检索结果-内蒙古大学图书馆

Spin-triplet excitonic insulator: The case of graphone

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Jiang, Zeyu Lou, Wenkai Liu, Yu Li, Yuanchang Song, Haifeng Chang, Kai Duan, Wenhui Zhang, Shengbai State Key Laboratory of Low-Dimensional Quantum Physics Collaborative Innovation Center of Quantum Matter Department of Physics Tsinghua University Beijing100084 China SKLSM Institute of Semiconductors Chinese Academy of Sciences P.O. Box 912 Beijing100083 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100088 China Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China CAEP Software Center for High Performance Numerical Simulation Beijing100088 China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing100190 China Institute for Advanced Study Tsinghua University Beijing100084 China Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States

While various excitonic insulators have been studied in the literature, due to the perceived too-small spin splitting, spin-triplet excitonic insulator is rare. In two-dimensional systems such as a graphone, however, it is possible, as revealed by first-principles calculations coupled with Bethe-Salpeter equation. The critical temperature, given by an effective Hamiltonian, is 11.5 K. While detecting excitonic insulators is still a daunting challenge, the condensation of triplet excitons will result in spin superfluidity, which can be directly measured by a transport experiment. Nonlocal dielectric screening also leads to an unexpected phenomenon, namely, an indirect-to-direct transition crossover between single-particle band and exciton dispersion in graphone, which offers yet another test by experiment. Copyright © 2019, The Authors. All rights reserved.

关键词： Calculations

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

学校读者我要写书评

暂无评论

Nature 2024年第8003期627卷 347-357页

作者： Ken Suzuki Konstantinos Hatzikotoulas Lorraine Southam Henry J Taylor Xianyong Yin Kim M Lorenz Ravi Mandla Alicia Huerta-Chagoya Giorgio E M Melloni Stavroula Kanoni Nigel W Rayner Ozvan Bocher Ana Luiza Arruda Kyuto Sonehara Shinichi Namba Simon S K Lee Michael H Preuss Lauren E Petty Philip Schroeder Brett Vanderwerff Mart Kals Fiona Bragg Kuang Lin Xiuqing Guo Weihua Zhang Jie Yao Young Jin Kim Mariaelisa Graff Fumihiko Takeuchi Jana Nano Amel Lamri Masahiro Nakatochi Sanghoon Moon Robert A Scott James P Cook Jung-Jin Lee Ian Pan Daniel Taliun Esteban J Parra Jin-Fang Chai Lawrence F Bielak Yasuharu Tabara Yang Hai Gudmar Thorleifsson Niels Grarup Tamar Sofer Matthias Wuttke Chloé Sarnowski Christian Gieger Darryl Nousome Stella Trompet Soo-Heon Kwak Jirong Long Meng Sun Lin Tong Wei-Min Chen Suraj S Nongmaithem Raymond Noordam Victor J Y Lim Claudia H T Tam Yoonjung Yoonie Joo Chien-Hsiun Chen Laura M Raffield Bram Peter Prins Aude Nicolas Lisa R Yanek Guanjie Chen Jennifer A Brody Edmond Kabagambe Ping An Anny H Xiang Hyeok Sun Choi Brian E Cade Jingyi Tan K Alaine Broadaway Alice Williamson Zoha Kamali Jinrui Cui Manonanthini Thangam Linda S Adair Adebowale Adeyemo Carlos A Aguilar-Salinas Tarunveer S Ahluwalia Sonia S Anand Alain Bertoni Jette Bork-Jensen Ivan Brandslund Thomas A Buchanan Charles F Burant Adam S Butterworth Mickaël Canouil Juliana C N Chan Li-Ching Chang Miao-Li Chee Ji Chen Shyh-Huei Chen Yuan-Tsong Chen Zhengming Chen Lee-Ming Chuang Mary Cushman John Danesh Swapan K Das H Janaka de Silva George Dedoussis Latchezar Dimitrov Ayo P Doumatey Shufa Du Qing Duan Kai-Uwe Eckardt Leslie S Emery Daniel S Evans Michele K Evans Krista Fischer James S Floyd Ian Ford Oscar H Franco Timothy M Frayling Barry I Freedman Pauline Genter Hertzel C Gerstein Vilmantas Giedraitis Clicerio González-Villalpando Maria Elena González-Villalpando Penny Gordon-Larsen Myron Gross Lindsay A Guare Sophie Hackinger Liisa Hakaste Sohee Han Andrew T Hattersley Christian Herder Momoko Horikoshi Annie-Green Howard Willa Centre for Genetics and Genomics Versus Arthritis Centre for Musculoskeletal Research Division of Musculoskeletal and Dermatological Sciences University of Manchester Manchester UK. Department of Diabetes and Metabolic Diseases Graduate School of Medicine University of Tokyo Tokyo Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. konstantinos.hatzikotoulas@helmholtz-munich.de. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. Center for Precision Health Research National Human Genome Research Institute National Institutes of Health Bethesda MD USA. British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Heart and Lung Research Institute University of Cambridge Cambridge UK. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor MI USA. Corporal Michael J. Crescenz VA Medical Center Philadelphia PA USA. Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Department of Genetics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Programs in Metabolism and Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Diabetes Unit and Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. TIMI Study Group Division of Cardiovascular Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London L

