检索结果-内蒙古大学图书馆

Characteristics of plasma produced from tsunami impacted soil sample using a transversely excited atmospheric pressure-carbon dioxide (TEA-CO2) laser

引用

AIP Conference Proceedings 2023年第1期2480卷

作者： Rara Mitaphonna Muliadi Ramli Kurnia Lahna Nazli Ismail Kazuyoshi Kurihara Nasrullah Idris Doctoral Program of Mathematics and Applied Sciences Universitas Syiah Kuala 23111 Banda Aceh Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Jl. Syech Abdurrauf No. 3 Darussalam 23111 Banda Aceh Aceh Indonesia Department of Physics Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Jl. Syech Abdurrauf No. 3 Darussalam 23111 Banda Aceh Aceh Indonesia Department of Physics Faculty of Education University of Fukui 3-9-1 Bunkyo Fukui 910-8507 Japan

In this work, plasma characteristics in terms of temperature and electron density of a plasma of the tsunami impacted soil sample induced by a transversely excited atmospheric carbon dioxide (TEA-CO2) LIBS was studied. Practically, the tsunami impacted soil samples was taken from at the worst affected area by the giant tsunami, Banda Aceh city, Aceh province, Indonesia. The generated plasma has been produced after TEA-CO2 pulses laser was targeted onto sample placed on a sample holder in air surrounding gas. The emission spectral lines emitted from the plasma region collected using system of an optical multichannel analyser (OMA) allowed atomic emission lines could be clearly identified. The temperature of plasma was estimateed through the method of Boltzmann plot, while the plasma electron density has been obtained by method of Saha-Boltzmann using spectral lines of calcium both atomic and ionic spectral lines. As results, the plasma temperature and electron density produced were T = 8691 K and 5,2 × 1015 cm−3, respectively. Generally, the plasma temperature and density electron obtained are fit to the Maxwell distribution criteria kT <50,000 and Mcwhriter’s criteria (Ne >1.6×1012 T½ (ΔE)3) for plasma laser in state of local thermal equilibrium LTE).

关键词：

来源：评论

学校读者我要写书评

暂无评论

The bulk van der Waals layered magnet CrSBr is a quasi-1D material

arXiv

引用

arXiv 2022年

作者： Klein, Julian Pingault, Benjamin Florian, Matthias Heißenbüttel, Marie-Christin Steinhoff, Alexander Song, Zhigang Torres, Kierstin Dirnberger, Florian Curtis, Jonathan B. Weile, Mads Penn, Aubrey Deilmann, Thorsten Dana, Rami Bushati, Rezlind Quan, Jiamin Luxa, Jan Sofer, Zdenek Alù, Andrea Menon, Vinod M. Wurstbauer, Ursula Rohlfing, Michael Narang, Prineha Lončar, Marko Ross, Frances M. Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States QuTech Delft University of Technology Delft2600 GA Netherlands Department of Electrical and Computer Engineering Department of Physics University of Michigan Ann ArborMI48109 United States Institut für Festkörpertheorie Westfälische Wilhelms-Universität Münster Münster48149 Germany Institut für Theoretische Physik Universität Bremen P.O. Box 330 440 Bremen28334 Germany Bremen Center for Computational Materials Science University of Bremen Bremen28359 Germany Department of Physics City College of New York New YorkNY10031 United States College of Letters and Science UCLA Los AngelesCA90095 United States Department of Physics Technical University of Denmark Kgs. LyngbyDK-2800 Denmark MIT.nano Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics The Graduate Center City University of New York New YorkNY10016 United States Photonics Initiative CUNY Advanced Science Research Center New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10026 United States Department of Inorganic Chemistry University of Chemistry and Technology Prague Technická 5 Prague 6166 28 Czech Republic Institute of Physics Center for Nanotechnology University of Münster Münster48149 Germany

Correlated quantum phenomena in one-dimensional (1D) systems that exhibit competing electronic and magnetic order are of strong interest for studying fundamental interactions and excitations, such as Tomonaga-Luttinger liquids and topological orders and defects with properties completely different from the quasiparticles expected in their higher-dimensional counterparts. However, clean 1D electronic systems are difficult to realize experimentally, particularly magnetically ordered systems. Here, we show that the van der Waals layered magnetic semiconductor CrSBr behaves like a quasi-1D material embedded in a magnetically ordered environment. The strong 1D electronic character originates from the Cr-S chains and the combination of weak interlayer hybridization and anisotropy in effective mass and dielectric screening with an effective electron mass ratio of meX/meY ∼ 50. This extreme anisotropy experimentally manifests in strong electron-phonon and exciton-phonon interactions, a Peierls-like structural instability and a Fano resonance from a van Hove singularity of similar strength of metallic carbon nanotubes. Moreover, due to the reduced dimensionality and interlayer coupling, CrSBr hosts spectrally narrow (1 meV) excitons of high binding energy and oscillator strength that inherit the 1D character. Overall, CrSBr is best understood as a stack of weakly hybridized monolayers and appears to be an experimentally attractive candidate for the study of exotic exciton and 1D correlated many-body physics in the presence of magnetic order. Copyright © 2022, The Authors. All rights reserved.

