检索结果-内蒙古大学图书馆

5th IEEE International Conference on computational Advances in Bio and Medical sciences, ICCABS 2015

作者： Rytsareva, Inna Sims, Seth Chockalingam, Sriram P. Aluru, Srinivas Campo, David S. Tetik, Cansu Thankachan, Sharma V. Khudyakov, Yury Yueli, Zheng Chirag, Jain Sue, Amanda Molecular Epidemiology and Bioinformatics Laboratory Division of Viral Hepatitis Centers for Disease Control and Prevention Atlanta United States Dept. of Computer Science and Engineering IIT Bombay Mumbai India School of Computational Science and Engineering Georgia Institute of Technology Atlanta United States Centers for Disease Control and Prevention Atlanta United States

ISBN: (纸本)9781467396639

Hepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections associated with unsafe injection practices, drug diversion, and other exposures to blood are difficult to detect and investigate. Molecular analysis has been frequently used in the study of HCV outbreaks and transmission chains;helping identify a cluster of sequences as linked by transmission if their genetic distances are below a previously defined threshold. © 2015 IEEE.

关键词： Public health

来源：评论

学校读者我要写书评

暂无评论

Accelerated molecular mechanical and solvation energetics on multicore CPUs and manycore GPUs 15

Accelerated molecular mechanical and solvation energetics on...

引用

6th ACM Conference on Bioinformatics, computational Biology, and Health Informatics, BCB 2015

作者： Cha, Deukhyun Zhang, Qin Tithi, Jesmin Jahan Rand, Alexander Chowdhury, Rezaul A. Bajaj, Chandrajit Bluware Inc. HoustonTX United States CGG HoustonTX United States Dept. Computer Science State University of New York Stony BrookNY United States CD-adapco AustinTX United States Dept. Computer Science Institute of Computational Engineering and Sciences University of Texas at Austin AustinTX United States

ISBN: (纸本)9781450338530

Motivation. Despite several reported acceleration successes of programmable GPUs (Graphics Processing Units) for molecular modeling and simulation tools, the general focus has been on fast computation with small molecules. This was primarily due to the limited memory size on the GPU. Moreover simultaneous use of CPU and GPU cores for a single kernel execution - a necessity for achieving high parallelism - has also not been fully considered. Results. We present fast computation methods for molecular mechanical (Lennard-Jones and Coulombic) and generalized Born solvation energetics which run on commodity multicore CPUs and manycore GPUs. The key idea is to trade off accuracy of pairwise, long-range atomistic energetics for higher speed of execution. A simple yet efficient CUDA kernel for GPU acceleration is presented which ensures high arithmetic intensity and memory efficiency. Our CUDA kernel uses a cache-friendly, recursive and linearspace octree data structure to handle very large molecular structures with up to several million atoms. Based on this CUDA kernel, we present a hybrid method which simultaneously exploits both CPU and GPU cores to provide the best performance based on selected parameters of the approximation scheme. Our CUDA kernels achieve more than two orders of magnitude speedup over serial computation for many of the molecular energetics terms. The hybrid method is shown to be able to achieve the best performance for all values of the approximation parameter. Availability. The source code and binaries are freely available as PMEOPA (Parallel Molecular Energetic using Octree Pairwise Approximation) and downloadable from http://***/software. Copyright 2015 ACM.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

