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arXiv 2019年

作者： Graham, Matthew J. Kulkarni, S.R. Bellm, Eric C. Adams, Scott M. Barbarino, Cristina Blagorodnova, Nadejda Bodewits, Dennis Bolin, Bryce Brady, Patrick R. Cenko, S. Bradley Chang, Chan-Kao Coughlin, Michael W. Kishalay, D.E. Eadie, Gwendolyn Farnham, Tony L. Feindt, Ulrich Franckowiak, Anna Fremling, Christoffer Gal-Yam, Avishay Gezari, Suvi Ghosh, Shaon Goldstein, Daniel A. Golkhou, V. Zach Goobar, Ariel Ho, Anna Y.Q. Huppenkothen, Daniela Ivezić, Željko Jones, Lynne Juric, Mario Kaplan, David L. Kasliwal, Mansi M. Kelley, Michael S.P. Kupfer, Thomas Lee, Chien-De Lin, Hsing Wen Lunnan, Ragnhild Mahabal, Ashish A. Miller, Adam A. Ngeow, Chow-Choong Nugent, Peter Ofek, Eran O. Prince, Thomas A. Rauch, Ludwig Roestel, Jan V.A.N. Schulze, Steve Singer, Leo P. Sollerman, Jesper Taddia, Francesco Lin, Y.A.N. Ye, Quan-Zhi Yu, Po-Chieh Andreoni, Igor Barlow, Tom Bauer, James Beck, Ron Belicki, Justin Biswas, Rahul Brinnel, Valery Brooke, Tim Bue, Brian Bulla, Mattia Burdge, Kevin Burruss, Rick Connolly, Andrew Cromer, John Cunningham, Virginia Dekany, Richard Delacroix, Alex Desai, Vandana Duev, Dmitry A. Feeney, Michael Flynn, David Frederick, Sara Gal-Yam, Avishay Giomi, Matteo Groom, Steven Hacopians, Eugean Hale, David Helou, George Henning, John Hover, David Hillenbrand, Lynne A. Howell, Justin Hung, Tiara Imel, David Ip, Wing-Huen Jackson, Edward Kaspi, Shai Kaye, Stephen Kowalski, Marek Kramer, Emily Kuhn, Michael Landry, Walter Laher, Russ R. Peter, M.A.O. Masci, Frank J. Monkewitz, Serge Murphy, Patrick Nordin, Jakob Patterson, Maria T. Penprase, Bryan Porter, Michael Rebbapragada, Umaa Reiley, Dan Riddle, Reed Rigault, Mickael Rodriguez, Hector Rusholme, Ben Santen, Jakob V.A.N. Shupe, David L. Smith, Roger M. Soumagnac, Maayane T. Stein, Robert Surace, Jason Szkody, Paula Terek, Scott Sistine, Angela V.A.N. Velzen, Sjoert V.A.N. Vestrand, W. Thomas Walters, Richard Ward, Charlotte Zhang, Chaoran Zolkower, Jeffry Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States DIRAC Institute Department of Astronomy University of Washington 3910 15th Avenue NE SeattleWA98195 United States The Oskar Klein Centre & Department of Astronomy Stockholm University AlbaNova StockholmSE-106 91 Sweden Department of Astronomy University of Maryland College ParkMD20742 United States Department of Physics Auburn University AuburnAL36849 United States Department of Astronomy University of Washington SeattleWA98195 United States B612 Asteroid Institute 20 Sunnyside Ave Suite 427 Mill ValleyCA94941 Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin–Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Astrophysics Science Division NASA Goddard Space Flight Center MC 661 GreenbeltMD20771 United States Joint Space-Science Institute University of Maryland College ParkMD20742 United States Institute of Astronomy National Central University 32001 Taiwan eScience Institute University of Washington SeattleWA98195 United States The Oskar Klein Centre Department of Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden Deutsches Elektronensynchrotron Platanenallee 6 ZeuthenD-15738 Germany Department of Particle Physics and Astrophysics Weizmann Institute of Science 234 Herzl St. Rehovot76100 Israel Kavli Institute for Theoretical Physics University of California Santa BarbaraCA93106 United States Department of Physics University of California Santa BarbaraCA93106 United States Department of Physics University of Michigan Ann ArborMI48109 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Center for Interdisciplinary Exploration and Research in Astrophysics Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Adler Planetarium C

