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arXiv 2021年

作者： Krachmalnicoff, N. Matsumura, T. de la Hoz, E. Basak, S. Gruppuso, A. Minami, Y. Baccigalupi, C. Komatsu, E. Martínez-González, E. Vielva, P. Aumont, J. Aurlien, R. Azzoni, S. Banday, A.J. Barreiro, R.B. Bartolo, N. Bersanelli, M. Calabrese, E. Carones, A. Casas, F.J. Cheung, K. Chinone, Y. Columbro, F. de Bernardis, P. Diego-Palazuelos, P. Errard, J. Finelli, F. Fuskeland, U. Galloway, M. Genova-Santos, R.T. Gerbino, M. Ghigna, T. Giardiello, S. Gjerløw, E. Hazumi, M. Henrot-Versillé, S. Kisner, T. Lamagna, L. Lattanzi, M. Levrier, F. Luzzi, G. Maino, D. Masi, S. Migliaccio, M. Montier, L. Morgante, G. Mot, B. Nagata, R. Nati, F. Natoli, P. Pagano, L. Paiella, A. Paoletti, D. Patanchon, G. Piacentini, F. Polenta, G. Poletti, D. Puglisi, G. Remazeilles, M. Rubino-Martin, J. Sasaki, M. Shiraishi, M. Signorelli, G. Stever, S. Tartari, A. Tristram, M. Tsuji, M. Vacher, L. Wehus, I.K. Zannoni, M. Via Bonomea 265 Trieste34136 Italy INFN Sezione di Trieste via Valerio 2 Trieste34127 Italy IFPU Via Beirut 2 Trieste Grignano34151 Italy UTIAS The University of Tokyo Chiba Kashiwa277-8583 Japan Avenida los Castros SN Santander39005 Spain Dpto. de Física Moderna Universidad de Cantabria Avda. los Castros s/n SantanderE-39005 Spain School of Physics Indian Institute of Science Education and Research Thiruvananthapuram Maruthamala PO Vithura Kerala Thiruvananthapuram695551 India INAF - OAS Bologna via Piero Gobetti 93/3 Bologna40129 Italy INFN Sezione di Bologna Viale C. Berti Pichat 6/2 Bologna40127 Italy Research Center for Nuclear Physics Osaka University Osaka Ibaraki567-0047 Japan Max-Planck-Institute for Astrophysics Garching Germany IRAP Université de Toulouse CNRS CNES UPS Toulouse France Institute of Theoretical Astrophysics University of Oslo Blindern Oslo Norway Department of Physics University of Oxford Denys Wilkinson Building Keble Road OxfordOX1 3RH United Kingdom Dipartimento di Fisica e Astronomia "G. Galilei" Universita'degli Studi di Padova via Marzolo 8 PadovaI-35131 Italy INFN Sezione di Padova via Marzolo 8 PadovaI-35131 Italy Dipartimento di Fisica Universita' degli Studi di Milano Via Celoria 16 Milano20133 Italy INFN Sezione di Milano Via Celoria 16 Milano20133 Italy Cardiff University School of Physics and Astronomy CardiffCF10 3XQ United Kingdom Dipartimento di Fisica Università di Roma Tor Vergata Via della Ricerca Scientifica 1 Roma00133 Italy INFN Sezione di Roma2 Università di Roma Tor Vergata via della Ricerca Scientifica 1 Roma00133 Italy University of California Berkeley Department of Physics BerkeleyCA94720 United States University of California Berkeley Space Science Laboratory BerkeleyCA94720 United States Computational Cosmology Center BerkeleyCA94720 United States University of Tokyo School of Science Research Center for the Early Universe RESCEU Jap