引用

Scientific data 2021年第1期8卷 72页

作者： Gilberto Pastorello Carlo Trotta Eleonora Canfora Housen Chu Danielle Christianson You-Wei Cheah Cristina Poindexter Jiquan Chen Abdelrahman Elbashandy Marty Humphrey Peter Isaac Diego Polidori Markus Reichstein Alessio Ribeca Catharine van Ingen Nicolas Vuichard Leiming Zhang Brian Amiro Christof Ammann M Altaf Arain Jonas Ardö Timothy Arkebauer Stefan K Arndt Nicola Arriga Marc Aubinet Mika Aurela Dennis Baldocchi Alan Barr Eric Beamesderfer Luca Belelli Marchesini Onil Bergeron Jason Beringer Christian Bernhofer Daniel Berveiller Dave Billesbach Thomas Andrew Black Peter D Blanken Gil Bohrer Julia Boike Paul V Bolstad Damien Bonal Jean-Marc Bonnefond David R Bowling Rosvel Bracho Jason Brodeur Christian Brümmer Nina Buchmann Benoit Burban Sean P Burns Pauline Buysse Peter Cale Mauro Cavagna Pierre Cellier Shiping Chen Isaac Chini Torben R Christensen James Cleverly Alessio Collalti Claudia Consalvo Bruce D Cook David Cook Carole Coursolle Edoardo Cremonese Peter S Curtis Ettore D'Andrea Humberto da Rocha Xiaoqin Dai Kenneth J Davis Bruno De Cinti Agnes de Grandcourt Anne De Ligne Raimundo C De Oliveira Nicolas Delpierre Ankur R Desai Carlos Marcelo Di Bella Paul di Tommasi Han Dolman Francisco Domingo Gang Dong Sabina Dore Pierpaolo Duce Eric Dufrêne Allison Dunn Jiří Dušek Derek Eamus Uwe Eichelmann Hatim Abdalla M ElKhidir Werner Eugster Cacilia M Ewenz Brent Ewers Daniela Famulari Silvano Fares Iris Feigenwinter Andrew Feitz Rasmus Fensholt Gianluca Filippa Marc Fischer John Frank Marta Galvagno Mana Gharun Damiano Gianelle Bert Gielen Beniamino Gioli Anatoly Gitelson Ignacio Goded Mathias Goeckede Allen H Goldstein Christopher M Gough Michael L Goulden Alexander Graf Anne Griebel Carsten Gruening Thomas Grünwald Albin Hammerle Shijie Han Xingguo Han Birger Ulf Hansen Chad Hanson Juha Hatakka Yongtao He Markus Hehn Bernard Heinesch Nina Hinko-Najera Lukas Hörtnagl Lindsay Hutley Andreas Ibrom Hiroki Ikawa Marcin Jackowicz-Korczynski Dalibor Janouš Wilma Jans Rachhpal Jassal Shicheng Jiang Tomomichi Kato Myr Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. gzpastorello@lbl.gov. DIBAF University of Tuscia Viterbo 01100 Italy. Euro-Mediterranean Centre on Climate Change Foundation (CMCC) Lecce 73100 Italy. Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Department of Civil Engineering California State University Sacramento CA 95819 USA. Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI 48823 USA. Department of Computer Science University of Virginia Charlottesville VA 22904 USA. TERN Ecosystem Processes Menzies Creek VIC3159 Australia. Max Planck Institute for Biogeochemistry Jena 07701 Germany. Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA CNRS UVSQ UPSACLAY Gif sur Yvette 91190 France. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Department of Soil Science University of Manitoba Winnipeg MB R3T2N2 Canada. Department of Agroecology and Environment Agroscope Research Institute Zürich 8046 Switzerland. School of Geography and Earth Sciences McMaster University L8S4K1 Hamilton ON Canada. Department of Physical Geography and Ecosystem Science Lund University Lund 22362 Sweden. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln NE 68583 USA. School of Ecosystem and Forest Sciences The University of Melbourne Richmond VIC3121 Australia. Department of Biology Research Group PLECO University of Antwerp Antwerp 2610 Belgium. Joint Research Centre European Commission Ispra 21027 Italy. TERRA Teaching and Research Center University of Liege Gembloux B-5030 Belgium. Finnish Meteorological Institute Helsinki 00560 Finland. ES

来源：评论

学校读者我要写书评

暂无评论

26th Annual computational Neuroscience Meeting (CNS*2017) of the Organization for computational Neuroscience Antwerp, Belgium, July 15-20, 2017