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

关键词：

Micado psf-reconstruction work package description

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Simioni, Matteo Arcidiacono, Carmelo Grazian, Andrea Clenet, Yann Davies, Richard Gullieuszik, Marco Kleijn, Gijs Verdoes Pedichini, Fernando Wagner, Roland Ramlau, Ronny Zeilinger, Werner W. Vidal, Fabrice Vulcani, Benedetta Ragazzoni, Roberto Sevin, Arnaud Salasnich, Bernardo Baruffolo, Andrea Busoni, Lorenzo Esposito, Simone Gendron, Éric Piazzesi, Roberto Portaluri, Elisa Zanella, Anita Helin, Tapio Kuncarayakti, Hanindyo Mattila, Seppo Falomo, Renato Pinna, Enrico INAF Osservatorio Astronomico di Padova Vicolo dell’Osservatorio 5 PadovaI-35122 Italy LESIA Observatoire de Paris Section de Meudon 5 place Jules Janssen Meudon CedexF-92195 France MPE - Max-Planck-Institut für extraterrestrische Physik Giessenbachstrasse 1 GarchingD-85748 Germany University of Groningen PO Box 72 Groningen9700 AB Netherlands INAF Osservatorio Astronomico di Roma Via Frascati 33 Monte Porzio Catone00040 Italy Industrial Mathematics Institute Johannes Kepler University Linz Altenberger Strasse 69 Linz4040 Austria RICAM - Johann Radon Institute for Computational and Applied Mathematics Altenberger Strasse 69 Linz4040 Austria Department of Astrophysics University of Vienna Tuerkenschanzstrasse 17 WienA-1180 Austria Dipartimento di Fisica e Astronomia Università degli studi di Padova via Marzolo 8 PadovaI-35131 Italy INAF Osservatorio Astrofisico di Arcetri Via E. Fermi 5 Firenze50125 Italy INAF Osservatorio Astronomico d’Abruzzo Via Mentore Maggini TeramoI-64100 Italy LUT University P.O.Box 20 LappeenrantaFI-53851 Finland Tuorla Observatory Department of Physics and Astronomy University of Turku FI-20014 Finland

The point spread function reconstruction (PSF-R) capability is a deliverable of the MICADO@ESO-ELT project. The PSF-R team works on the implementation of the instrument software devoted to reconstruct the point spread function (PSF), independently of the science data, using adaptive optics (AO) telemetry data, both for Single Conjugate (SCAO) and Multi-Conjugate Adaptive Optics (MCAO) mode of the MICADO camera and spectrograph. The PSF-R application will provide reconstructed PSFs through an archive querying system to restore the telemetry data synchronous to each science frame that MICADO will generate. Eventually, the PSF-R software will produce the output according to user specifications. The PSF-R service will support the state-of-the-art scientific analysis of the MICADO imaging and spectroscopic data. © 2020, CC BY.

关键词： Optical transfer function

Local and global strong solutions for SQG in bounded domains

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Constantin, Peter Nguyen, Huy Quang DEPARTMENT OF MATHEMATICS PRINCETON UNIVERSITY PRINCETONNJ08544 United States PROGRAM IN APPLIED AND COMPUTATIONAL MATHEMATICS PRINCETON UNIVERSITY PRINCETONNJ08544 United States

We prove local well-posedness for the inviscid surface quasigeostrophic (SQG) equation in bounded domains of R2. When fractional Dirichlet Laplacian dissipation is added, global existence of strong solutions is obtained for small data for critical and supercritical cases. Global existence of strong solutions with arbitrary data is obtained in the subcritical *** Codes 35Q35, 35Q86 Copyright © 2017, The Authors. All rights reserved.

