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arXiv 2017年

作者： Coulton, William R. Aiola, Simone Battaglia, Nicholas Calabrese, Erminia Choi, Steve K. Devlin, Mark J. Gallardo, Patricio A. Hill, J. Colin Hincks, Adam D. Hubmayr, Johannes Hughes, John P. Kosowsky, Arthur Louis, Thibaut Madhavacheril, Mathew S. Maurin, Loïc Naess, Sigurd Nati, Federico Niemack, Michael D. Page, Lyman A. Partridge, Bruce Sherwin, Blake D. Spergel, David N. Staggs, Suzanne T. van Engelen, Alexander Wollack, Edward J. Joseph Henry Laboratories Princeton University PrincetonNJ08544 United States Department of Astrophysical Sciences Princeton University Peyton Hall PrincetonNJ08544 United States Center for Computational Astrophysics Flatiron Institute 162 5th Avenue 10010 New YorkNY United States School of Physics and Astronomy Cardiff University The Parade CardiffCF24 3AA United Kingdom Department of Physics and Astronomy University of Pennsylvania 209 South 33rd Street PhiladelphiaPA19104 United States Department of Physics Cornell University IthacaNY14853 United States School of Natural Sciences Institute for Advanced Study Olden Lane PrincetonNJ08540 Department of Physics University of Rome "La Sapienza" Piazzale Aldo Moro 5 RomeI-00185 Italy Department of Physics and Astronomy University of British Columbia VancouverBCV6T 1Z4 Canada NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Department of Physics and Astronomy Rutgers State University of New Jersey PiscatawayNJ08854-8019 United States Department of Physics and Astronomy University of Pittsburgh PittsburghPA15260 United States Pittsburgh Particle Physics Astrophysics and Cosmology Center University of Pittsburgh PittsburghPA15260 Laboratoire de l’Accélérateur Linéaire Univ. Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France 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 and Astronomy Haverford College HaverfordPA19041 United States Department of Applied Mathematics and Theoretical Physics University of Cambridge Wilberforce Road CambridgeCB3 0WA Berkeley Center for Cosmological Physics University of California BerkeleyCA94720 Canadian Institute for Theoretical Astrophysics University of Toronto TorontoONM5S 3H8 Canada NASA/Goddard Space Flight Center GreenbeltMD20771 United States

Most secondary sources of cosmic microwave background anisotropy (radio sources, dusty galaxies, thermal Sunyaev Zel’dovich distortions from hot gas, and gravitational lensing) are highly non-Gaussian. Statistics beyond the power spectrum are therefore potentially important sources of information about the physics of these processes. We combine data from the Atacama Cosmology Telescope and with data from the Planck satellite (only using Planck data in the overlapping region) to constrain the amplitudes of a set of theoretical bispectrum templates from the thermal Sunyaev-Zeldovich (tSZ) effect, dusty star-forming galaxies (DSFGs), gravitational lensing, and radio galaxies. We make a strong detection of radio galaxies (>5σ) and have hints of non-Gaussianity arising from the tSZ effect, DSFGs, from cross-correlations between the tSZ effect and DSFGs and from cross-correlations among the tSZ effect, DSFGs and radio galaxies. These results suggest that the same halos host radio sources, DSFGs, and have tSZ signal. We present a new method to calculate the non-Gaussian contributions to the template covariances. Using this method we find significant non-Gaussian contributions to the variance and covariance of our templates, with templates involving the tSZ effect most effected. Strong degeneracies exist between the various sources at the current noise levels. In light of these degeneracies, combined with theoretical uncertainty in the templates, these results are a demonstration of this technique. With these caveats, we demonstrate the utility of future bispectrum measurements by using the tSZ bispectrum measurement to constrain a combination of the amplitude of matter fluctuations and the matter density to be σ8Ω0.17m = 0.65+0.05-0.06. Improvements in signal to noise from upcoming Advanced ACT, SPT-3G, Simons Observatory, and CMB-S4 observations will enable the separation of bispectrum components and robust constraints on cosmological parameters. Copyright © 2017, The Aut