引用

BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

来源：评论

学校读者我要写书评

暂无评论

Direct evidence for shock-powered optical emission in a nova

arXiv

引用

arXiv 2020年

作者： Aydi, Elias Sokolovsky, Kirill V. Chomiuk, Laura Steinberg, Elad Li, Kwan Lok Vurm, Indrek Metzger, Brian D. Strader, Jay Mukai, Koji Pejcha, Ondřej Shen, Ken J. Wade, Gregg A. Kuschnig, Rainer Moffat, Anthony F.J. Pablo, Herbert Pigulski, Andrzej Popowicz, Adam Weiss, Werner Zwintz, Konstanze Izzo, Luca Pollard, Karen R. Handler, Gerald Ryder, Stuart D. Filipović, Miroslav D. Alsaberi, Rami Z.E. Manojlović, Perica de Oliveira, Raimundo Lopes Walter, Frederick M. Vallely, Patrick J. Buckley, David A.H. Brown, Michael J.I. Harvey, Eamonn J. Kawash, Adam Kniazev, Alexei Kochanek, Christopher S. Linford, Justin Mikolajewska, Joanna Molaro, Paolo Orio, Marina Page, Kim L. Shappee, Benjamin J. Sokoloski, Jennifer L. Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Sternberg Astronomical Institute Moscow State University Universitetskii pr. 13 Moscow119992 Russia Astro Space Center of Lebedev Physical Institute Profsoyuznaya St. 84/32 Moscow117997 Russia Columbia Astrophysics Laboratory Department of Physics Columbia University New YorkNY10027 United States Racah Institute of Physics Hebrew University Jerusalem91904 Israel Department of Physics UNIST Ulsan44919 Korea Republic of Institute of Astronomy National Tsing Hua University Hsinchu30013 Taiwan Tartu Observatory University of Tartu Tõravere Tartumaa61602 Estonia CRESST X-ray Astrophysics Laboratory NASA GSFC GreenbeltMD20771 United States Department of Physics University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Institute of Theoretical Physics Faculty of Mathematics and Physics Charles University Prague Czech Republic Department of Astronomy Theoretical Astrophysics Center University of California BerkeleyCA94720 United States Department of Physics and Space Science Royal Military College of Canada PO Box 17000 Station Forces KingstonONK7K 7B4 Canada Institute of Communication Networks and Satellite Communications Graz University of Technology Infeldgasse 12 Graz8010 Austria Dépt. de Physique Univ. De Montréal C.P. 6128 Succ. Centre-Ville Centre de Recherche en Astrophysique du Quéebec MontréealQCH3C 3J7 Canada AAVSO 49 Bay State Rd CambridgeMA02138 United States Instytut Astronomiczny Uniwersytet Wroclawski Kopernika 11 Wroclaw51-622 Poland Silesian University of Technology Institute of Electronics Akademicka 16 Gliwice44-100 Poland Institute for Astrophysics University of Vienna Tuerkenschanzstrasse 17 ViennaA-1180 Austria Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstrasse 25 InnsbruckA-6020 Austria D

Classical novae are thermonuclear explosions that occur on the surfaces of white dwarf stars in interacting binary systems1. It has long been thought that the luminosity of classical novae is powered by continued nuclear burning on the surface of the white dwarf after the initial runaway2. However, recent observations of GeV γrays from classical novae have hinted that shocks internal to the nova ejecta may dominate the nova emission. Shocks have also been suggested to power the luminosity of events as diverse as stellar mergers3, supernovae4, and tidal disruption events5, but observational confirmation has been lacking. Here we report simultaneous space-based optical and γ-ray observations of the 2018 nova V906 Carinae (ASASSN-18fv), revealing a remarkable series of distinct correlated flares in both bands. The optical and γ-ray flares occur simultaneously, implying a common origin in shocks. During the flares, the nova luminosity doubles, implying that the bulk of the luminosity is shock-powered. Furthermore, we detect concurrent but weak X-ray emission from deeply embedded shocks, confirming that the shock power does not appear in the X-ray band and supporting its emergence at longer wavelengths. Our data, spanning the spectrum from radio to γ-ray, provide direct evidence that shocks can power substantial luminosity in classical novae and other optical transients. Copyright © 2020, The Authors. All rights reserved.