We present a demonstration of the in-flight polarization angle calibration for the JAXA/ISAS second strategic large class mission, LiteBIRD, and estimate its impact on the measurement of the tensor-to-scalar ratio parameter, r, using simulated data. We generate a set of simulated sky maps with CMB and polarized foreground emission, and inject instrumental noise and polarization angle offsets to the 22 (partially overlapping) LiteBIRD frequency channels. Our in-flight angle calibration relies on nulling the EB cross correlation of the polarized signal in each channel. This calibration step has been carried out by two independent groups with a blind analysis, allowing an accuracy of the order of a few arc-minutes to be reached on the estimate of the angle offsets. Both the corrected and uncorrected multi-frequency maps are propagated through the foreground cleaning step, with the goal of computing clean CMB maps. We employ two component separation algorithms, the Bayesian-Separation of Components and Residuals Estimate Tool (B-SeCRET), and the Needlet Internal Linear Combination (NILC). We find that the recovered CMB maps obtained with algorithms that do not make any assumptions about the foreground properties, such as NILC, are only mildly affected by the angle miscalibration. However, polarization angle offsets strongly bias results obtained with the parametric fitting method. Once the miscalibration angles are corrected by EB nulling prior to the component separation, both component separation algorithms result in an unbiased estimation of the r parameter. While this work is motivated by the conceptual design study for LiteBIRD, its framework can be broadly applied to any CMB polarization experiment. In particular, the combination of simulation plus blind analysis provides a robust forecast by taking into account not only detector sensitivity but also systematic effects. Copyright © 2021, The Authors. All rights reserved.

关键词： Conceptual design

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End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems

arXiv

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arXiv 2018年

作者： Zhang, Linfeng Han, Jiequn Wang, Han Saidi, Wissam A. Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University United States Institute of Applied Physics and Computational Mathematics China CAEP Software Center for High Performance Numerical Simulation China Department of Mechanical Engineering and Materials Science University of Pittsburgh United States Department of Chemistry and Department of Physics Princeton University United States Princeton Institute for the Science and Technology of Materials Princeton University United States Department of Mathematics Princeton University United States Beijing Institute of Big Data Research China

Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES of a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity. Copyright © 2018, The Authors. All rights reserved.

关键词： Potential energy

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Vortex patterns and the critical rotational frequency in rotating dipolar Bose-Einstein condensates

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Physical Review A 2018年第2期98卷 023610-023610页

作者： Yongyong Cai Yongjun Yuan Matthias Rosenkranz Han Pu Weizhu Bao Beijing Computational Science Research Center Haidian District Beijing 100193 People's Republic of China Key Laboratory of High Performance Computing and Stochastic Information Processing (Ministry of Education of China) College of Mathematics and Statistics Hunan Normal University Changsha Hunan 410081 People's Republic of China Department of Mathematics National University of Singapore 119076 Singapore Department of Physics and Astronomy and Rice Quantum Institute Rice University Houston Texas 77251 USA

Based on the two-dimensional mean-field equations for pancake-shaped dipolar Bose-Einstein condensates in a rotating frame, for both attractive and repulsive dipole-dipole interaction (DDI) as well as arbitrary polarization direction, we study the profiles of the single vortex state and show how the critical rotational frequency changes with the s-wave contact interaction strength, DDI strength, and polarization angle. In addition, we find numerically that at the “magic angle” ϑ=arccos(3/3), the critical rotational frequency is almost independent of the DDI strength. By numerically solving the dipolar Gross-Pitaevskii equation at high rotation speed, we identify different patterns of vortex lattices which strongly depend on the polarization direction. As a result, we undergo a study of vortex lattice structures for the whole regime of polarization direction and find evidence that the vortex lattice orientation tends to be aligned to the dipole polarization axis for positive DDI strength and to the perpendicular direction of the dipole axis for negative DDI strength.

关键词： Bose-Einstein condensates Dipolar gases

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab 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 Heidelberg Division of Medical Image Computing 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

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Weak doping dependence of the antiferromagnetic coupling between nearest-neighbor Mn2+ spins in (Ba1−xKx)(Zn1−yMny)2As2

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Physical Review B 2018年第10期97卷 104418-104418页

