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arXiv 2017年

作者： Pablo-Pedro, Ricardo Lopez-Rios, Hector Mendoza-Cortes, Jose-L Kong, Jing Fomine, Serguei van Voorhis, Troy Dresselhaus, Mildred S. Department of Chemistry Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Instituto de Investigaciones en Materiales Universidad Nacional Autónoma de México Apartado Postal 70-360 CU Coyoacán Ciudad de México04510 Mexico Department of Chemical & Biomedical Engineering Florida A&M University Florida State University Joint College of Engineering Tallahassee FL32310 United States Scientific Computing Department Materials Science and Engineering Program High Performance Material Institute Condensed Matter Theory National High Magnetic Field Laboratory Florida State University TallahasseeFL32310 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

High-performance materials rely on small reorganization energies to facilitate both charge separation and charge transport. Here, we performed DFT calculations to predict small reorganization energies of rectangular silicene nanoclusters with hydrogen-passivated edges denoted by H-SiNC. We observe that across all geometries, H-SiNCs feature large electron affinities and highly stabilized anionic states, indicating their potential as n-type materials. Our findings suggest that fine-tuning the size of H-SiNCs along the "zigzag" and "armchair" directions may permit the design of novel n-type electronic materials and spinctronics devices that incorporate both high electron affinities and very low internal reorganization energies. Copyright © 2017, The Authors. All rights reserved.

关键词： Silicene

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Exploring Low Internal Reorganization Energies for Silicene Nanoclusters

arXiv

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arXiv 2017年

关键词： Silicene

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Thread-aware adaptive prefetcher on multicore systems: Improving the performance for multithreaded workloads

Thread-aware adaptive prefetcher on multicore systems: Impro...

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作者： Liu, Peng Yu, Jiyang Huang, Michael C. College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi214125 China Department of Shannon Lab. Huawei Technologies Co. Ltd. Hangzhou310051 China Department of Electrical and Computer Engineering University of Rochester RochesterNY14627-0231 United States

Most processors employ hardware data prefetching techniques to hide memory access latencies. However, the prefetching requests from different threads on a multicore processor can cause severe interference with prefetching and/or demand requests of others. The data prefetching can lead to significant performance degradation due to shared resource contention on shared memory multicore systems. This article proposes a thread-aware data prefetching mechanism based on low-overhead runtime information to tune prefetching modes and aggressiveness, mitigating the resource contention in the memory system. Our solution has three new components: (1) a self-tuning prefetcher that uses runtime feedback to dynamically adjust data prefetching modes and arguments of each thread, (2) a filtering mechanism that informs the hardware about which prefetching request can cause shared data invalidation and should be discarded, and (3) a limiter thread acceleration mechanism to estimate and accelerate the critical thread which has the longest completion time in the parallel region of execution. On a set of multithreaded parallel benchmarks, our thread-aware data prefetchingmechanism improves the overall performance of 64-core system by 13% over a multimode prefetch baseline system with two-level cache organization and conventional modified, exclusive, shared, and invalid-based directory coherence *** compare our approach with the feedback directed prefetching technique and find that it provides 9% performance improvement on multicore systems, while saving the memory bandwidth consumption.

关键词： Benchmarking

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Extended multi-structure local binary pattern for high-resolution image scene classification

Extended multi-structure local binary pattern for high-resol...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xiaoyong Bian Chen Chen Qian Du Yuxia Sheng Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Department of Electrical Engineering University of Texas at Dallas Richardson TX USA Department of Electrical and Computer Engineering Mississippi State University Mississippi State USA School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

This paper presents a novel extended multi-structure local binary pattern (EMSLBP) approach for high-resolution image classification, generalizing the well-known local binary pattern (LBP) approach. In the proposed EMSLBP approach, three-coupled descriptors with multi-structure sampling are proposed to extract complementary features (pixel value and radial difference) from local image patches. The anisotropic features derived from elliptical sampling are also rotation invariant by averaging the histograms over rotational angles and combined with the isotropic features extracted from circular sampling. Experimental results show that the proposed method can effectively capture local spatial pattern and local contrast, consistently outperforming several state-of-the-art classification algorithms.