关键词： Luminance

来源：评论

学校读者我要写书评

暂无评论

PennyLane: Automatic differentiation of hybrid quantum-classical computations

arXiv

引用

arXiv 2018年

作者： Bergholm, Ville Izaac, Josh Schuld, Maria Gogolin, Christian Ahmed, Shahnawaz Ajith, Vishnu Alam, M. Sohaib Alonso-Linaje, Guillermo AkashNarayanan, B. Asadi, Ali Arrazola, Juan Miguel Azad, Utkarsh Banning, Sam Blank, Carsten Bromley, Thomas R. Cordier, Benjamin A. Ceroni, Jack Delgado, Alain Matteo, Olivia Di Dusko, Amintor Garg, Tanya Guala, Diego Hayes, Anthony Hill, Ryan Ijaz, Aroosa Isacsson, Theodor Ittah, David Jahangiri, Soran Jain, Prateek Jiang, Edward Khandelwal, Ankit Kottmann, Korbinian Lang, Robert A. Lee, Christina Loke, Thomas Lowe, Angus McKiernan, Keri Meyer, Johannes Jakob Montañez-Barrera, J.A. Moyard, Romain Niu, Zeyue O’Riordan, Lee James Oud, Steven Panigrahi, Ashish Park, Chae-Yeun Polatajko, Daniel Quesada, Nicolás Roberts, Chase Sá, Nahum Schoch, Isidor Shi, Borun Shu, Shuli Sim, Sukin Singh, Arshpreet Strandberg, Ingrid Soni, Jay Száva, Antal Thabet, Slimane Vargas-Hernández, Rodrigo A. Vincent, Trevor Vitucci, Nicola Weber, Maurice Wierichs, David Wiersema, Roeland Willmann, Moritz Wong, Vincent Zhang, Shaoming Killoran, Nathan Xanadu 777 Bay Street Toronto Canada Wallenberg Centre for Quantum Technology Department of Microtechnology and Nanoscience Chalmers University of Technology Gothenburg412 96 Sweden Indian Institute of Information Technology Kottayam India NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States data cybernetics Martin-Kolmsperger-Str 26 Landsberg86899 Germany Department of Medical Informatics and Clinical Epidemiology Oregon Health and Science University PortlandOR97202 United States Dept. of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Department of Physics Indian Institute of Technology Roorkee Uttarakhand Roorkee India qBraid 5235 South Harper Court ChicagoIL60615 United States Factal Analytics Level 2 Chimes Building Plot 61 Sector - 44 Haryana Gurgaon122003 India Centre for High Energy Physics Indian Institute of Science Karnataka Bengaluru560012 India ICFO - Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Av. Carl Friedrich Gauss 3 Castelldefels Barcelona08860 Spain Chemical Physics Theory Group Department of Chemistry University of Toronto Canada DUG Technology 76 Kings Park Rd West PerthWA6005 Australia Rigetti Computing 2919 Seventh Street BerkeleyCA94710 United States Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute for Advanced Simulation Jülich Supercomputing Centre Forschungszentrum Jülich Jülich52425 Germany University of Amsterdam Netherlands School of Physical Sciences National Institute of Science Education and Research HBNI Odisha Jatni752 050 India Cervest United Kingdom Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 Rio de Janeiro22290-180 Brazil Zurich8092 Switzerland Neo4j UK Ltd United Kingdom Zapata Computing Inc. United States ITC Infotech Bangalore India Department of Microtechnology and Nanoscienc

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane’s core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Transcript expression-aware annotation improves rare variant interpretation (vol 581, pg 452, 2020)