关键词： Galaxies

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Automating the Practice of Science – Opportunities, Challenges, and Implications

arXiv

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arXiv 2024年

作者： Musslick, Sebastian Bartlett, Laura K. Chandramouli, Suyog H. Dubova, Marina Gobet, Fernand Griffiths, Thomas L. Hullman, Jessica King, Ross D. Nathan Kutz, J. Lucas, Christopher G. Mahesh, Suhas Pestilli, Franco Sloman, Sabina J. Holmes, William R. Institute of Cognitive Science Osnabrück University Osnabrück49090 Germany Department of Cognitive Linguistic & Psychological Sciences Brown University ProvidenceRI02912 United States Centre for Philosophy of Natural and Social Science London School of Economics Lakatos Building Houghton Street LondonWC2A 2AE United Kingdom AaltoFI-00076 Finland Department of Computing Science University of Alberta 8900 114 St NW EdmontonABT6G 2S4 Canada Cognitive Science Program Indiana University 1101 E 10th St BloomingtonIN47405 United States School of Psychology University of Roehampton LondonSW15 4JD United Kingdom Departments of Psychology and Computer Science Princeton University PrincetonNJ United States Department of Computer Science Northwestern University IL United States Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Department of Computer Science and Engineering Chalmers University of Technology Gothenburg412 96 Sweden Department of Applied Mathematics and Electrical and Computer Engineering University of Washington Seattle98195 United States School of Informatics University of Edinburgh 10 Crichton St. EH8 9AB United Kingdom Department of Materials Science and Engineering University of Toronto Canada Department of Psychology Department of Neuroscience The University of Texas AustinTX United States Department of Computer Science University of Manchester M13 9PL United Kingdom

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?;What are the current bottlenecks of automating scientific practice?;and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice. © 2024, CC BY.

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

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BMC NEUROSCIENCE 2017年第Sup1期18卷 1-79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 Bra

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New families in our Solar neighborhood: applying Gaussian Mixture models for objective classification of structures in the Milky Way and in simulations

arXiv

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arXiv 2021年

作者： Nikakhtar, Farnik Sanderson, Robyn E. Wetzel, Andrew Loebman, Sarah Sharma, Sanjib Beaton, Rachael Ted Mackereth, J. Poovelil, Vijith Jacob Zasowski, Gail Bonaca, Ana Martell, Sarah Jönsson, Henrik Faucher-Giguère, Claude-André Department of Physics & Astronomy University of Pennsylvania 209 S 33rd St. PhiladelphiaPA19104 United States Center for Computational Astrophysics Flatiron Institute 162 5th Ave New YorkNY10010 United States Department of Physics & Astronomy University of California DavisCA95616 United States Department of Physics University of California Merced 5200 North Lake Rd. MercedCA95343 United States Sydney Institute for Astronomy School of Physics University of Sydney A28 NSW2006 Australia Australia Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Canadian Institute for Theoretical Astrophysics University of Toronto 60 St. George Street TorontoONM5S 3H8 Canada Dunlap Institute for Astronomy and Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Department of Physics & Astronomy University of Utah Salt Lake CityUT84112 United States Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States School of Physics UNSW SydneyNSW2052 Australia Materials Science and Applied Mathematics Malmö University MalmöSE-205 06 Sweden Department of Physics and Astronomy CIERA Northwestern University 2145 Sheridan Road EvanstonIL60208 United States

The standard picture of galaxy formation motivates the decomposition of the Milky Way into 3–4 stellar populations with distinct kinematic and elemental abundance distributions: the thin disk, thick disk, bulge, and stellar halo. To test this idea, we construct a Gaussian mixture model (GMM) for both simulated and observed stars in the Solar neighborhood, using measured velocities and iron abundances (i.e., an augmented Toomre diagram) as the distributions to be decomposed. We compare results for the Gaia-APOGEE DR16 crossmatch catalog of the Solar neighborhood with those from a suite of synthetic Gaia-APOGEE crossmatches constructed from FIRE-2 cosmological simulations of Milky Way-mass galaxies. We find that in both the synthetic and real data, the best-fit GMM uses five independent components, some of whose properties resemble the standard populations predicted by galaxy formation theory. Two components can be identified unambiguously as the thin disk and another as the halo. However, instead of a single counterpart to the thick disk, there are three intermediate components with different age and alpha abundance distributions (although these data are not used to construct the model). We use decompositions of the synthetic data to show that the classified components indeed correspond to stars with different origins. By analogy with the simulated data, we show that our mixture model of the real Gaia-APOGEE crossmatch distinguishes the following components: (1) a classic thin disk of young stars on circular orbits (46%), (2) thin disk stars heated by interactions with satellites (22%), (3, 4) two components representing the velocity asymmetry of the alpha-enhanced thick disk (27%), and (5) a stellar halo consistent with early, massive accretion (4%). Copyright © 2021, The Authors. All rights reserved.