The Zwicky Transient Facility (ZTF), a public-private enterprise, is a new time domain survey employing a dedicated camera on the Palomar 48-inch Schmidt telescope with a 47 deg2 field of view and 8 second readout time. It is well positioned in the development of time domain astronomy, offering operations at 10% of the scale and style of the Large Synoptic Survey Telescope (LSST) with a single 1-m class survey telescope. The public surveys will cover the observable northern sky every three nights in g and r filters and the visible Galactic plane every night in g and r. Alerts generated by these surveys are sent in real time to brokers. A consortium of universities which provided funding ("partnership") are undertaking several boutique surveys. The combination of these surveys producing one million alerts per night allows for exploration of transient and variable astrophysical phenomena brighter than r ∼ 20.5 on timescales of minutes to years. We describe the primary science objectives driving ZTF including the physics of supernovae and relativistic explosions, multi-messenger astrophysics, supernova cosmology, active galactic nuclei and tidal disruption events, stellar variability, and Solar System objects. Copyright © 2019, The Authors. All rights reserved.

关键词： Surveys

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The next decade of astroinformatics and astrostatistics

arXiv

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arXiv 2019年

作者： Siemiginowska, Aneta Eadie, Gwendolyn Czekala, Ian Feigelson, Eric Ford, Eric B. Kashyap, Vinay Kuhn, Michael Loredo, Tom Ntampaka, Michelle Stevens, Abbie Avelino, Arturo Borne, Kirk Budavari, Tamas Burkhart, Blakesley Cisewski-Kehe, Jessi Civano, Francesca Chilingarian, Igor van Dyk, David A. Fabbiano, Giuseppina Finkbeiner, Douglas P. Foreman-Mackey, Daniel Freeman, Peter Fruscione, Antonella Goodman, Alyssa A. Graham, Matthew Guenther, Hans Moritz Hakkila, Jon Hernquist, Lars Huppenkothen, Daniela James, David J. Law, Casey Lazio, Joseph Lee, Thomas López-Morales, Mercedes Mahabal, Ashish A. Mandel, Kaisey Meng, Xiao-Li Moustakas, John Muna, Demitri Peek, J.E.G. Richards, Gordon Portillo, Stephen K.N. Scargle, Jeff de Souza, Rafael S. Speagle, Joshua S. Stassun, Keivan G. Stenning, David C. Taylor, Stephen R. Tremblay, Grant R. Trimble, Virginia Yanamandra-Fisher, Padma A. Young, C. Alex Center for Astrophysics AAS Working Group on Astroinformatics and Astrostatistics eScience Institute University of Washington SeattleWA98195 United States DIRAC Institute Department of Astronomy University of Washington SeattleWA98195 United States Department of Astronomy University of Washington SeattleWA98195 United States Department of Astronomy University of California BerkeleyCA94720 United States Penn State University University ParkPA16802 United States Center for Astrophysics Harvard & Smithsonian CambridgeMA02138 United States California Institute of Technology PasadenaCA91109 United States Department of Statistical Sciences IthacaNY14853 United States Harvard University CambridgeMA02138 United States Department of Physics & Astronomy Michigan State University East LansingMI48824 United States Department of Astronomy University of Michigan Ann ArborMI48109 United States Booz Allen Hamilton Annapolis JunctionMD United States Department of Applied Mathematics & Statistics Johns Hopkins University BaltimoreMD21218 United States Department of Statistics & Data Science Yale University New HavenCT06511 United States Department of Mathematics Imperial College London SW7 2AZ United Kingdom Flatiron Institute Center for Computational Astrophysics New YorkNY10010 United States Carnegie Mellon University PittsburghPA United States Massachusetts Institute of Technology Kavli Institute for Astrophysics and Space Research CambridgeMA02139 United States Department of Physics & Astronomy Associate Dean of the Graduate School University of Charleston CharlestonSC29424 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States University of California DavisCA95616 United States TAPIR Group Division of Physics Mathematics & Astronomy California Institute of Technology PasadenaCA91125 United States University of Cambridge CambridgeCB3 0HA United Kingdom Department of Phy