关键词： Feature extraction Histograms Nickel Image classification Support vector machines Training Image resolution

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Optimal tuning of governors on synchronous generators in a multi-area power system with a large photovoltaic plant

Optimal tuning of governors on synchronous generators in a m...

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IEEE Power engineering Society Conference and Exposition in Africa, PowerAfrica

作者： Steven Rosen Paranietharan Arunagirinathan Iroshani Jayawardene Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University Clemson SC USA Eskom Centre of Excellence in HVDC Engineering University of KwaZulu-Natal Durban South Africa

The integration of renewables, such as solar and wind power, introduces new challenges for existing multi-area power system operations. These variable generation sources are transforming the grid into a more dynamic system which significantly affects real-time operation. With dynamic generation, supply and demand will deviate frequently over time causing severe frequency fluctuations. The generators on the grid must respond rapidly to these fluctuations in order to stabilize the system and ultimately return to nominal frequency. In this paper, a 210 MW photovoltaic (PV) plant is integrated into a two-area four-machine power system. Due to the output variations of the PV plant, especially during cloudy conditions, the system frequency will significantly fluctuate. The objective of this paper is the optimal tuning of governors on synchronous generators in order to damp and mitigate frequency deviations as fast as possible. The governor parameters are tuned using a heuristic optimization method to provide the least number of frequency events and minimum frequency deviation with varying penetration levels of PV power. With optimally tuned governor parameters, a multi-area power system can operate reliably while maximizing the utilization of clean power generation sources.

关键词： Generators Tuning Power system stability time-frequency analysis real-time systems Automatic generation control

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Performance Analysis of GPU-Based Convolutional Neural Networks

Performance Analysis of GPU-Based Convolutional Neural Netwo...

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International Conference on Parallel Processing (ICPP)

作者： Xiaqing Li Guangyan Zhang H. Howie Huang Zhufan Wang Weimin Zheng Tsinghua National Laboratory for Information Science and Technology State Key Lab of Mathematical Engineering and Advanced Computing Wuxi China Department of Computer Science and Technology Tsinghua University Department of Electrical and Computer Engineering George Washington University

ISBN: (纸本)9781509028245

As one of the most important deep learning models, convolutional neural networks (CNNs) have achieved great successes in a number of applications such as image classification, speech recognition and nature language understanding. Training CNNs on large data sets is computationally expensive, leading to a flurry of research and development of open-source parallel implementations on GPUs. However, few studies have been performed to evaluate the performance characteristics of those implementations. In this paper, we conduct a comprehensive comparison of these implementations over a wide range of parameter configurations, investigate potential performance bottlenecks and point out a number of opportunities for further optimization.

关键词： Convolution Graphics processing units Runtime Training Computational modeling computer architecture Kernel

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Erratum to: Search for supersymmetry in final states with two same-sign or three leptons and jets using 36 fb−1 of = 13 TeV pp collision data with the ATLAS detector

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Journal of High Energy Physics 2019年第8期2019卷 1-19页

