshino, S Otabe, S Pan, K Pang, H Parisi, A Park, J Arellano, F E Peña Pinto, I Sago, N Saito, S Saito, Y Sakai, K Sakai, Y Sakuno, Y Sasaki, Y Sato, S Sato, T Sawada, T Sekiguchi, T Sekiguchi, Y Shao, L Shibagaki, S Shimizu, R Shimoda, T Shimode, K Shinkai, H Shishido, T Shoda, A Somiya, K Son, E J Sotani, H Sugimoto, R Suresh, J Suzuki, T Tagoshi, H Takahashi, H Takahashi, R Takamori, A Takano, S Takeda, H Takeda, M Tanaka, H Tanaka, K Tanaka, T Tanioka, S Martin, E N Tapia San Telada, S Tomaru, T Tomigami, Y Tomura, T Travasso, F Trozzo, L Tsang, T Ts Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe (RESCEU) The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research (ICRR) KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Hirosaki University 3 Bunkyo-cho Hirosaki City Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan (NAOJ) 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information (KISTI) 245 Daehak-ro Yuseong-gu Daejeon 34141 Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University No. 19 Xinjiekou Street Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Rd. Danshui Dist. New Taipei City 25137 Taiwan Department of Physics and Institute of Astronomy Nation

KAGRA is a newly built gravitational wave observatory, a laser interferometer with a 3 km arm length, located in Kamioka, Gifu prefecture, Japan. In this article, we describe the KAGRA data management system, i.e., recording of data, transfer from the KAGRA experiment site to computing resources, as well as data distribution to tier sites, including international sites in Taiwan and Korea. The amount of KAGRA data exceeded 1.0 PiB and increased by about 1.5 TB per day during operation in 2020. Our system has succeeded in data management, and has achieved performance that can withstand observations after 2023, that is, a transfer rate of 20 MB s-1or more and file storage of sufficient capacity for petabyte class. We also discuss the sharing of data between the global gravitational-wave detector network with other experiments, namely LIGO and Virgo. The latency, which consists of calculation of calibrated strain data and transfer time within the global network, is very important from the view of multi-messenger astronomy using gravitational waves. Real-time calbrated data delivered from the KAGRA detector site and other detectors to our computing system arrive with about 4–15 seconds of latency. These latencies are sufficiently short compared to the time taken for gravitational wave event search computations. We also established a high-latency exchange of offline calibrated data that was aggregated with a better accuracy compared with real-time data.

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Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

引用

arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Author Correction: The power of genetic diversity in genome-wide association studies of lipids

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Nature 2023年第7965期618卷 E19-E20页

作者： Sarah E Graham Shoa L Clarke Kuan-Han H Wu Stavroula Kanoni Greg J M Zajac Shweta Ramdas Ida Surakka Ioanna Ntalla Sailaja Vedantam Thomas W Winkler Adam E Locke Eirini Marouli Mi Yeong Hwang Sohee Han Akira Narita Ananyo Choudhury Amy R Bentley Kenneth Ekoru Anurag Verma Bhavi Trivedi Hilary C Martin Karen A Hunt Qin Hui Derek Klarin Xiang Zhu Gudmar Thorleifsson Anna Helgadottir Daniel F Gudbjartsson Hilma Holm Isleifur Olafsson Masato Akiyama Saori Sakaue Chikashi Terao Masahiro Kanai Wei Zhou Ben M Brumpton Humaira Rasheed Sanni E Ruotsalainen Aki S Havulinna Yogasudha Veturi QiPing Feng Elisabeth A Rosenthal Todd Lingren Jennifer Allen Pacheco Sarah A Pendergrass Jeffrey Haessler Franco Giulianini Yuki Bradford Jason E Miller Archie Campbell Kuang Lin Iona Y Millwood George Hindy Asif Rasheed Jessica D Faul Wei Zhao David R Weir Constance Turman Hongyan Huang Mariaelisa Graff Anubha Mahajan Michael R Brown Weihua Zhang Ketian Yu Ellen M Schmidt Anita Pandit Stefan Gustafsson Xianyong Yin Jian'an Luan Jing-Hua Zhao Fumihiko Matsuda Hye-Mi Jang Kyungheon Yoon Carolina Medina-Gomez Achilleas Pitsillides Jouke Jan Hottenga Gonneke Willemsen Andrew R Wood Yingji Ji Zishan Gao Simon Haworth Ruth E Mitchell Jin Fang Chai Mette Aadahl Jie Yao Ani Manichaikul Helen R Warren Julia Ramirez Jette Bork-Jensen Line L Kårhus Anuj Goel Maria Sabater-Lleal Raymond Noordam Carlo Sidore Edoardo Fiorillo Aaron F McDaid Pedro Marques-Vidal Matthias Wielscher Stella Trompet Naveed Sattar Line T Møllehave Betina H Thuesen Matthias Munz Lingyao Zeng Jianfeng Huang Bin Yang Alaitz Poveda Azra Kurbasic Claudia Lamina Lukas Forer Markus Scholz Tessel E Galesloot Jonathan P Bradfield E Warwick Daw Joseph M Zmuda Jonathan S Mitchell Christian Fuchsberger Henry Christensen Jennifer A Brody Mary F Feitosa Mary K Wojczynski Michael Preuss Massimo Mangino Paraskevi Christofidou Niek Verweij Jan W Benjamins Jorgen Engmann Rachel L Kember Roderick C Slieker Ken Sin Lo Nuno R Zilhao Phuong Le Marcus E Kleber Graciela E Delgado Shaofeng Huo Dai Department of Internal Medicine Division of Cardiology University of Michigan Ann Arbor MI USA. VA Palo Alto Health Care System Palo Alto CA USA. Department of Medicine Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Department of Biostatistics and Center for Statistics Genetics University of Michigan Ann Arbor MI USA. Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Clinical Pharmacology William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK. Endocrinology Boston Children's Hospital Boston MA USA. Medical and Population Genetics Broad Institute Cambridge MA USA. Department of Genetic Epidemiology University of Regensburg Regensburg Germany. McDonnell Genome Institute and Department of Medicine Washington University St Louis MO USA. Department of Precision Medicine Division of Genome Science National Institute of Health Cheongju-si South Korea. Tohoku Medical Megabank Organization Tohoku University Sendai Japan. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa. Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA. Blizard Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Wellcome Sanger Institute Hinxton UK. Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA. Atlanta VA Health Care System Decatur GA USA. Malcolm Randall VA Medical Center Gainesville FL USA. Division of Vascular Sur