引用

NATURE 2021年第7846期590卷 E54-E54页

作者： Cummings, Beryl B. Karczewski, Konrad J. Kosmicki, Jack A. Seaby, Eleanor G. Watts, Nicholas A. Singer-Berk, Moriel Mudge, Jonathan M. Karjalainen, Juha Satterstrom, F. Kyle O'Donnell-Luria, Anne H. Poterba, Timothy Seed, Cotton Solomonson, Matthew Alfoldi, Jessica Daly, Mark J. MacArthur, Daniel G. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Analytical and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Medical School Boston MA USA Genomic Informatics Group University Hospital Southampton Southampton UK Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA Division of Genetics and Genomics Boston Children’s Hospital Boston MA USA Department of Pediatrics Harvard Medical School Boston MA USA European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton Cambridge UK Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Broad Genomics Broad Institute of MIT and Harvard Cambridge MA USA National Heart & Lung Institute and MRC London Institute of Medical Sciences Imperial College London London UK Cardiovascular Research Centre Royal Brompton & Harefield Hospitals NHS Trust London UK Cardiac Arrhythmia Service and Cardiovascular Research Center Massachusetts General Hospital Boston MA USA Estonian Genome Center Institute of Genomics University of Tartu Tartu Estonia Department of Medicine Harvard Medical School Boston MA USA Cardiac Arrhythmia Service Massachusetts General Hospital Boston MA USA Division of General Internal Medicine Massachusetts General Hospital Boston MA USA Institute for Molecular Medicine FIMM University of Helsinki Helsinki Finland Department of Public Health Faculty of Medicine University of Helsinki Helsinki Finland Department of Genetics Harvard Medical School Boston MA USA Data Sciences Platform Broad Institute of MIT and Harvard Cambridge MA USA Genomics Platform Broad Institute of MIT and Harvard Cambridge MA USA Wellcome Sanger Institute Hinxton Cambridgeshire UK Unidad de Investigaci

来源：评论

学校读者我要写书评

暂无评论

On the EMG-based torque estimation for humans coupled with a force-controlled elbow exoskeleton

On the EMG-based torque estimation for humans coupled with a...

引用

International Conference on Advanced Robotics (ICAR)

作者： Jessica Beltran Ullauri Luka Peternel Barkan Ugurlu Yoji Yamada Jun Morimoto Dept. of Mechanical Science and Engineering Nagoya University Nagoya Japan Dept. of Brain Robot Interface Computational Neuroscience Labs Kyoto Japan Dept. for Automation Jožef Stefan Institute Ljubljana Slovenia Dept. of Mechanical Engineering Ozyegin University Istanbul Turkey

Exoskeletons are successful at supporting human motion only when the necessary amount of power is provided at the right time. Exoskeleton control based on EMG signals can be utilized to command the required amount of support in real-time. To this end, one needs to map human muscle activity to the desired task-specific exoskeleton torques. In order to achieve such mapping, this paper analyzes two distinct methods to estimate the human-elbow-joint torque based on the related muscle activity. The first model is adopted from pneumatic artificial muscles (PAMs). The second model is based on a machine learning method known as Gaussian Process Regression (GPR). The performance of both approaches were assessed based on their ability to estimate the elbow-joint torque of two able-bodied subjects using EMG signals that were collected from biceps and triceps muscles. The experiments suggest that the GPR-based approach provides relatively more favorable predictions.

关键词： Muscles Torque Joints Exoskeletons Electromyography Force Elbow

来源：评论

学校读者我要写书评

暂无评论

159 Dermoscopy versus videocapillaroscopy in the assessment of nailfold capillaroscopy images in patients with systemic sclerosis and healthy controls