关键词：

Prior variances and depth un-biased estimators in eeg focal source imaging 1

学校读者我要写书评

暂无评论

Joint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107

作者： Koulouri, Alexandra Rimpiläinen, V. Brookes, M. Kaipio, J.P. Institute for Computational and Applied Mathematics University of Münster Münster Germany Institute for Biomagnetism and Biosignalanalysis University of Münster Münster Germany Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Department of Mathematics University of Auckland Auckland New Zealand

ISBN: (数字)9789811051227

ISBN: (纸本)9789811051210

In electroencephalography (EEG) source imaging, the inverse source estimates are depth biased in such a way that their maxima are often close to the sensors. This depth bias can be quantified by inspecting the statistics (mean and covariance) of these estimates. In this paper, we find weighting factors within a Bayesian framework for the used 1/2 sparsity prior that the resulting maximum a posterior (MAP) estimates do not favour any particular source location. Due to the lack of an analytical expression for the MAP estimate when this sparsity prior is used, we solve the weights indirectly. First, we calculate the Gaussian prior variances that lead to depth un-biased maximum a posterior (MAP) estimates. Subsequently, we approximate the corresponding weight factors in the sparsity prior based on the solved Gaussian prior variances. Finally, we reconstruct focal source configurations using the sparsity prior with the proposed weights and two other commonly used choices of weights that can be found in literature. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Inverse problems

Randomized near neighbor graphs, giant components and applications in data science

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Linderman, George C. Mishne, Gal Kluger, Yuval Steinerberger, Stefan Department of Pathology Program in Applied Mathematics Yale University

If we pick n random points uniformly in [0;1]dand connect each point to its k-nearest neighbors, then it is well known that there exists a giant connected component with high probability. We prove that in [0;1]dit suffices to connect every point to cd,1log log n points chosen randomly among its cd,2log n-nearest neighbors to ensure a giant component of size n-o(n) with high probability. This construction yields a much sparser random graph with ∼ n log log n instead of ∼ n log n edges that has comparable connectivity properties. This result has nontrivial implications for problems in data science where an affinity matrix is constructed: instead of picking the k-nearest neighbors, one can often pick′ k random points out of the k-nearest neighbors without sacrificing efficiency. This can massively simplify and accelerate computation, we illustrate this with several numerical examples. Copyright © 2017, The Authors. All rights reserved.

关键词： k-nearest neighbors

Replays of spatial memories suppress topological fluctuations in cognitive map

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Babichev, Andrey Morozov, Dmitriy Dabaghian, Yuri Department of Computational and Applied Mathematics Rice University HoustonTX77005 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Neurology University of Texas McGovern Medical School 6431 Fannin St HoustonTX77030

The spiking activity of the hippocampal place cells plays a key role in producing and sustaining an internalized representation of the ambient space-a cognitive map. These cells do not only exhibit location-specific spiking during navigation, but also may rapidly replay the navigated routs through endogenous dynamics of the hippocampal network. Physiologically, such reactivations are viewed as manifestations of "memory replays" that help to learn new information and to consolidate previously acquired memories by reinforcing synapses in the parahippocampal networks. Below we propose a computational model of these processes that allows assessing the effect of replays on acquiring a robust topological map of the environment and demonstrate that replays may play a key role in stabilizing the hippocampal representation of space. Copyright © 2018, The Authors. All rights reserved.