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Correction to: Early-life exposure to PM2.5 and risk of acute asthma clinical encounters among children in Massachusetts: a case-crossover analysis

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Environmental health : a global access science source 2018年第1期17卷 25页

作者： Roxana Khalili Scott M Bartell Xuefei Hu Yang Liu Howard H Chang Candice Belanoff Matthew J Strickland Verónica M Vieira Environmental Health Sciences Graduate Program Susan and Henry Samueli College of Health Sciences University of California Irvine CA USA. Program in Public Health Susan and Henry Samueli College of Health Sciences University of California Irvine 653 E. Peltason Dr. AIRB 2042 Irvine CA 92697-3957 USA. Department of Statistics Donald Bren School of Information and Computer Sciences University of California Irvine CA USA. Department of Epidemiology School of Medicine Susan and Henry Samueli College of Health Sciences University of California Irvine CA USA. Department of Environmental Health Emory University Rollins School of Public Health Atlanta GA USA. Department of Community Health Sciences Boston University School of Public Health Boston MA USA. School of Community Health Sciences University of Nevada Reno NV USA. Environmental Health Sciences Graduate Program Susan and Henry Samueli College of Health Sciences University of California Irvine CA USA. vvieira@uci.edu. Program in Public Health Susan and Henry Samueli College of Health Sciences University of California Irvine 653 E. Peltason Dr. AIRB 2042 Irvine CA 92697-3957 USA. vvieira@uci.edu.

After publication of the article [1], it was brought to our attention that a number in Table 1 is incorrect.

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The importance of transparency and reproducibility in artificial intelligence research

arXiv

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arXiv 2020年

作者： Haibe-Kains, Benjamin Adam, George Alexandru Hosny, Ahmed Khodakarami, Farnoosh Waldron, Levi Wang, Bo McIntosh, Chris Kundaje, Anshul Greene, Casey S. Hoffman, Michael M. Leek, Jeffrey T. Huber, Wolfgang Brazma, Alvis Pineau, Joelle Tibshirani, Robert Hastie, Trevor Ioannidis, John P.A. Quackenbush, John Aerts, Hugo J.W.L. Princess Margaret Cancer Centre University Health Network TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Ontario Institute for Cancer Research TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Program Brigham and Women's Hospital Harvard Medical School BostonMA United States Radiation Oncology and Radiology Dana-Farber Cancer Institute Brigham and Women's Department of Epidemiology and Biostatistics Institute for Implementation Science in Population Health CUNY Graduate School of Public Health and Health Policy New YorkNY United States Peter Munk Cardiac Centre University Health Network TorontoON Canada Hospital Harvard Medical School BostonMA United States Department of Genetics Stanford University School of Medicine StanfordCA United States Dept. of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Childhood Cancer Data Lab Alex's Lemonade Stand Foundation PhiladelphiaPA United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute EMBL-EBI Hinxton United Kingdom McGill University MontrealQC Canada Montreal Institute for Learning Algorithms QC Canada StanfordCA United States Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Departments of Medicine of Health Research and Policy and of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Department of Statistics Stanford University School of Humanities and Sciences StanfordCA United States Department of Biostatistics Harvard T.H Chan School of Public Health BostonMA Un

In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines its scientific value. We identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al and provide solutions with implications for the broader field. Copyright © 2020, The Authors. All rights reserved.