作者： M. A. Surmach B. J. Chen Z. Deng C. Q. Jin J. K. Glasbrenner I. I. Mazin A. Ivanov D. S. Inosov Institut für Festkörper- und Materialphysik TU Dresden D-01069 Dresden Germany Institute of Physics Chinese Academy of Sciences 100190 Beijing China Center for High Pressure Science and Technology Advanced Research Beijing 100094 China School of Physics University of Chinese Academy of Sciences Beijing 100049 China Collaborative Innovation Center of Quantum Matter Beijing 100049 China Department of Computational and Data Sciences/Center for Simulation and Modeling George Mason University 4400 University Drive Fairfax Virginia 22030 USA Code 6393 Naval Research Laboratory Washington DC 20375 USA Institut Laue-Langevin 71 avenue des Martyrs CS 20156 F-38042 Grenoble Cedex 9 France

Dilute magnetic semiconductors (DMS) are nonmagnetic semiconductors doped with magnetic transition metals. The recently discovered DMS material (Ba1−xKx)(Zn1−yMny)2As2 offers a unique and versatile control of the Curie temperature TC by decoupling the spin (Mn2+, S=5/2) and charge (K+) doping in different crystallographic layers. In an attempt to describe from first-principles calculations the role of hole doping in stabilizing ferromagnetic order, it was recently suggested that the antiferromagnetic exchange coupling J between the nearest-neighbor Mn ions would experience a nearly twofold suppression upon doping 20% of holes by potassium substitution. At the same time, further-neighbor interactions become increasingly ferromagnetic upon doping, leading to a rapid increase of TC. Using inelastic neutron scattering, we have observed a localized magnetic excitation at about 13 meV associated with the destruction of the nearest-neighbor Mn-Mn singlet ground state. Hole doping results in a notable broadening of this peak, evidencing significant particle-hole damping, but with only a minor change in the peak position. We argue that this unexpected result can be explained by a combined effect of superexchange and double-exchange interactions.

关键词： Ferromagnetism Magnetic semiconductors Inelastic neutron scattering

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DeePCG: Constructing coarse-grained models via deep neural networks

arXiv

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arXiv 2018年

作者： Weinan, E. Zhang, Linfeng Han, Jiequn Wang, Han Car, Roberto 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 Program in Applied and Computational Mathematics Princeton Institute for 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 Beijing Institute of Big Data Research Beijing100871 China

We introduce a general framework for constructing coarse-grained potential models without ad hoc approximations such as limiting the potential to two- A nd/or three-body contributions. The scheme, called Deep Coarse-Grained Potential (abbreviated DeePCG), exploits a carefully crafted neural network to construct a many-body coarse-grained potential. The network is trained with full atomistic data in a way that preserves the natural symmetries of the system. The resulting model is very accurate and can be used to sample the configurations of the coarse-grained variables in a much faster way than with the original atomistic model. As an application we consider liquid water and use the oxygen coordinates as the coarse-grained variables, starting from a full atomistic simulation of this system at the ab initio molecular dynamics level. We find that the two-body, three-body, and higher-order oxygen correlation functions produced by the coarse-grained and full atomistic models agree very well with each other, illustrating the effectiveness of the DeePCG model on a rather challenging task. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Collapsed tetragonal phase as a strongly covalent and fully nonmagnetic state: Persistent magnetism with interlayer As–As bond formation in Rh-doped Ca0.8Sr0.2Fe2As2

引用

Physical Review B 2018年第2期97卷 020510(R)-020510(R)页

作者： K. Zhao J. K. Glasbrenner H. Gretarsson D. Schmitz J. Bednarcik M. Etter J. P. Sun R. S. Manna A. Al-Zein S. Lafuerza W. Scherer J. G. Cheng P. Gegenwart Experimentalphysik VI Center for Electronic Correlations and Magnetism University of Augsburg 86159 Augsburg Germany Department of Computational and Data Sciences Computational Materials Science Center George Mason University 4400 University Drive Fairfax Virginia 22030 USA National Research Council/Code 6393 Naval Research Laboratory Washington DC 20375 USA Max-Planck-Institut für Festkörperforschung Heisenbergstraße 1 D-70569 Stuttgart Germany CPM Institute of Physics University of Augsburg 86135 Augsburg Germany Deutsches Elektronen-Synchrotron (DESY) 22607 Hamburg Germany Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing 100190 China European Synchrotron Radiation Facility Boîte Postale 220 F-38043 Grenoble Cedex France