Over the past century, major advances in astronomy and astrophysics have been largely driven by improvements in instrumentation and data collection. With the amassing of high quality data from new telescopes, and especially with the advent of deep and large astronomical surveys, it is becoming clear that future advances will also rely heavily on how those data are analyzed and interpreted. New methodologies derived from advances in statistics, computer science, and machine learning are beginning to be employed in sophisticated investigations that are not only bringing forth new discoveries, but are placing them on a solid footing. Progress in wide-field sky surveys, interferometric imaging, precision cosmology, exoplanet detection and characterization, and many subfields of stellar, Galactic and extragalactic astronomy, has resulted in complex data analysis challenges that must be solved to perform scientific inference. research in astrostatistics and astroinformatics will be necessary to develop the state-of-the-art methodology needed in astronomy. Overcoming these challenges requires dedicated, interdisciplinary research. We recommend: (1) increasing funding for interdisciplinary projects in astrostatistics and astroinformatics;(2) dedicating space and time at conferences for interdisciplinary research and promotion;(3) developing sustainable funding for long-term astrostatisics appointments;and (4) funding infrastructure development for data archives and archive support, state-of-the-art algorithms, and efficient computing. Copyright © 2019, The Authors. All rights reserved.

关键词： Finance

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The next decade of astroinformatics and astrostatistics

arXiv

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arXiv 2019年

作者： Siemiginowska, Aneta Eadie, Gwendolyn Czekala, Ian Feigelson, Eric Ford, Eric B. Kashyap, Vinay Kuhn, Michael Loredo, Tom Ntampaka, Michelle Stevens, Abbie Avelino, Arturo Borne, Kirk Budavari, Tamas Burkhart, Blakesley Cisewski-Kehe, Jessi Civano, Francesca Chilingarian, Igor Dyk, David A. Van Fabbiano, Giuseppina Finkbeiner, Douglas P. Foreman-Mackey, Daniel Freeman, Peter Fruscione, Antonella Goodman, Alyssa A. Graham, Matthew Guenther, Hans Moritz Hakkila, Jon Hernquist, Lars Huppenkothen, Daniela James, David J. Law, Casey Lazio, Joseph Lee, Thomas López-Morales, Mercedes Mahabal, Ashish A. Mandel, Kaisey Meng, Xiao-Li Moustakas, John Muna, Demitri Peek, J.E.G. Richard, Gordon Portillo, Stephen K.N. Scargle, Jeff De Souza, Rafael S. Speagle, Joshua S. Stassun, Keivan G. Stenning, David C. Taylor, Stephen R. Tremblay, Grant R. Trimble, Virginia Yanamandra-Fisher, Padma A. Young, C. Alex EScience Institute University of Washington SeattleWA98195 United States Department of Astronomy Dirac Institute University of Washington SeattleWA98195 United States Department of Astronomy University of Washington SeattleWA98195 United States Department of Astronomy University of California BerkeleyCA94720 United States Penn State University PA16802 United States Center for Astrophysics Harvard and Smithsonian CambridgeMA02138 United States California Institute of Technology PasadenaCA91109 United States Department of Statistical Sciences Cornell University IthacaNY14853 United States Harvard University CambridgeMA02138 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Astronomy University of Michigan Ann ArborMI48109 United States Booz Allen Hamilton Annapolis JunctionMD United States Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD21218 United States Department of Statistics and Data Science Yale University New HavenCT06511 United States Department of Mathematics Imperial College London SW7 2AZ United Kingdom Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States Carnegie Mellon University PittsburghPA United States Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics and Astronomy Inc. Dean of Graduate School University of Charleston CharlestonSC29424 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States University of California Davis CA95616 United States Division of Physics Mathematics & Astronomy Tapir Group California Institute of Technology PasadenaCA91125 United States University of Cambridge CambridgeCB3 0HA United Kingdom Department of Physics and Astronomy Siena College LoudonvilleNY12211 United States Center f

关键词： Finance

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Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics

arXiv

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arXiv 2017年

作者： Zhang, Linfeng Han, Jiequn Wang, Han Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China CAEP Software Center for High Performance Numerical Simulation Huayuan Road 6 Beijing100088 China Department of Chemistry Department of Physics Program in Applied and Computational Mathematics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Center for Data Science Beijing International Center for Mathematical Research Peking University Beijing Institute of Big Data Research Beijing100871 China

We introduce a scheme for molecular simulations, the Deep Potential Molecular Dynamics (DeePMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is "first principle-based" in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DeePMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size. Copyright © 2017, The Authors. All rights reserved.