关键词： Artificial intelligence

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Search results side note: Creating information search context in creative design process 11

Search results side note: Creating information search contex...

引用

11th International Conference on Knowledge, Information and Creativity Support Systems, KICSS 2016

作者： Jambak, Muhammad Ikhwan Yusof, Shafiz Affendi Bin Mohd Binus Graduate Program Computer Science Bina Nusantara University Jl. Kebon Jeruk Raya No. 27 Jakarta Barat11530 Indonesia Faculty of Engineering and Information Sciences University of Wollongong in Dubai Knowledge Village Dubai United Arab Emirates

ISBN: (纸本)9781509051304

Information involves in design process, and in fact designing is an activity of converting data, information and knowledge into a map and guidance of creating an artifact. Unfortunately, the existing instruments that supporting designers' information search do not fit to them in term of their naturally way of doing so. This research is aimed at developing an information search application purposely for designers especially in the creative design process. And this paper will focus on the development and implementation a so called Search Result Side Note application. © 2016 IEEE.

关键词： Information retrieval

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HFLTS-TOPSIS With Pseudo-distance in Determining The Best Lecturers

引用

Journal of Physics: Conference Series 2019年第1期1363卷

作者： Dahlan Abdullah H Hartono Kundharu Saddhono Nuning Kurniasih Akbar Iskandar Dian Rianita Purwarno Rianti Setyawasih Erwinsyah Satria Tato Nuryanto Lilik Herawati Sujinah I Ketut Sudarsana Department of Informatics Universitas Malikussaleh Aceh Utara Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Department of Public Administrations Universitas Lancang Kuning Indonesia Faculty of Literature Universitas Islam Sumatera Utara Medan Indonesia Department of Management Faculty of Economic Universitas Islam 45 Bekasi Indonesia Faculty of Teacher Training and Education Universitas Bung Hatta Padang Indonesia FITK IAIN Syekh Nurjati Cirebon Indonesia Department of Indonesian Language Teaching Institut Agama Islam Negeri Syekh Nurjati Cirebon Cirebon Indonesia Faculty of Education University Muhammadiyah of Surabaya Surabaya Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Indonesia

Decision making is sometimes faced with a situation where assessment is given in the form of linguistic variables, which have a certain range of values so that decision making cannot be given in the form of numerical values. One approach commonly used is Hesitant Fuzzy Linguistic Term Sets (HFLTS) which is a multi-criteria and multi-alternative decision making method. In TOPSIS which has the concept of distance measurement, basically the chosen one is the best alternative is an alternative that has the closest distance to a positive solution and the farthest distance with a negative solution. The incorporation of HFLTS-TOPSIS has the advantage that decision making can be done in the form of variable linguistics and also a situation where there are benefit criteria and cost criteria. This study will use the HFLTS-TOPSIS approach that has been modified in the distance calculation section so that distance calculation is based on the difference of preference degree between the two HFLTS. The results of research conducted by a number of researchers show that using pseudo-distance will provide advantages compared to the standard HLFTS-TOPSIS. Pseudo-distance HFLTS-TOPSIS will be applied in the selection of the best lecturers. The criteria used in this study are: Student Satisfaction, Research, Number of late, and number of complaints from students.

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FRAMR-EMR: Framework for prognostic predictive model development using electronic medical record data with a case study in osteoarthritis risk

arXiv

引用

arXiv 2017年

作者： Black, Jason E. Terry, Amanda L. Lizotte, Daniel J. Graduate Program in Epidemiology and Biostatistics Western University 1151 Richmond Street LondonONN6A 5C1 Canada Department of Family Medicine Department of Epidemiology and Biostatistics Schulich Interfaculty Program in Public Health Western University 1151 Richmond Street LondonONN6A 3K7 Canada Department of Computer Science Department of Epidemiology and Biostatistics Schulich Interfaculty Program in Public Health Department of Statistical and Actuarial Sciences Western University LondonONN6A 3K7 Canada