关键词： Excitons

来源：评论

学校读者我要写书评

暂无评论

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

arXiv

引用

arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

A substitutional quantum defect in WS2 discovered by high-throughput computational screening and fabricated by site-selective STM manipulation

arXiv

引用

arXiv 2023年

作者： Thomas, John C. Chen, Wei Xiong, Yihuang Barker, Bradford A. Zhou, Junze Chen, Weiru Rossi, Antonio Kelly, Nolan Yu, Zhuohang Zhou, Da Kumari, Shalini Barnard, Edward S. Robinson, Joshua A. Terrones, Mauricio Schwartzberg, Adam Ogletree, D. Frank Rotenberg, Eli Noack, Marcus M. Griffin, Sinéad Raja, Archana Strubbe, David A. Rignanese, Gian-Marco Weber-Bargioni, Alexander Hautier, Geoffroy Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA United States Thayer School of Engineering Dartmouth College HanoverNH03755 United States Institute of Condensed Matter and Nanoscicence Université catholique de Louvain Louvain-la-Neuve1348 Belgium Department of Physics University of California Merced MercedCA95343 United States Advanced Light Source Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Materials Science and Engineering The Pennsylvania State University University ParkPA16082 United States Center for Two-Dimensional and Layered Materials The Pennsylvania State University University ParkPA16802 United States Department of Physics The Pennsylvania State University University ParkPA16802 United States Department of Chemistry The Pennsylvania State University University ParkPA16802 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

Point defects in two-dimensional materials are of key interest for quantum information science. However, the parameter space of possible defects is immense, making the identification of high-performance quantum defects very challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS2, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime. Our computed database spans more than 700 charged defects formed through substitution on the tungsten or sulfur site. We found that sulfur substitutions enable the most promising quantum defects. We computationally identify the neutral cobalt substitution to sulfur (Co0S) and fabricate it with scanning tunneling microscopy (STM). The Co0S electronic structure measured by STM agrees with first principles and showcases an attractive quantum defect. Our work shows how HT computational screening and nanoscale synthesis routes can be combined to design promising quantum defects. © 2023, CC BY.

关键词： Nanocrystals

来源：评论

学校读者我要写书评

暂无评论

Training of Physical Neural Networks

arXiv

引用

arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A Robust Light-Curve Diagnostic for Electron-Capture Supernovae and Low-Mass Fe-Core-Collapse Supernovae

arXiv

引用

arXiv 2024年

作者： Sato, Masato Tominaga, Nozomu Blinnikov, Sergei I. Potashov, Marat Sh. Moriya, Takashi J. Hiramatsu, Daichi Astronomical Science Program Graduate Institute for Advanced Studies SOKENDAI 2-21-1 Osawa Tokyo Mitaka181-8588 Japan National Astronomical Observatory of Japan National Institutes of Natural Sciences 2-21-1 Osawa Tokyo Mitaka181-8588 Japan Department of Physics Faculty of Science and Engineering Konan University 8-9-1 Okamoto Hyogo Kobe658-8501 Japan NRC Kurchatov Institute Moscow123182 Russia Moscow127055 Russia Keldysh Institute of Applied Mathematics RAS 4 Miusskaya Square Moscow125047 Russia School of Physics and Astronomy Monash University ClaytonVIC3800 Australia Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138-1516 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States

Core-collapse supernovae (CCSNe) are the terminal explosions of massive stars. While most massive stars explode as iron-core-collapse supernovae (FeCCSNe), slightly less massive stars explode as electron-capture supernovae (ECSNe), shaping the low-mass end of CCSNe. ECSNe was proposed ∼ 40 years ago and first-principles simulations also predict their successful explosions. Observational identification and investigation of ECSNe are important for the completion of stellar evolution theory. To date, only one promising candidate has been proposed, SN 2018zd, other than the historical progenitor of the Crab Nebula, SN 1054. We present representative synthetic light curves of low-mass FeCCSNe and ECSNe exploding with energies in circumstellar media (CSM) estimated with theoretically or observationally plausible methods. The plateaus of the ECSNe are shorter, brighter, and bluer than those of the FeCCSNe. To investigate the robustness of their intrinsic differences, we adopted various explosion energies and CSM. Although they may have similar bolometric light-curve plateaus, ECSNe are bluer than FeCCSNe in the absence of strong CSM interaction, illustrating that multicolor observations are essential to identify ECSNe. This provides a robust indicator of ECSNe because the bluer plateaus stem from the low-density envelopes of their super-asymptotic-giant-branch progenitors. Furthermore, we propose a distance-independent method to identify ECSNe: (g − r)tPT/2 PT − 0.4, i.e., blue g − r at the middle of the plateau (g − r)tPT/2, where tPT is the transition epoch from plateau to tail. Using this method, we identified SN 2018zd as an ECSN, which we believe to be the first ECSN identified with modern observing techniques. © 2024, CC BY.