引用

Rheumatology 2018年第SUPPL_3期57卷

作者： Dinsdale, Graham Peytrignet, Sebastien Moore, Tonia Berks, Michael Roberts, Christopher Manning, Joanne Allen, John Anderson, Marina Cutolo, Maurizio Hesselstrand, Roger Howell, Kevin Pizzorni, Carmen Smith, Vanessa Sulli, Alberto Wildt, Marie Taylor, Christopher Murray, Andrea Herrick, Ariane Centre for Musculoskeletal Research University of Manchester Salford Royal Hospital NHS Foundation Trust MAHSC Manchester UNITED KINGDOM Centre for Imaging Sciences Division of Informatics Imaging & Data Sciences University of Manchester Manchester UNITED KINGDOM Centre for Biostatistics Division of Population Health Health Services Research & Primary Care University of Manchester Manchester UNITED KINGDOM Microvascular Diagnostics Northern Medical Physics and Clinical Engineering Freeman Hospital Newcastle-upon-Tyne UNITED KINGDOM Institute of Ageing and Chronic Disease University of Liverpool Liverpool UNITED KINGDOM Research Laboratory and Academic Division of Clinical Rheumatology Department of Internal Medicine University of Genova Genova ITALY Department of Clinical Sciences Section of Rheumatology Lund University Lund SWEDEN Institute of Immunity and Transplantation University College London Royal Free Campus London UNITED KINGDOM Research Laboratory and Academic Division of Clinical Rheumatology Dept. Internal Medicine University of Genova Genova ITALY Department of Rheumatology Ghent University Hospital Faculty of Internal Medicine Ghent University Ghent BELGIUM Research Laboratory and Academic Division of Clinical Rheumatology Dept. Internal Medicine University of Genova Manchester ITALY NIHR Manchester Musculoskeletal Biomedical Research Centre Central Manchester NHS Foundation Trust Manchester Academic Health Science Centre Manchester UNITED KINGDOM

来源：评论

学校读者我要写书评

暂无评论

Euclid preparation: IX. EuclidEmulator2 – Power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations

arXiv

引用

arXiv 2020年

作者： Knabenhansz, M. Stadel, J. Potter, D. Dakin, J. Hannestad, S. Tram, T. Marelli, S. Schneider, A. Teyssier, R. Andreon, S. Auricchio, N. Baccigalupi, C. Balaguera-Antolínez, A. Baldi, M. Bardelli, S. Battaglia, P. Bender, R. Biviano, A. Bodendorf, C. Bozzo, E. Branchini, E. Brescia, M. Burigana, C. Cabanac, R. Camera, S. Capobianco, V. Cappi, A. Carbone, C. Carretero, J. Carvalho, C.S. Casas, R. Casas, S. Castellano, M. Castignani, G. Cavuoti, S. Cledassou, R. Colodro-Conde, C. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Corcione, L. Coupon, J. Courtois, H.M. da Silva, A. de la Torre, S. Di Ferdinando, D. Duncan, C.A.J. Dupac, X. Fabbian, G. Farrens, S. Ferreira, P.G. Finelli, F. Frailis, M. Franceschi, E. Galeotta, S. Garilli, B. Giocoli, C. Gozaliasl, G. Graciá-Carpio, J. Grupp, F. Guzzo, L. Holmes, W. Hormuth, F. Israel, H. Jahnke, K. Keihanen, E. Kermiche, S. Kirkpatrick, C.C. Kubik, B. Kunz, M. Kurki-Suonio, H. Ligori, S. Lilje, P.B. Lloro, I. Maino, D. Marggraf, O. Markovic, K. Martinet, N. Marulli, F. Massey, R. Mauri, N. Maurogordato, S. Medinaceli, E. Meneghetti, M. Metcalf, B. Meylan, G. Moresco, M. Morin, B. Moscardini, L. Munari, E. Neissner, C. Niemi, S.M. Padilla, C. Paltani, S. Pasian, F. Patrizii, L. Pettorino, V. Pires, S. Polenta, G. Poncet, M. Raison, F. Renzi, A. Rhodes, J. Riccio, G. Romelli, E. Roncarelli, M. Saglia, R. Sánchez, A.G. Sapone, D. Schneider, P. Scottez, V. Secroun, A. Serrano, S. Sirignano, C. Sirri, G. Stanco, L. Sureau, F. Tallada Crespí, P. Taylor, A.N. Tenti, M. Tereno, I. Toledo-Moreo, R. Torradeflot, F. Valenziano, L. Valiviita, J. Vassallo, T. Viel, M. Wang, Y. Welikala, N. Whittaker, L. Zacchei, A. Zucca, E. Institute for Computational Science University of Zurich Winterthurerstrasse 190 Zurich8057 Switzerland Department of Physics and Astronomy University of Aarhus Ny Munkegade 120 Aarhus CDK-8000 Denmark Risk Safety and Uncertainty Quantification Dept. of Civil Engineering ETH Zurich Stefano-Franscini-Platz 5 Zurich8093 Switzerland Institute for Particle Physics and Astrophysics Dept. of Physics ETH Zurich Wolfgang-Pauli-Strasse 27 Zurich8093 Switzerland INAF-Osservatorio Astronomico di Brera via Brera 28 MilanoI-20122 Italy INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna via Piero Gobetti 93/3 BolognaI-40129 Italy SISSA International School for Advanced Studies via Bonomea 265 Trieste TSI-34136 Italy INFN Sezione di Trieste via Valerio 2 Trieste TSI-34127 Italy INAF-Osservatorio Astronomico di Trieste via G. B. Tiepolo 11 TriesteI-34131 Italy Universidad de la Laguna E-38206 San Cristóbal de La Laguna Tenerife Spain Instituto de Astrofísica de Canarias Calle vía Làctea s/n San Cristóbal de la Laguna Tenerife38204 Spain via Gobetti 93/3 BolognaI-40127 Italy Dipartimento di Fisica e Astronomia Universitá di Bologna via Gobetti 93/2 BolognaI-40129 Italy INFN-Sezione di Bologna viale Berti Pichat 6/2 BolognaI-40127 Italy INAF-IASF Bologna via Piero Gobetti 101 BolognaI-40129 Italy Universitäts-Sternwarte München Fakultät für Physik Ludwig-Maximilians-Universität München Scheinerstrasse 1 München81679 Germany Max Planck Institute for Extraterrestrial Physics Giessenbachstr. 1 GarchingD-85748 Germany IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy Department of Astronomy University of Geneva ch. d’Écogia 16 VersoixCH-1290 Switzerland INFN-Sezione di Roma Tre via della Vasca Navale 84 RomaI-00146 Italy Department of Mathematics and Physics Roma Tre University via della Vasca Navale 84 RomeI-00146 Italy INAF-Osservatorio Astronomico di Roma via Frascati 33 Montepo