A well-known feature of the CaFe2As2-based superconductors is the pressure-induced collapsed tetragonal phase that is commonly ascribed to the formation of an interlayer As–As bond. Using detailed x-ray scattering and spectroscopy, we find that Rh-doped Ca0.8Sr0.2Fe2As2 does not undergo a first-order phase transition and that local Fe moments persist despite the formation of interlayer As–As bonds. Our density functional theory calculations reveal that the Fe–As bond geometry is critical for stabilizing magnetism and the pressure-induced drop in the c lattice parameter observed in pure CaFe2As2 is mostly due to a constriction within the FeAs planes. The collapsed tetragonal phase emerges when covalent bonding of strongly hybridized Fe 3d and As 4p states completely wins out over their exchange splitting. Thus the collapsed tetragonal phase is properly understood as a strong covalent phase that is fully nonmagnetic with the As–As bond forming as a by-product.

关键词： Superconductivity Pnictides X-ray scattering

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The gravity collective: A search for the electromagnetic counterpart to the neutron star-black hole merger GW190814

arXiv

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arXiv 2021年

作者： Kilpatrick, Charles D. Coulter, David A. Arcavi, Iair Brink, Thomas G. Dimitriadis, Georgios Filippenko, Alexei V. Foley, Ryan J. Howell, D. Andrew Jones, David O. Makler, Martin Piro, Anthony L. Rojas-Bravo, César Sand, David J. Swift, Jonathan J. Tucker, Douglas Zheng, Weikang Allam, Sahar S. Annis, James T. Antilen, Juanita Bachmann, Tristan G. Bloom, Joshua S. Bom, Clecio R. Bostroem, K. Azalee Brout, Dillon Burke, Jamison Butler, Robert E. Butner, Melissa Campillay, Abdo Clever, Karoli E. Conselice, Christopher J. Cooke, Jeff Dage, Kristen C. de Carvalho, Reinaldo R. Jaeger, Thomas D.E. Desai, Shantanu Garcia, Alyssa Garcia-Bellido, Juan Gill, Mandeep S.S. Girish, Nachiket Hallakoun, Na'Ama Herner, Kenneth Hiramatsu, Daichi Holz, Daniel E. Huber, Grace Kawash, Adam M. McCully, Curtis Medallon, Sophia A. Metzger, Brian D. Modak, Shaunak Morgan, Robert Muñoz, Ricardo R. Muñoz-Elgueta, Nahir Murakami, Yukei S. Olivares, Felipe E. Palmese, Antonella Patra, Kishore Pereira, Maria E.S. Pessi, Thallis L. Pineda-Garcia, J. Quirola-Vásquez, Jonathan Ramirez-Ruiz, Enrico Rembold, Sandro Barboza Rest, Armin Rodríguez, Ósmar Santana-Silva, Luidhy Sherman, Nora F. Siebert, Matthew R. Smith, Carli Smith, J. Allyn Soares-Santos, Marcelle Stacey, Holland Stahl, Benjamin E. Strader, Jay Strasburger, Erika Sunseri, James Tinyanont, Samaporn Tucker, Brad E. Ulloa, Natalie Valenti, Stefano Vasylyev, Sergiy Wiesner, Matthew P. Zhang, Keto D. Northwestern University EvanstonIL60208 United States Department of Astronomy and Astrophysics University of California Santa CruzCA95064 United States The School of Physics and Astronomy Tel Aviv University Tel Aviv69978 Israel CIFAR Azrieli Global Scholars program CIFAR Toronto Canada Department of Astronomy University of California BerkeleyCA94720-3411 United States Miller Institute for Basic Research in Science University of California BerkeleyCA94720 United States Department of Physics University of California Santa BarbaraCA93106-9530 United States Las Cumbres Observatory 6740 Cortona Dr Suite 102 GoletaCA93117-5575 United States International Center for Advanced Studies & Instituto de Ciencias Físicas ECyT-UNSAM & CONICET Buenos Aires1650 Argentina Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 Rio de Janeiro RJCEP 22290-180 Brazil The Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Steward Observatory University of Arizona 933 North Cherry Avenue TucsonAZ85721-0065 United States Thacher Observatory Thacher School 5025 Thacher Rd. OjaiCA93023 United States Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Departamento de Astronomía Universidad de Chile Camino El Observatorio 1515 Las Condes Santiago Chile Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road MS 50B-4206 BerkeleyCA94720-3411 United States Centro Federal de Educação Tecnológica Celso Suckow da Fonseca Rodovia Mário Covas lote J2 quadra J Itaguaí RJCEP 23810-000 Brazil Department of Physics and Astronomy University of California DavisCA95616 United States Center for Astrophysics Harvard & Smithsonian 60 Garden St CambridgeMA02138 United States Department of Astronomy Indiana University 727 E. Third St. BloomingtonIN47405 United States Dep