关键词： Topology

AI-Driven Antimicrobial Peptide Discovery: Mining and Generation

学校读者我要写书评

暂无评论

Accounts of chemical research 2025年第12期58卷 2025 Jun 3页

作者： Paulina Szymczak Wojciech Zarzecki Jiejing Wang Yiqian Duan Jun Wang Luis Pedro Coelho Cesar de la Fuente-Nunez Ewa Szczurek Institute of AI for Health Helmholtz Zentrum Munich Neuherberg 85764 Germany. Faculty of Mathematics Informatics and Mechanics University of Warsaw Warsaw 02-097 Poland. Faculty of Electronics and Information Technology Warsaw University of Technology Warsaw 00-661 Poland. Institute of Microbiology Chinese Academy of Sciences Beijing 100101 China. Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai 200433 China. Centre for Microbiome Research School of Biomedical Sciences Queensland University of Technology Translational Research Institute Woolloongabba Queensland 4102 Australia. Centre for Data Science Queensland University of Technology Brisbane Queensland 4001 Australia. Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania 19104 United States of America. Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania Philadelphia Pennsylvania 19104 United States of America. Department of Chemistry School of Arts and Sciences University of Pennsylvania Philadelphia Pennsylvania 19104 United States of America. Penn Institute for Computational Science University of Pennsylvania Philadelphia Pennsylvania 19104 United States of America.

ConspectusThe escalating threat of antimicrobial resistance (AMR) poses a significant global health crisis, potentially surpassing cancer as a leading cause of death by 2050. Traditional antibiotic discovery methods have not kept pace with the rapidly evolving resistance mechanisms of pathogens, highlighting the urgent need for novel therapeutic strategies. In this context, antimicrobial peptides (AMPs) represent a promising class of therapeutics due to their selectivity toward bacteria and slower induction of resistance compared to classical, small molecule antibiotics. However, designing effective AMPs remains challenging because of the vast combinatorial sequence space and the need to balance efficacy with low toxicity. Addressing this issue is of paramount importance for chemists and researchers dedicated to developing next-generation antimicrobial *** intelligence (AI) presents a powerful tool to revolutionize AMP discovery. By leveraging AI, we can navigate the immense sequence space more efficiently, identifying peptides with optimal therapeutic properties. This Account explores the emerging application of AI in AMP discovery, focusing on two primary strategies: AMP mining, and AMP generation, as well as the use of discriminative methods as a valuable *** mining involves scanning biological sequences to identify potential AMPs. Discriminative models are then used to predict the activity and toxicity of these peptides. This approach has successfully identified numerous promising candidates, which were subsequently validated experimentally, demonstrating the potential of AI in AMP design and *** generation, on the other hand, creates novel peptide sequences by learning from existing data through generative modeling. This class of models optimizes for desired properties, such as increased activity and reduced toxicity, potentially producing synthetic peptides that surpass naturally occurring ones. Despite the risk of generating un

关键词：

Joint analysis of Dark Energy Survey Year 3 data and CMB lensing from SPT and Planck. III. Combined cosmological constraints