关键词： Supernovae

来源：评论

学校读者我要写书评

暂无评论

The Phonon Quasiparticle Approach for Anharmonic Properties of Solids

引用

Journal of physics: Conference Series 2022年第1期2207卷

作者： Zhen Zhang Dong-Bo Zhang Tao Sun Renata M. Wentzcovitch Department of Applied Physics and Applied Mathematics Columbia University New York NY 10027 USA College of Nuclear Science and Technology Beijing Normal University Beijing 100875 People's Republic of China Beijing Computational Science Research Center Beijing 100193 People's Republic of China Key Laboratory of Computational Geodynamics Chinese Academy of Sciences Beijing 100049 People's Republic of China Department of Earth and Environmental Sciences Columbia University New York NY 10027 USA Lamont–Doherty Earth Observatory Columbia University Palisades NY 10964 USA

Knowledge of lattice anharmonicity is essential to elucidate distinctive thermal properties in crystalline solids. Yet, accurate ab initio investigations of lattice anharmonicity encounter difficulties owing to the cumbersome computations. Here we introduce the phonon quasiparticle approach and review its application to various materials. This method efficiently and reliably addresses lattice anharmonicity by combining ab initio molecular dynamics and lattice dynamics calculations. Thus, in principle, it accounts for full anharmonic effects and overcomes finite-size effects typical of ab initio molecular dynamics. The validity and effectiveness of the current approach are demonstrated in the computation of thermodynamic and heat transport properties of weakly and strongly anharmonic systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Topological Data Analysis of Black Hole Images

arXiv

引用

arXiv 2022年

作者： Christian, Pierre Chan, Chi-Kwan Hsu, Anthony Özel, Feryal Psaltis, Dimitrios Natarajan, Iniyan Physics Department Fairfield University 1073 N Benson Rd FairfieldCT06824 United States Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States Department of Computer Science University of Arizona 1040 4th St TucsonAZ85721 United States Wits Centre for Astrophysics University of the Witwatersrand 1 Jan Smuts Avenue Braamfontein Johannesburg2050 South Africa South African Radio Astronomy Observatory Observatory Cape Town7925 South Africa

Features such as photon rings, jets, or hot spots can leave particular topological signatures in a black hole image. As such, topological data analysis can be used to characterize images resulting from high resolution observations (synthetic or real) of black holes in the electromagnetic sector. We demonstrate that persistent homology allows for this characterization to be made automatically by counting the number of connected components and one-dimensional holes. Further, persistent homology also allows for the distance between connected components or diameter of holes to be extracted from the image. In order to apply persistent homology on synthetic black hole images, we also introduce metronization, a new algorithm to prepare black hole images into a form that is suitable for topological analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Black holes

来源：评论

学校读者我要写书评

暂无评论

The atacama cosmology telescope: DR5 maps of 18000 square degrees of the microwave sky from ACT 2008-2018 data