We present optical follow-up imaging obtained with the Katzman Automatic Imaging Telescope, Las Cumbres Observatory Global Telescope Network, Nickel Telescope, Swope Telescope, and Thacher Telescope of the LIGO/Virgo gravitational wave (GW) signal from the neutron star–black hole (NSBH) merger GW190814. We searched the GW190814 localization region (19 deg2 for the 90th percentile best localization), covering a total of 51 deg2 and 94.6% of the two-dimensional localization region. Analyzing the properties of 189 transients that we consider as candidate counterparts to the NSBH merger, including their localizations, discovery times from merger, optical spectra, likely host-galaxy redshifts, and photometric evolution, we conclude that none of these objects are likely to be associated with GW190814. Based on this finding, we consider the likely optical properties of an electromagnetic counterpart to GW190814, including possible kilonovae and short gamma-ray burst afterglows. Using the joint limits from our follow-up imaging, we conclude that a counterpart with an rband decline rate of 0.68 mag day−1, similar to the kilonova AT 2017gfo, could peak at an absolute magnitude of at most −17.8 mag (50% confidence). Our data are not constraining for "red" kilonovae and rule out "blue" kilonovae with M > 0.5 M* (30% confidence). We strongly rule out all known types of short gamma-ray burst afterglows with viewing angles ◦ assuming an initial jet opening angle of ∼ 5.2◦ and explosion energies and circumburst densities similar to afterglows explored in the literature. Finally, we explore the possibility that GW190814 merged in the disk of an active galactic nucleus, of which we find four in the localization region, but we do not find any candidate counterparts among these sources. © 2021, CC BY.

关键词： Stars

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COSMOS2020: A panchromatic view of the universe to z ∼ 10 from two complementary catalogs

arXiv

引用

arXiv 2021年

作者： Weaver, John R. Kauffmann, O.B. Ilbert, O. McCracken, H.J. Moneti, A. Toft, S. Brammer, G. Shuntov, M. Davidzon, I. Hsieh, B.C. Laigle, C. Anastasiou, A. Jespersen, C.K. Vinther, J. Capak, P. Casey, C.M. McPartland, C.J.R. Milvang-Jensen, B. Mobasher, B. Sanders, D.B. Zalesky, L. Arnouts, S. Aussel, H. Dunlop, J.S. Faisst, A. Franx, M. Furtak, L.J. Fynbo, J.P.U. Gould, K.M.L. Greve, T.R. Gwyn, S. Kartaltepe, J.S. Kashino, D. Koekemoer, A.M. Kokorev, V. Le Fèvre, O. Lilly, S. Masters, D. Magdis, G. Mehta, V. Peng, Y. Riechers, D.A. Salvato, M. Sawicki, M. Scarlata, C. Scoville, N. Shirley, R. Sneppen, A. Smolčić, V. Steinhardt, C. Stern, D. Tanaka, M. Taniguchi, Y. Teplitz, H.I. Vaccari, M. Wang, W.-H. Zamorani, G. Niels Bohr Institute University of Copenhagen Jagtvej 128 Copenhagen2200 Denmark Aix Marseille Univ CNRS LAM Laboratoire d’Astrophysique de Marseille Marseille France Institut d’Astrophysique de Paris UMR 7095 CNRS Sorbonne Université 98 bis boulevard Arago Paris75014 France Institute of Astrophysics & Astronomy Academia Sinica Taipei10617 Taiwan The University of Texas at Austin 2515 Speedway Blvd Stop C1400 AustinTX78712 United States Institute for Astronomy University of Hawaii 2680 Woodlawn Drive HonoluluHI96822 United States Department of Physics and Astronomy University of California Riverside 900 University Avenue RiversideCA92521 United States AIM UMR 7158 CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité Gif-sur-YvetteF-91191 France Institute for Astronomy University of Edinburgh Royal Observatory EdinburghEH9 3HJ United Kingdom Infrared Processing and Analysis Center California Institute of Technology 1200 E. California Blvd PasadenaCA91125 United States Leiden Observatory Leiden University P.O.Box 9513 LeidenNL-2300 AA Netherlands DTU-Space Technical University of Denmark Elektrovej 327 Kgs. LyngbyDK2800 Denmark Department of Physics and Astronomy University College London Gower Place LondonWC1E 6BT United Kingdom Herzberg Astronomy and Astrophysics National Research Council of Canada 5071 West Saanich Rd. VictoriaBCV9E 2E7 Canada School of Physics and Astronomy Institute of Technology 84 Lomb Memorial Drive RochesterNY14623 United States Department of Physics ETH Zürich Wolfgang-Pauli-Strasse 27 Zurich8093 Switzerland Institute for Advanced Research Nagoya University Furocho Chikusa Aichi Nagoya464-8602 Japan Space Telescope Science Institute 3700 San Martin Dr. BaltimoreMD21218 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States Institute for Astronomy Astrophysics Space Applications and Remo