学校读者我要写书评

暂无评论

Physical Review D 2023年第2期107卷 023531-023531页

作者： T. M. C. Abbott M. Aguena A. Alarcon O. Alves A. Amon F. Andrade-Oliveira J. Annis B. Ansarinejad S. Avila D. Bacon E. J. Baxter K. Bechtol M. R. Becker B. A. Benson G. M. Bernstein E. Bertin J. Blazek L. E. Bleem S. Bocquet D. Brooks E. Buckley-Geer D. L. Burke H. Camacho A. Campos J. E. Carlstrom A. Carnero Rosell M. Carrasco Kind J. Carretero R. Cawthon C. Chang C. L. Chang R. Chen A. Choi R. Chown C. Conselice J. Cordero M. Costanzi T. Crawford A. T. Crites M. Crocce L. N. da Costa C. Davis T. M. Davis T. de Haan J. De Vicente J. DeRose S. Desai H. T. Diehl M. A. Dobbs S. Dodelson P. Doel C. Doux A. Drlica-Wagner K. Eckert T. F. Eifler F. Elsner J. Elvin-Poole S. Everett W. Everett X. Fang I. Ferrero A. Ferté B. Flaugher P. Fosalba O. Friedrich J. Frieman J. García-Bellido M. Gatti E. M. George T. Giannantonio G. Giannini D. Gruen R. A. Gruendl J. Gschwend G. Gutierrez N. W. Halverson I. Harrison K. Herner S. R. Hinton G. P. Holder D. L. Hollowood W. L. Holzapfel K. Honscheid J. D. Hrubes H. Huang E. M. Huff D. Huterer B. Jain D. J. James M. Jarvis T. Jeltema S. Kent L. Knox A. Kovacs E. Krause K. Kuehn N. Kuropatkin O. Lahav A. T. Lee P.-F. Leget P. Lemos A. R. Liddle C. Lidman D. Luong-Van J. J. McMahon N. MacCrann M. March J. L. Marshall P. Martini J. McCullough P. Melchior F. Menanteau S. S. Meyer R. Miquel L. Mocanu J. J. Mohr R. Morgan J. Muir J. Myles T. Natoli A. Navarro-Alsina R. C. Nichol Y. Omori S. Padin S. Pandey Y. Park F. Paz-Chinchón M. E. S. Pereira A. Pieres A. A. Plazas Malagón A. Porredon J. Prat C. Pryke M. Raveri C. L. Reichardt R. P. Rollins A. K. Romer A. Roodman R. Rosenfeld A. J. Ross J. E. Ruhl E. S. Rykoff C. Sánchez E. Sanchez J. Sanchez K. K. Schaffer L. F. Secco I. Sevilla-Noarbe E. Sheldon T. Shin E. Shirokoff M. Smith Z. Staniszewski A. A. Stark E. Suchyta M. E. C. Swanson G. Tarle C. To M. A. Troxel I. Tutusaus T. N. Varga J. D. Vieira N. Weaverdyck R. H. Wechsler J. Weller R. Williamson W. L. K. Wu B. Yanny B. Yin Y. Zhang J. Zuntz Cerro Tololo Inter-American Observatory NSF’s National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile Laboratório Interinstitucional de e-Astronomia–LIneA Rua Gal. José Cristino 77 Rio de Janeiro RJ–20921-400 Brazil Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Institute of Astronomy University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Fermi National Accelerator Laboratory P. O. Box 500 Batavia Illinois 60510 USA School of Physics University of Melbourne Parkville VIC 3010 Australia Instituto de Fisica Teorica UAM/CSIC Universidad Autonoma de Madrid 28049 Madrid Spain Institute of Cosmology and Gravitation University of Portsmouth Portsmouth PO1 3FX United Kingdom Institute for Astronomy University of Hawai’i 2680 Woodlawn Drive Honolulu Hawaii 96822 USA Physics Department 2320 Chamberlin Hall University of Wisconsin-Madison 1150 University Avenue Madison Wisconsin 53706-1390 USA Department of Astronomy and Astrophysics University of Chicago Chicago Illinois 60637 USA Kavli Institute for Cosmological Physics University of Chicago Chicago Illinois 60637 USA Department of Physics and Astronomy University of Pennsylvania Philadelphia Pennsylvania 19104 USA CNRS UMR 7095 Institut d’Astrophysique de Paris F-75014 Paris France Sorbonne Universités UPMC Univ Paris 06 UMR 7095 Institut d’Astrophysique de Paris F-75014 Paris France Department of Physics Northeastern University Boston Massachusetts 02115 USA High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue Argonne Illinois 60439 USA University Observatory Faculty of Physics Ludwig-Maximilians-Universität Scheinerstr. 1 81679 Munich Germany Department of Physics and Astronomy University College London Gower Stre

We present cosmological constraints from the analysis of two-point correlation functions between galaxy positions and galaxy lensing measured in Dark Energy Survey (DES) Year 3 data and measurements of cosmic microwave background (CMB) lensing from the South Pole Telescope (SPT) and Planck. When jointly analyzing the DES-only two-point functions and the DES cross-correlations with SPT+Planck CMB lensing, we find Ωm=0.344±0.030 and S8≡σ8(Ωm/0.3)0.5=0.773±0.016, assuming ΛCDM. When additionally combining with measurements of the CMB lensing autospectrum, we find Ωm=0.306−0.021+0.018 and S8=0.792±0.012. The high signal-to-noise of the CMB lensing cross-correlations enables several powerful consistency tests of these results, including comparisons with constraints derived from cross-correlations only, and comparisons designed to test the robustness of the galaxy lensing and clustering measurements from DES. Applying these tests to our measurements, we find no evidence of significant biases in the baseline cosmological constraints from the DES-only analyses or from the joint analyses with CMB lensing cross-correlations. However, the CMB lensing cross-correlations suggest possible problems with the correlation function measurements using alternative lens galaxy samples, in particular the redmagic galaxies and high-redshift maglim galaxies, consistent with the findings of previous studies. We use the CMB lensing cross-correlations to identify directions for further investigating these problems.

关键词： Cosmic microwave background Cosmological constant Cosmological parameters Dark energy Dark matter Evolution of the Universe Large scale structure of the Universe Optical, UV, & IR astronomy Normal galaxies