关键词： Galaxies

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DPA-2: a large atomic model as a multi-task learner

arXiv

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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The Simons Observatory: Modeling optical systematics in the large aperture telescope

arXiv

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arXiv 2020年

作者： Gudmundsson, Jon E. Gallardo, Patricio A. Puddu, Roberto Dicker, Simon R. Adler, Alexandre E. Ali, Aamir M. Bazarko, Andrew Chesmore, Grace E. Coppi, Gabriele Cothard, Nicholas F. Dachlythra, Nadia Devlin, Mark Dünner, Rolando Fabbian, Giulio Galitzki, Nicholas Golec, Joseph E. Patty Ho, Shuay-Pwu Hargrave, Peter C. Kofman, Anna M. Lee, Adrian T. Limon, Michele Matsuda, Frederick T. Mauskopf, Philip D. Moodley, Kavilan Nati, Federico Niemack, Michael D. Orlowski-Scherer, John Page, Lyman A. Partridge, Bruce Puglisi, Giuseppe Reichardt, Christian L. Sierra, Carlos E. Simon, Sara M. Teply, Grant P. Tucker, Carole Wollack, Edward J. Xu, Zhilei Zhu, Ningfeng The Oskar Klein Centre Department of Physics Stockholm University StockholmSE-106 91 Sweden Department of Physics Cornell University IthacaNY14853 United States Instituto de Astrofísica Centro de Astro-Ingeniería Facultad de Física Pontificia Universidad Católica de Chile Macul Santiago Chile Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Department of Physics University of California BerkeleyCA94720 United States Joseph Henry Laboratories of Physics Jadwin Hall Princeton University PrincetonNJ08544 United States Department of Astronomy and Astrophysics The University of Chicago ChicagoIL60637 United States Department of Applied and Engineering Physics Cornell University IthacaNY14853 United States Department of Physics & Astronomy University of Sussex BrightonBN1 9QH United Kingdom Department of Physics University of California San Diego San diegoCA92093 United States Department of Physics University of Chicago ChicagoIL60637 United States School of Physics and Astronomy Cardiff University CardiffCF24 3AA United Kingdom The University of Tokyo Institutes for Advanced Study The University of Tokyo KashiwaChiba277-8583 Japan School of Earth and Space Exploration Department of Physics Arizona State University TempeAZ85281 United States Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban4041 South Africa School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Westville Campus Durban4041 South Africa Department of Physics University of Milano-Bicocca Milano Italy Department of Astronomy Cornell University IthacaNY14853 United States Department of Astronomy Haverford College HaverfordPA19041 United States Computational Cosmology Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States School of Physics University of Melbourne ParkvilleVIC3010 Australia Fermi National Accelerator Laboratory MS2

We present geometrical and physical optics simulation results for the Simons Observatory Large Aperture Telescope. This work was developed as part of the general design process for the telescope;allowing us to evaluate the impact of various design choices on performance metrics and potential systematic effects. The primary goal of the simulations was to evaluate the final design of the reflectors and the cold optics which are now being built. We describe non-sequential ray tracing used to inform the design of the cold optics, including absorbers internal to each optics tube. We discuss ray tracing simulations of the telescope structure that allow us to determine geometries that minimize detector loading and mitigate spurious near-field effects that have not been resolved by the internal baffling. We also describe physical optics simulations, performed over a range of frequencies and field locations, that produce estimates of monochromatic far field beam patterns which in turn are used to gauge general optical performance. Finally, we describe simulations that shed light on beam sidelobes from panel gap diffraction. Copyright © 2020, The Authors. All rights reserved.