arXiv

引用

arXiv 2020年

作者： Naess, Sigurd Aiola, Simone Austermann, Jason E. Battaglia, Nick Beall, James A. Becker, Daniel T. Bond, Richard J. Calabrese, Erminia Choi, Steve K. Cothard, Nicholas F. Crowley, Kevin T. Darwish, Omar Datta, Rahul Denison, Edward V. Devlin, Mark Duell, Cody J. Duff, Shannon M. Duivenvoorden, Adriaan J. Dunkley, Jo Dünner, Rolando Fox, Anna E. Gallardo, Patricio A. Halpern, Mark Han, Dongwon Hasselfield, Matthew Hill, J. Colin Hilton, Gene C. Hilton, Matt Hincks, Adam D. Hložek, Renée Ho, Shuay-Pwu Patty Hubmayr, Johannes Huffenberger, Kevin Hughes, John P. Kosowsky, Arthur B. Louis, Thibaut Madhavacheril, Mathew S. McMahon, Jeff Moodley, Kavilan Nati, Federico Nibarger, John P. Niemack, Michael D. Page, Lyman Partridge, Bruce Salatino, Maria Schaan, Emmanuel Schillaci, Alessandro Schmitt, Benjamin Sherwin, Blake D. Sehgal, Neelima Sifón, Cristóbal Spergel, David Staggs, Suzanne Stevens, Jason Storer, Emilie Ullom, Joel N. Vale, Leila R. van Engelen, Alexander van Lanen, Jeff Vavagiakis, Eve M. Wollack, Edward J. Xu, Zhilei Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States Department of Astrophysical Sciences Peyton Hall Princeton University PrincetonNJ08544 United States Joseph Henry Laboratories of Physics Jadwin Hall Princeton University PrincetonNJ08544 United States Department of Physics and Astronomy Haverford College HaverfordPA19041 United States Laboratoire de l’Accélérateur Linéaire Univ. Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France NASA/Goddard Space Flight Center GreenbeltMD20771 United States Physics and Astronomy Department Stony Brook University Stony BrookNY11794 United States Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban4041 South Africa Department of Physics California Institute of Technology PasadenaCA91125 United States Instituto de Física Pontificia Universidad Católica de Valparaíso Chile Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Berkeley Center for Cosmological Physics University of California BerkeleyCA94720 United States School of Physics and Astronomy Cardiff University The Parade Cardiff WalesCF24 3AA United Kingdom Department of Physics University of Milano-Bicocca Piazza della Scienza 3 MilanoMI20126 Italy Astrophysics Research Centre School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Durban4041 South Africa Department of Physics and Astronomy Rutgers State University of New Jersey PiscatawayNJ08854-8019 United States StanfordCA United States Instituto de Astrofísica and Centro de Astro-Ingeniería Facultad de Física Pontificia Universidad Católica de Chile Av. Vicuña Mackenna 4860 Macul Santiago7820436 Chile Department of Physics Cornell University IthacaNY14853 United States Department of Astronomy Cornell University IthacaNY14853 United States Dept. of Physics and Astronomy Johns Hopkins University 3400 N. Charles St. BaltimoreMD21218 United States NIST Q

This paper presents a maximum-likelihood algorithm for combining sky maps with disparate sky coverage, angular resolution and spatially varying anisotropic noise into a single map of the sky. We use this to merge hundreds of individual maps covering the 2008–2018 ACT observing seasons, resulting in by far the deepest ACT maps released so far. We also combine the maps with the full Planck maps, resulting in maps that have the best features of both Planck and ACT: Planck’s nearly white noise on intermediate and large angular scales and ACT’s high-resolution and sensitivity on small angular scales. The maps cover over 18 000 square degrees, nearly half the full sky, at 100, 150 and 220 GHz. They reveal 4 000 optically-confirmed clusters through the Sunyaev Zel’dovich effect (SZ) and 18 500 point source candidates at > 5σ, the largest single collection of SZ clusters and millimeter wave sources to date. The multi-frequency maps provide millimeter images of nearby galaxies and individual Milky Way nebulae, and even clear detections of several nearby stars. Other anticipated uses of these maps include, for example, thermal SZ and kinematic SZ cluster stacking, CMB cluster lensing and galactic dust science. The method itself has negligible bias. However, due to the preliminary nature of some of the component data sets, we caution that these maps should not be used for precision cosmological analysis. The maps are part of ACT DR5, and are available on LAMBDA at https://***/product/act/actpol_prod_***. There is also a web atlas at https://***/public/snaess/actpol/dr5/atlas. © 2020, CC0.

关键词： Galaxies

来源：评论

学校读者我要写书评

暂无评论

Comparison of volume transport in the Halmahera Sea between La Nina 2011 and El Nino 2015 events based on numerical model

引用

IOP Conference Series: Earth and Environmental Science 2020年第1期618卷

作者： Muhammad Riza Mutiara R Putri Idris Mandang Department of Earth Science Faculty of Earth Science and Technology Bandung Institute of Technology Jalan Ganesha No 10 Bandung 40132 Indonesia Hydro-Atmosphere Environment Research Group Physical Oceanography and Computational Modeling Laboratory Program Study of Physics Faculty of Mathematics and Natural Sciences Mulawarman University Jalan Barong Tongkok No 04 Samarinda 75123 Indonesia

Variations of volume transport in the Halmahera Sea are strongly influenced by the El Nino Southern Oscillation (ENSO). Based on the Southern Oscillation Index (SOI), in 2011 La Nina event took place with a strength of 3.02, while in 2015 El Nino occurred with a strength of -2.6. This paper discusses the variation of transport volume caused by the ENSO phenomenon based on the results of the Regional Ocean Model System (ROMS). On the Halmahera Sea at a latitude of 0.3°S with a width of 67 km and a depth of down to 200 m, net volume transport always moves southward. The largest volume transport in La Nina 2011 occurred in September-October, which was -8.9 Sv. Meanwhile, in El Nino 2015 the largest volume transport occurred in July-August, which was equal to -4.9 Sv. The cross correlation coefficient between volume transport and SOI in 2011 and 2015 was r = 0.55 and r = 0.61 respectively, where these results indicate a strong relationship.

关键词：

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：