作者： Aaboud, M. Aad, G. Abbott, B. Abdinov, O. Abeloos, B. Abidi, S. H. AbouZeid, O. S. Abraham, N. L. Abramowicz, H. Abreu, H. Abreu, R. Abulaiti, Y. Acharya, B. S. Adachi, S. Adamczyk, L. Adelman, J. Adersberger, M. Adye, T. Affolder, A. A. Afik, Y. Agatonovic-Jovin, T. Agheorghiesei, C. Aguilar-Saavedra, J. A. Ahlen, S. P. Ahmadov, F. Aielli, G. Akatsuka, S. Akerstedt, H. Åkesson, T. P. A. Akilli, E. Akimov, A. V. Alberghi, G. L. Albert, J. Albicocco, P. Alconada Verzini, M. J. Alderweireldt, S. C. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexander, G. Alexopoulos, T. Alhroob, M. Ali, B. Aliev, M. Alimonti, G. Alison, J. Alkire, S. P. Allbrooke, B. M. M. Allen, B. W. Allport, P. P. Aloisio, A. Alonso, A. Alonso, F. Alpigiani, C. Alshehri, A. A. Alstaty, M. I. Alvarez Gonzalez, B. Álvarez Piqueras, D. Alviggi, M. G. Amadio, B. T. Amaral Coutinho, Y. Amelung, C. Amidei, D. Amor Dos Santos, S. P. Amoroso, S. Amundsen, G. Anastopoulos, C. Ancu, L. S. Andari, N. Andeen, T. Anders, C. F. Anders, J. K. Anderson, K. J. Andreazza, A. Andrei, V. Angelidakis, S. Angelozzi, I. Angerami, A. Anisenkov, A. V. Anjos, N. Annovi, A. Antel, C. Antonelli, M. Antonov, A. Antrim, D. J. Anulli, F. Aoki, M. Aperio Bella, L. Arabidze, G. Arai, Y. Araque, J. P. Araujo Ferraz, V. Arce, A. T. H. Ardell, R. E. Arduh, F. A. Arguin, J-F. Argyropoulos, S. Arik, M. Armbruster, A. J. Armitage, L. J. Arnaez, O. Arnold, H. Arratia, M. Arslan, O. Artamonov, A. Artoni, G. Artz, S. Asai, S. Asbah, N. Ashkenazi, A. Asquith, L. Assamagan, K. Astalos, R. Atkinson, M. Atlay, N. B. Augsten, K. Avolio, G. Axen, B. Ayoub, M. K. Azuelos, G. Baas, A. E. Baca, M. J. Bachacou, H. Bachas, K. Backes, M. Bagnaia, P. Bahmani, M. Bahrasemani, H. Baines, J. T. Bajic, M. Baker, O. K. Baldin, E. M. Balek, P. Balli, F. Balunas, W. K. Banas, E. Bandyopadhyay, A. Banerjee, Sw. Bannoura, A. A. E. Barak, L. Barberio, E. L. Barberis, D. Barbero, M. Barillari, T. Barisits, M-S Barkeloo, J. T. Barklow, T. Barlow, N. Barnes, S. L. Barnett, B. M. Barnett, R. M. Barnovska-Blenessy, Z Faculté des Sciences Université Mohamed Premier and LPTPM Oujda Morocco CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman U.S.A. Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan LAL Univ. Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France Department of Physics University of Toronto Toronto Canada Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz U.S.A. Department of Physics and Astronomy University of Sussex Brighton United Kingdom Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion: Israel Institute of Technology Haifa Israel Center for High Energy Physics University of Oregon Eugene U.S.A. Department of Physics Stockholm University Stockholm Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Udine Italy ICTP Trieste Italy Department of Physics King’s College London London United Kingdom International Center for Elementary Particle Physics and Department of Physics The University of Tokyo Tokyo Japan AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics Northern Illinois University DeKalb U.S.A. Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Institute of Physics University of Belgrade Belgrade Serbia Department of Physics Alexandru Ioan Cuza University of Iasi Iasi Romania Laboratório de Instrumentação e Física Experimental de Partículas – LIP Lisboa Portugal Departamento de Fisica Teorica y del Cosmos Universidad de Granada Granada Portugal Department of Physics Boston University Boston U.S.A. Joint Institute for Nuclear Research JINR Dubna Dubna Russia INFN Sezione di Roma Tor Vergata Roma Italy Dip

One correction is made to the figure 4e of the paper.