关键词： Physical optics

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The Black Hole Explorer: Motivation and Vision

arXiv

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arXiv 2024年

作者： Johnson, Michael D. Akiyama, Kazunori Baturin, Rebecca Bilyeu, Bryan Blackburn, Lindy Boroson, Don Cárdenas-Avendaño, Alejandro Chael, Andrew Chan, Chi-Kwan Chang, Dominic Cheimets, Peter Chou, Cathy Doeleman, Sheperd S. Farah, Joseph Galison, Peter Gamble, Ronald Gammie, Charles F. Gelles, Zachary Gómez, José L. Gralla, Samuel E. Grimes, Paul Gurvits, Leonid I. Hadar, Shahar Haworth, Kari Hada, Kazuhiro Hecht, Michael H. Honma, Mareki Houston, Janice Hudson, Ben Issaoun, Sara Jia, He Jorstad, Svetlana Kauffmann, Jens Kovalev, Yuri Y. Kurczynski, Peter Lafon, Robert Lupsasca, Alexandru Lehmensiek, Robert Ma, Chung-Pei Marrone, Daniel P. Marscher, Alan P. Melnick, Gary J. Narayan, Ramesh Niinuma, Kotaro Noble, Scott C. Palmer, Eric J. Palumbo, Daniel C.M. Paritsky, Lenny Peretz, Eliad Pesce, Dominic Plavin, Alexander Quataert, Eliot Rana, Hannah Ricarte, Angelo Roelofs, Freek Shtyrkova, Katia Sinclair, Laura C. Small, Jeffrey Kumara, Sridharan Tirupati Srinivasan, Ranjani Strominger, Andrew Tiede, Paul Tong, Edward Wang, Jade Weintroub, Jonathan Wielgus, Maciek Wong, George Zhang, Xinyue Alice Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Black Hole Initiative Harvard University 20 Garden Street CambridgeMA02138 United States Haystack Observatory Massachusetts Institute of Technology WestfordMA01886 United States Mizusawa VLBI Observatory National Astronomical Observatory of Japan Iwate023-0861 Japan MIT Lincoln Laboratory LexingtonMA02421 United States Princeton Gravity Initiative Princeton University PrincetonNJ08544 United States Steward Observatory and Department of Astronomy University of Arizona 933 N. Cherry Ave. 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 Las Cumbres Observatory 6740 Cortona Drive GoletaCA93117-5575 United States Department of Physics University of California Santa BarbaraCA93106-9530 United States Department of History of Science Harvard University CambridgeMA02138 United States Department of Physics Harvard University CambridgeMA02138 United States NASA Goddard Space Flight Center GreenbeltMD20771 United States Departments of Astronomy and of Physics University of Illinois UrbanaIL61801 United States Instituto de Astrofísica de Andalucía CSIC Glorieta de la Astronomía s/n GranadaE-18008 Spain Department of Physics University of Arizona TucsonAZ85719 United States Joint Institute for VLBI ERIC Dwingeloo7991PD Netherlands Faculty of Aerospace Engineering Delft University of Technology Delft2629 HS Netherlands Department of Mathematics and Physics University of Haifa at Oranim Kiryat Tivon3600600 Israel Haifa Research Center for Theoretical Physics and Astrophysics University of Haifa Haifa3498838 Israel 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-

We present the Black Hole Explorer (BHEX), a mission that will produce the sharpest images in the history of astronomy by extending submillimeter Very-Long-Baseline Interferometry (VLBI) to space. BHEX will discover and measure the bright and narrow "photon ring" that is predicted to exist in images of black holes, produced from light that has orbited the black hole before escaping. This discovery will expose universal features of a black hole's spacetime that are distinct from the complex astrophysics of the emitting plasma, allowing the first direct measurements of a supermassive black hole's spin. In addition to studying the properties of the nearby supermassive black holes M87*and Sgr A*, BHEX will measure the properties of dozens of additional supermassive black holes, providing crucial insights into the processes that drive their creation and growth. BHEX will also connect these supermassive black holes to their relativistic jets, elucidating the power source for the brightest and most efficient engines in the universe. BHEX will address fundamental open questions in the physics and astrophysics of black holes that cannot be answered without submillimeter space VLBI. The mission is enabled by recent technological breakthroughs, including the development of ultra-high-speed downlink using laser communications, and it leverages billions of dollars of existing ground infrastructure. We present the motivation for BHEX, its science goals and associated requirements, and the pathway to launch within the next decade. © 2024, CC BY.