Background: Prognostic predictive models are used in the delivery of primary care to estimate a patient's risk of future disease development. Electronic medical record (EMR) data can be used for the construction of these models. Objectives: To provide a framework for those seeking to develop prognostic predictive models using EMR data, and to illustrate these steps using osteoarthritis risk estimation as an example. Methods (FRAMR-EMR): The FRAmework for Modelling Risk from EMR data (FRAMR-EMR) was created, which outlines step-by-step guidance for the construction of a prognostic predictive model using EMR data. Our framework is composed of four main steps: Scoping and Background Search;Data Preparation;Cohort Construction;and Model Building and Evaluation. Throughout these steps, several potential pitfalls specific to using EMR data for predictive purposes are described and methods for addressing them are suggested. Results (Case Study): We used the DELPHI (DELiver Primary Healthcare Information) database to develop a prognostic predictive model for estimation of osteoarthritis risk. We constructed a retrospective cohort of 28,447 eligible primary care patients. Patients were included in the cohort if they had an encounter with their primary care practitioner between 1 January 2008 and 31 December 2009;additionally, patients were required to have an encounter following the follow-up window to ensure patients were not lost to follow-up. Patients were excluded if they had a diagnosis of osteoarthritis prior to baseline. Construction of a prognostic predictive model following FRAMR-EMR yielded a predictive model capable of estimating 5-year risk of osteoarthritis diagnosis. Logistic regression was used to predict osteoarthritis based on age, sex, BMI, previous leg injury, and osteoporosis. Internal validation of the model's performance demonstrated good discrimination and moderate calibration. Conclusions: This study provides guidance to those interested in developing

关键词： Medical informatics

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Signatures of Electronic Nematicity In (111) Laalo3/Srtio3 Interfaces

arXiv

引用

arXiv 2017年

作者： Davis, S. Huang, Z. Han, K. Ariando Venkatesan, T. Chandrasekhar, V. Graduate Program in Applied Physics Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208 United States NUSNNI-Nanocore National University of Singapore 117411 Singapore Department of Physics National University of Singapore 117551 Singapore NUS Graduate School for Integrative Sciences & Engineering National University of Singapore 117456 Singapore Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore Department of Material Science and Engineering National University of Singapore 117575 Singapore

Symmetry breaking is a fundamental concept in condensed matter physics whose presence often heralds new phases of matter. For instance, the breaking of time reversal symmetry is traditionally linked to magnetic phases in a material, while the breaking of gauge symmetry can lead to superfluidity/superconductivity. Nematic phases are phases in which rotational symmetry is broken while maintaining translational symmetry, and are traditionally associated with liquid crystals. Electronic nematic states where the orthogonal in-plane crystal directions have different electronic properties have garnered a great deal of attention after their discovery in Sr3Ru2O7,1 multiple iron based superconductors,2,3 and in the superconducting state of CuBiSe.4,5 Here we demonstrate the existence of an electronic nematic phase in the two-dimensional carrier gas that forms at the (111) LaAlO3 (LAO)/SrTiO3 (STO) interface that onsets at low temperatures, and is tunable by an electric field. Copyright © 2017, The Authors. All rights reserved.

关键词： Electronic properties

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Magnetoresistance in the superconducting state at the (111) LaAlO3/SrTiO3 interface

arXiv

引用

arXiv 2017年

Condensed matter systems that simultaneously exhibit superconductivity and ferromagnetism are rare due the antagonistic relationship between conventional spin-singlet superconductivity and ferromagnetic order. In materials in which superconductivity and magnetic order is known to coexist (such as some heavy-fermion materials), the superconductivity is thought to be of an unconventional nature. Recently, the conducting gas that lives at the interface between the perovskite band insulators LaAlO3 (LAO) and SrTiO3 (STO) has also been shown to host both superconductivity and magnetism. Most previous research has focused on LAO/STO samples in which the interface is in the (001) crystal plane. Relatively little work has focused on the (111) crystal orientation, which has hexagonal symmetry at the interface, and has been predicted to have potentially interesting topological properties, including unconventional superconducting pairing states. Here we report measurements of the magnetoresistance of (111) LAO/STO heterostructures at temperatures at which they are also superconducting. As with the (001) structures, the magnetoresistance is hysteretic, indicating the coexistence of magnetism and superconductivity, but in addition, we find that this magnetoresistance is anisotropic. Such an anisotropic response is completely unexpected in the superconducting state, and suggests that (111) LAO/STO heterostructures may support unconventional superconductivity. Copyright © 2017, The Authors. All rights reserved.

关键词： Strontium titanates

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