关键词： Molecular dynamics

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Energy loss of an energetic Ga ion in hot Au plasmas

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Matter and Radiation at Extremes 2016年第5期1卷 257-263页

作者： Bin He Xujun Meng Jianguo Wang Data Center for High Energy Density Physics Research Institute of Applied Physics and Computational MathematicsBeijing 100088PR China

Self-consistent calculations of energy loss for a Ga ion moving in hot Au plasmas are made under the assumption of wide ranges of the projectile energy and the plasma temperature with all important mechanisms considered in *** relevant results are found to be quite different from those of an a particle or a *** important reason for this is the rapid increasing of the charge state of a Ga ion at plasma *** reason also leads to the inelastic stopping which does not always decrease with the increase of plasma temperature,unlike the case of an a *** nuclear stopping becomes very important at high enough plasma temperature due to the heavy reduced mass of a Ga and an Au ion and the above-mentioned *** well-known binary collision model[***.126(1962)1]and its revised one[***.A 29(1984)2145]are not working or unsatisfactory in this case.

关键词： Energy loss Hot Au plasma Ga ion

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Machine learning clustering technique applied to powder X-ray diffraction patterns to distinguish alloy substitutions

arXiv

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arXiv 2018年

作者： Utimula, Keishu Hunkao, Rutchapon Yano, Masao Kimoto, Hiroyuki Hongo, Kenta Kawaguchi, Shogo Suwanna, Sujin Maezono, Ryo School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Optical and Quantum Physics Laboratory Department of Physics faculty of Science Mahidol University Bangkok10400 Thailand Advanced Material Engineering Div. Toyota Motor Corporation Toyota-cho 1 Toyota Aichi471-8572 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan PRESTO JST Kawaguchi Saitama332-0012 Japan Sayo-gun Hyogo679-5148 Japan Asahidai 1-1 Nomi Ishikawa923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan

We applied the clustering technique using DTW (dynamic time wrapping) analysis to XRD (X-ray diffraction) spectrum patterns in order to identify the microscopic structures of substituents introduced in the main phase of magnetic alloys. The clustering is found to perform well to identify the concentrations of the substituents with successful rates (∼90%). The sufficient performance is attributed to the nature of DTW processing to filter out irrelevant informations such as the peak intensities (due to the incontrollability of diffraction conditions in polycrystalline samples) and the uniform shift of peak positions (due to the thermal expansions of lattices). The established framework is applicable not limited to the system treated in this work but widely to the systems to be tuned their properties by atomic substitutions within a phase. The framework has larger potential to predict wider properties from observed XRD patterns in a way that it can provide such properties evaluated from predicted microscopic local structure, such as magnetic moments, optical spectrum etc.). Copyright © 2018, The Authors. All rights reserved.

关键词： Cluster analysis

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On one-parametric formula relating the frequencies of twin-peak quasi-periodic oscillations

arXiv

引用

arXiv 2017年

作者： Török, Gabriel Goluchová, Kateřina Šrámková, Eva Horák, Jiří Bakala, Pavel Urbanec, Martin Institute of Physics and Research Centre for Computational Physics and Data Processing Faculty of Philosophy & Science Silesian University in Opava Bezručovo nám. 13 OpavaCZ-746 01 Czech Republic Astronomical Institute Boční II 1401/2a Praha 4 - SpořilovCZ-14131 Czech Republic

Twin-peak quasi-periodic oscillations (QPOs) are observed in several low-mass X-ray binary systems containing neutron stars (NSs). Timing analysis of X-ray fluxes of more than dozen of such systems reveals remarkable correlations between the frequencies of two characteristic peaks present in the power density spectra. The individual correlations clearly differ, but they roughly follow a common individual pattern. High values of measured QPO frequencies and strong modulation of the X-ray flux both suggest that the observed correlations are connected to orbital motion in the innermost part of an accretion disc. Several attempts to model these correlations with simple geodesic orbital models or phenomenological relations have failed in the past. We find and explore a surprisingly simple analytic relation that reproduces individual correlations for a group of several sources through a single parameter. When an additional free parameter is considered within our relation, it well reproduces the data of a large group of 14 sources. The very existence and form of this simple relation supports the hypothesis of the orbital origin of QPOs and provides the key for further development of QPO models. We discuss a possible physical interpretation of our relation’s parameters and their links to concrete QPO models. Copyright © 2017, The Authors. All rights reserved.