关键词： Photons

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The atacama cosmology telescope: Delensed power spectra and parameters

arXiv

引用

arXiv 2020年

作者： Han, Dongwon Sehgal, Neelima MacInnis, Amanda van Engelen, Alexander Sherwin, Blake D. Madhavacheril, Mathew S. Aiola, Simone Battaglia, Nicholas Beall, James A. Becker, Daniel T. Calabrese, Erminia Choi, Steve K. Darwish, Omar Denison, Edward V. Devlin, Mark J. Dunkley, Jo Ferraro, Simone Fox, Anna E. Hasselfield, Matthew Hill, J. Colin Hilton, Gene C. Hilton, Matt Hložek, Renée Hubmayr, Johannes Hughes, John P. Kosowsky, Arthur van Lanen, Jeff Louis, Thibaut Moodley, Kavilan Naess, Sigurd Namikawa, Toshiya Nati, Federico Nibarger, John P. Niemack, Michael D. Page, Lyman A. Partridge, Bruce Qu, Frank J. Schillaci, Alessandro Spergel, David N. Staggs, Suzanne Storer, Emilie Wollack, Edward J. Physics and Astronomy Department Stony Brook University Stony BrookNY11794 United States Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States School of Earth and Space Exploration Arizona State University TempeAZ85287 United States Department of Applied Mathematics and Theoretical Physics University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road CambridgeCB3 OHA United Kingdom Centre for the Universe Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Department of Astronomy Cornell University IthacaNY14853 United States NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States School of Physics and Astronomy Cardiff University The Parade Cardiff WalesCF24 3AA United Kingdom Department of Physics Cornell University IthacaNY14853 United States Department of Physics and Astronomy University of Pennsylvania 209 South 33rd Street PhiladelphiaPA19104 United States Joseph Henry Laboratories of Physics Jadwin Hall Princeton University PrincetonNJ08544 United States Berkeley Center for Cosmological Physics Department of Physics University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory One Cyclotron Road BerkeleyCA94720 United States Department of Astronomy and Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Physics Columbia University New YorkNY10027 United States Astrophysics Research Centre School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Westville Campus Durban4041 South Africa David A. Dunlap Department of Astronomy and

We present ΛCDM cosmological parameter constraints obtained from delensed microwave background power spectra. Lensing maps from a subset of DR4 data from the Atacama Cosmology Telescope (ACT) are used to undo the lensing effect in ACT spectra observed at 150 and 98 GHz. At 150 GHz, we remove the lensing distortion with an effective efficiency of 30% (TT), 30% (EE), 26% (TE) and 20% (BB);this results in detections of the delensing effect at 8.7σ (TT), 5.1σ (EE), 2.6σ (TE), and 2.4σ (BB) significance. The combination of 150 and 98 GHz TT, EE, and TE delensed spectra is well fit by a standard ΛCDM model. We also measure the shift in best-fit parameters when fitting delensed versus lensed spectra;while this shift does not inform our ability to measure cosmological parameters, it does provide a three-way consistency check among the lensing inferred from the best-fit parameters, the lensing in the CMB power spectrum, and the reconstructed lensing map. This shift is predicted to be zero when fitting with the correct model since both lensed and delensed spectra originate from the same region of sky. Fitting with a ΛCDM model and marginalizing over foregrounds, we find that the shift in cosmological parameters is consistent with zero. Our results show that gravitational lensing of the microwave background is internally consistent within the framework of the standard cosmological model. Copyright © 2020, The Authors. All rights reserved.

关键词： Power spectrum

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The atacama cosmology telescope: Summary of dr4 and dr5 data products and data access