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A pulsar-based timescale from the International Pulsar Timing Array

arXiv

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arXiv 2019年

作者： Hobbs, G. Guo, L. Caballero, R.N. Coles, W. Lee, K.J. Manchester, R.N. Reardon, D.J. Matsakis, D. Tong, M.L. Arzoumanian, Z. Bailes, M. Bassa, C.G. Bhat, N.D.R. Brazier, A. Burke-Spolaor, S. Champion, D.J. Chatterjee, S. Cognard, I. Dai, S. Desvignes, G. Dolch, T. Ferdman, R.D. Graikou, E. Guillemot, L. Janssen, G.H. Keith, M.J. Kerr, M. Kramer, M. Lam, M.T. Liu, K. Lyne, A. Lazio, T.J.W. Lynch, R. McKee, J.W. McLaughlin, M.A. Mingarelli, C.M.F. Nice, D.J. Oslowski, S. Pennucci, T.T. Perera, B.B.P. Perrodin, D. Possenti, A. Russell, C.J. Sanidas, S. Sesana, A. Shaifullah, G. Shannon, R.M. Simon, J. Spiewak, R. Stairs, I.H. Stappers, B.W. Swiggum, J.K. Taylor, S.R. Theureau, G. Toomey, L. van Haasteren, R. Wang, J.B. Wang, Y. Zhu, X.J. U.S. Naval Observatory WashingtonD.C. United States CSIRO Astronomy and Space Science Australia Telescope National Facility Box 76 EppingNSW1710 Australia Shanghai Astronomical Observatory CAS Shanghai200030 China Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Kavli institute for Astronomy and Astrophysics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California at San Diego La JollaCA92093 United States Centre for Astrophysics and Supercomputing Swinburne University of Technology PO Box 218 HawthornVIC3122 Australia National Time Service Center CAS Xi’anShaanxi710600 China Astrophysics Science Division NASA Goddard Space Flight Center GreenbeltMD20771 United States ASTRON Netherlands Institute for Radio Astronomy Oude Hoogeveensedijk 4 PD Dwingeloo7991 Netherlands International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Cornell Center for Advanced Computing Cornell University IthacaNY14853 United States Department of Physics and Astronomy West Virginia University MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Laboratoire de Physique et Chimie de l’Environnement et de l’Espace LPC2E CNRS- Université d’Orléans OrléansF-45071 France Station de radioastronomie de Nançay Observatoire de Paris PSL University CNRS/INSU NancayF-18330 France Department of Physics Hillsdale College 33 E. College Street HillsdaleMI49242 United States University of East Anglia Norwich Research Park NorwichNR4 7TJ United Kingdom Department of Astrophysics IMAPP Radboud University P.O. Box 9010 Nijmegen6500 GL Netherlands Jodrell Bank Centre for Astrophysics University of Manchester ManchesterM13 9PL Unite

We have constructed a new timescale, TT(IPTA16), based on observations of radio pulsars presented in the first data release from the International Pulsar Timing Array (IPTA). We used two analysis techniques with independent estimates of the noise models for the pulsar observations and different algorithms for obtaining the pulsar timescale. The two analyses agree within the estimated uncertainties and both agree with TT(BIPM17), a post-corrected timescale produced by the Bureau International des Poids et Mesures (BIPM). We show that both methods could detect significant errors in TT(BIPM17) if they were present. We estimate the stability of the atomic clocks from which TT(BIPM17) is derived using observations of four rubidium fountain clocks at the US Naval Observatory. Comparing the power spectrum of TT(IPTA16) with that of these fountain clocks suggests that pulsar-based timescales are unlikely to contribute to the stability of the best timescales over the next decade, but they will remain a valuable independent check on atomic timescales. We also find that the stability of the pulsar-based timescale is likely to be limited by our knowledge of solar-system dynamics, and that errors in TT(BIPM17) will not be a limiting factor for the primary goal of the IPTA, which is to search for the signatures of nano-Hertz gravitational waves. Copyright © 2019, The Authors. All rights reserved.

关键词： Pulsars

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