关键词： Stars

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Improved Upper Limit on the Neutrino Mass from a Direct Kinematic Method by KATRIN

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Physical Review Letters 2019年第22期123卷 221802-221802页

作者： M. Aker K. Altenmüller M. Arenz M. Babutzka J. Barrett S. Bauer M. Beck A. Beglarian J. Behrens T. Bergmann U. Besserer K. Blaum F. Block S. Bobien K. Bokeloh J. Bonn B. Bornschein L. Bornschein H. Bouquet T. Brunst T. S. Caldwell L. La Cascio S. Chilingaryan W. Choi T. J. Corona K. Debowski M. Deffert M. Descher P. J. Doe O. Dragoun G. Drexlin J. A. Dunmore S. Dyba F. Edzards L. Eisenblätter K. Eitel E. Ellinger R. Engel S. Enomoto M. Erhard D. Eversheim M. Fedkevych A. Felden S. Fischer B. Flatt J. A. Formaggio F. M. Fränkle G. B. Franklin H. Frankrone F. Friedel D. Fuchs A. Fulst D. Furse K. Gauda H. Gemmeke W. Gil F. Glück S. Görhardt S. Groh S. Grohmann R. Grössle R. Gumbsheimer M. Ha Minh M. Hackenjos V. Hannen F. Harms J. Hartmann N. Haußmann F. Heizmann K. Helbing S. Hickford D. Hilk B. Hillen D. Hillesheimer D. Hinz T. Höhn B. Holzapfel S. Holzmann T. Houdy M. A. Howe A. Huber T. M. James A. Jansen A. Kaboth C. Karl O. Kazachenko J. Kellerer N. Kernert L. Kippenbrock M. Kleesiek M. Klein C. Köhler L. Köllenberger A. Kopmann M. Korzeczek A. Kosmider A. Kovalík B. Krasch M. Kraus H. Krause L. Kuckert B. Kuffner N. Kunka T. Lasserre T. L. Le O. Lebeda M. Leber B. Lehnert J. Letnev F. Leven S. Lichter V. M. Lobashev A. Lokhov M. Machatschek E. Malcherek K. Müller M. Mark A. Marsteller E. L. Martin C. Melzer A. Menshikov S. Mertens L. I. Minter S. Mirz B. Monreal P. I. Morales Guzmán U. Naumann W. Ndeke H. Neumann S. Niemes M. Noe N. S. Oblath H.-W. Ortjohann A. Osipowicz B. Ostrick E. Otten D. S. Parno D. G. Phillips, II P. Plischke A. Pollithy A. W. P. Poon J. Pouryamout M. Prall F. Priester M. Röllig C. Röttele P. C.-O. Ranitzsch O. Rest R. Rinderspacher R. G. H. Robertson C. Rodenbeck P. Rohr Ch. Roll S. Rupp M. Ryšavý R. Sack A. Saenz P. Schäfer L. Schimpf K. Schlösser M. Schlösser L. Schlüter H. Schön K. Schönung M. Schrank B. Schulz J. Schwarz H. Seitz-Moskaliuk W. Seller V. Sibille D. Siegmann A. Skasyrskaya M. Slezák A. Špalek F. Spanier M. Steidl N. Steinbrink M. Sturm M. Suesser M. Sun D. Tcherniak Institute for Nuclear Physics (IKP) Karlsruhe Institute of Technology (KIT) Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen Germany Institute for Technical Physics (ITEP) Karlsruhe Institute of Technology (KIT) Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen Germany Max-Planck-Institut für Physik Föhringer Ring 6 80805 München Germany Technische Universität München James-Franck-Straße 1 85748 Garching Germany IRFU (DPhP & APC) CEA Université Paris-Saclay 91191 Gif-sur-Yvette France Helmholtz-Institut für Strahlen- und Kernphysik Rheinische Friedrich-Wilhelms-Universität Bonn Nussallee 14-16 53115 Bonn Germany Institute of Experimental Particle Physics (ETP) Karlsruhe Institute of Technology (KIT) Wolfgang-Gaede-Straße 1 76131 Karlsruhe Germany Laboratory for Nuclear Science Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge Massachusetts 02139 USA Institut für Kernphysik Westfälische Wilhelms-Universität Münster Wilhelm-Klemm-Straße 9 48149 Münster Germany Institut für Physik Johannes-Gutenberg-Universität Mainz 55099 Mainz Germany Institute for Data Processing and Electronics (IPE) Karlsruhe Institute of Technology (KIT) Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen Germany Max-Planck-Institut für Kernphysik Saupfercheckweg 1 69117 Heidelberg Germany Department of Physics and Astronomy University of North Carolina Chapel Hill North Carolina 27599 USA Triangle Universities Nuclear Laboratory Durham North Carolina 27708 USA Department of Physics Faculty of Mathematics and Natural Sciences University of Wuppertal Gaußstraße 20 42119 Wuppertal Germany Center for Experimental Nuclear Physics and Astrophysics and Department of Physics University of Washington Seattle Washington 98195 USA Nuclear Physics Institute of the CAS v. v. i. CZ-250 68 Řež Czech Republic Department of Physics Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA Institute for Nuclear Research of Russian Acade