arXiv

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arXiv 2021年

作者： Mallaby-Kay, Maya Atkins, Zachary Aiola, Simone Amodeo, Stefania Austermann, Jason E. Beall, James A. Becker, Daniel T. Bond, J. Richard Calabrese, Erminia Chesmore, Grace E. Choi, Steve K. Crowley, Kevin T. Darwish, Omar Denison, Edward V. Devlin, Mark J. Duff, Shannon M. Duivenvoorden, Adriaan J. Dunkley, Jo Ferraro, Simone Fichman, Kyra Gallardo, Patricio A. Golec, Joseph E. Guan, Yilun Han, Dongwon Hasselfield, Matthew Hill, J. Colin Hilton, Gene C. Hilton, Matt Hložek, Renée Hubmayr, Johannes Huffenberger, Kevin M. Hughes, John P. Koopman, Brian J. Louis, Thibaut MacInnis, Amanda Madhavacheril, Mathew S. McMahon, Jeff Moodley, Kavilan Naess, Sigurd Namikawa, Toshiya Nati, Federico Newburgh, Laura B. Nibarger, John P. Niemack, Michael D. Page, Lyman A. Salatino, Maria Schaan, Emmanuel Schillaci, Alessandro Sehgal, Neelima Sherwin, Blake D. Sifón, Cristóbal Simon, Sara Staggs, Suzanne T. Storer, Emilie R. Ullom, Joel N. Van Engelen, Alexander Van Lanen, Jeff Vale, Leila R. Wollack, Edward J. Xu, Zhilei Department of Astronomy University of Chicago ChicagoIL United States Joseph Henry Laboratories of Physics Jadwin Hall Princeton University PrincetonNJ08544 United States Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Astronomy Cornell University IthacaNY14853 United States NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Canadian Institute for Theoretical Astrophysics University of Toronto 60 St. George Street TorontoONM5S 3H8 Canada School of Physics and Astronomy Cardiff University The Parade CardiffCF24 3AA United Kingdom Department of Physics University of Chicago ChicagoIL60637 United States Department of Physics Cornell University IthacaNY14853 United States Department of Physics University of California Berkeley LeConte Hall BerkeleyCA94720 United States Department of Applied Mathematics and Theoretical Physics University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Department of Physics and Astronomy University of Pennsylvania 209 South 33rd Street PhiladelphiaPA19104 United States Department of Astrophysical Sciences Peyton Hall Princeton University PrincetonNJ08544 United States Lawrence Berkeley National Laboratory One Cyclotron Road BerkeleyCA94720 United States Department of Physics University of Michigan Ann ArborUSA48103 United States Department of Physics and Astronomy University of Pittsburgh PittsburghPA15260 United States Physics and Astronomy Department Stony Brook University Stony BrookNY11794 United States Department of Physics Columbia University 538 West 120th Street New YorkNY10027 United States Center for Computational Astrophysics Flatiron Institute New YorkNY10003 United States Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban4041 South Africa School of Mathematics Statistics and Computer Science University of KwaZulu-Natal We

Two recent large data releases for the Atacama Cosmology Telescope (ACT), called DR4 and DR5, are available for public access. These data include temperature and polarization maps that cover nearly half the sky at arcminute resolution in three frequency bands;lensing maps and componentseparated maps covering ∼ 2,100 deg2 of sky;derived power spectra and cosmological likelihoods;a catalog of over 4,000 galaxy clusters;and supporting ancillary products including beam functions and masks. The data and products are described in a suite of ACT papers;here we provide a summary. In order to facilitate ease of access to these data we present a set of Jupyter IPython notebooks developed to introduce users to DR4, DR5, and the tools needed to analyze these data. The data products (excluding simulations) and the set of notebooks are publicly available on the NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA);simulation products are available on the National Energy Research Scientific Computing Center (NERSC). © 2021, CC BY.

关键词： NASA

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Author Correction: Visualizing structure and transitions in high-dimensional biological data

引用

Nature biotechnology 2020年第1期38卷 108页

作者： Kevin R Moon David van Dijk Zheng Wang Scott Gigante Daniel B Burkhardt William S Chen Kristina Yim Antonia van den Elzen Matthew J Hirn Ronald R Coifman Natalia B Ivanova Guy Wolf Smita Krishnaswamy Department of Mathematics and Statistics Utah State University Logan UT USA. Cardiovascular Research Center section Cardiology Department of Internal Medicine Yale University New Haven CT USA. Department of Computer Science Yale University New Haven CT USA. School of Basic Medicine Qingdao University Qingdao China. Yale Stem Cell Center Department of Genetics Yale University New Haven CT USA. Computational Biology and Bioinformatics Program Yale University New Haven CT USA. Department of Genetics Yale University New Haven CT USA. Department of Computational Mathematics Science and Engineering Michigan State University East Lansing MI USA. Department of Mathematics Michigan State University East Lansing MI USA. Applied Mathematics Program Yale University New Haven CT USA. Department of Genetics Center for Molecular Medicine University of Georgia Athens GA USA. natalia.ivanova@uga.edu. Department of Mathematics and Statistics Université de Montréal Montréal Quebec Canada. guy.wolf@umontreal.ca. Mila-Quebec Artificial Intelligence Institute Montréal Quebec Canada. guy.wolf@umontreal.ca. Department of Computer Science Yale University New Haven CT USA. smita.krishnaswamy@yale.edu. Department of Genetics Yale University New Haven CT USA. smita.krishnaswamy@yale.edu.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： Data mining Machine learning

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