We present a new, updated version of the EuclidEmulator (called EuclidEmulator2), a fast and accurate predictor for the nonlinear correction of the matter power spectrum. Percent-level accurate emulation is now supported in the eight-dimensional parameter space of w0waCDM+ ∑ mv models between redshift z = 0 and z = 3 for spatial scales within the range 0.01 ℎ Mpc−1 ≤ k ≤ 10 ℎ Mpc−1. In order to achieve this level of accuracy, we have had to improve the quality of the underlying N-body simulations used as training data: (i) we use self-consistent linear evolution of non-dark matter species such as massive neutrinos, photons, dark energy and the metric field, (ii) we perform the simulations in the so-called N-body gauge, which allows one to interpret the results in the framework of general relativity, (iii) we run over 250 high-resolution simulations with 30003 particles in boxes of 1(ℎ−1 Gpc)3 volumes based on paired-and-fixed initial conditions and (iv) we provide a resolution correction that can be applied to emulated results as a post-processing step in order to drastically reduce systematic biases on small scales due to residual resolution effects in the simulations. We find that the inclusion of the dynamical dark energy parameter wa significantly increases the complexity and expense of creating the emulator. The high fidelity of EuclidEmulator2 is tested in various comparisons against N-body simulations as well as alternative fast predictors like HALOFIT, HMCode and CosmicEmu. A blind test is successfully performed against the Euclid Flagship v2.0 simulation. Nonlinear correction factors emulated with EuclidEmulator2 are accurate at the level of 1% or better for 0.01 ℎ Mpc−1 ≤ k ≤ 10 ℎ Mpc−1 and z ≤ 3 compared to high-resolution dark matter only simulations. EuclidEmulator2 is publicly available at https://***/miknab/EuclidEmulator2. © 2020, CC-BY-NC-SA.

关键词： Power spectrum

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：