We report on the neutrino mass measurement result from the first four-week science run of the Karlsruhe Tritium Neutrino experiment KATRIN in spring 2019. Beta-decay electrons from a high-purity gaseous molecular tritium source are energy analyzed by a high-resolution MAC-E filter. A fit of the integrated electron spectrum over a narrow interval around the kinematic end point at 18.57 keV gives an effective neutrino mass square value of (−1.0−1.1+0.9) eV2. From this, we derive an upper limit of 1.1 eV (90% confidence level) on the absolute mass scale of neutrinos. This value coincides with the KATRIN sensitivity. It improves upon previous mass limits from kinematic measurements by almost a factor of 2 and provides model-independent input to cosmological studies of structure formation.

关键词： Neutrinos

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Common Limitations of Image processing Metrics: A Picture Story

arXiv

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

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Deep learning for multi-messenger astrophysics a gateway for discovery in the big data era

arXiv

引用

arXiv 2019年

作者： Allen, Gabrielle Andreoni, Igor Bachelet, Etienne Bruce Berriman, G. Bianco, Federica B. Biswas, Rahul Kind, Matias Carrasco Chard, Kyle Cho, Minsik Cowperthwaite, Philip S. Etienne, Zachariah B. George, Daniel Gibbs, Tom Graham, Matthew Gropp, William Gupta, Anushri Haas, Roland Huerta, E.A. Jennings, Elise Katz, Daniel S. Khan, Asad Kindratenko, Volodymyr Kramer, William T.C. Liu, Xin Mahabal, Ashish McHenry, Kenton Miller, J.M. Neubauer, M.S. Oberlin, Steve Olivas, Alexander R. Rosofsky, Shawn Ruiz, Milton Saxton, Aaron Schutz, Bernard Schwing, Alex Seidel, Ed Shapiro, Stuart L. Shen, Hongyu Shen, Yue Sipocz, Brigitta M. Sun, Lunan Towns, John Tsokaros, Antonios Wei, Wei Wells, Jack Williams, Timothy J. Xiong, Jinjun Zhao, Zhizhen NCSA University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Las Cumbres Observatory 6740 Cortona Drive Suite 102 GoletaCA93117 United States Caltech/IPAC-NExScI 1200 E California Blvd PasadenaCA91125 United States Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Joseph R. Biden Jr . School of Public Policy and Administration University of Delaware NewarkDE19716 United States Data Science Institute University of Delaware NewarkDE19716 United States Center for Urban Science and Progress New York University 370 Jay St BrooklynNY11201 United States The Oskar Klein Centre for Cosmoparticle Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden University of Chicago ChicagoIL60605 United States IBM Systems AustinTX78717 United States Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Department of Mathematics West Virginia University MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95050 United States Center for Data Driven Discovery Caltech PasadenaCA91125 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Argonne National Laboratory Leadership Computing Facility LemontIL60439 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 Unit

This report provides an overview of recent work that harnesses the Big data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics;(ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data;(iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups;(iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big data Era;(v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

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