The Cosmic Evolution Survey (COSMOS) has become a cornerstone of extragalactic astronomy. Since the last public catalog in 2015, a wealth of new imaging and spectroscopic data has been collected in the COSMOS field. This paper describes the collection, processing, and analysis of this new imaging data to produce a new reference photometric redshift catalog. Source detection and multi-wavelength photometry is performed for 1.7 million sources across the 2 deg2 of the COSMOS field, ∼966 000 of which are measured with all available broad-band data using both traditional aperture photometric methods and a new profile-fitting photometric extraction tool, The Farmer, which we have developed. A detailed comparison of the two resulting photometric catalogs is presented. Photometric redshifts are computed for all sources in each catalog utilizing two independent photometric redshift codes. Finally, a comparison is made between the performance of the photometric methodologies and of the redshift codes to demonstrate an exceptional degree of self-consistency in the resulting photometric redshifts. The i © 2021, CC BY.

关键词： Photometry

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The leap to ordinal: Detailed functional prognosis after traumatic brain injury with a flexible modelling approach

arXiv

引用

arXiv 2022年

作者： Bhattacharyay, Shubhayu Milosevic, Ioan Wilson, Lindsay Menon, David K. Stevens, Robert D. Steyerberg, Ewout W. Nelson, David W. Ercole, Ari Åkerlund, Cecilia Amrein, Krisztina Andelic, Nada Andreassen, Lasse Anke, Audny Antoni, Anna Audibert, Gérard Azouvi, Philippe Azzolini, Maria Luisa Bartels, Ronald Barzó, Pál Beauvais, Romuald Beer, Ronny Bellander, Bo-Michael Belli, Antonio Benali, Habib Berardino, Maurizio Beretta, Luigi Blaabjerg, Morten Bragge, Peter Brazinova, Alexandra Brinck, Vibeke Brooker, Joanne Brorsson, Camilla Buki, Andras Bullinger, Monika Cabeleira, Manuel Caccioppola, Alessio Calappi, Emiliana Calvi, Maria Rosa Cameron, Peter Lozano, Guillermo Carbayo Carbonara, Marco Cavallo, Simona Chevallard, Giorgio Chieregato, Arturo Citerio, Giuseppe Clusmann, Hans Coburn, Mark Coles, Jonathan Cooper, Jamie D. Correia, Marta Čović, Amra Curry, Nicola Czeiter, Endre Czosnyka, Marek Dahyot-Fizelier, Claire Dark, Paul Dawes, Helen De Keyser, Véronique Degos, Vincent Della Corte, Francesco den Boogert, Hugo Depreitere, Bart Đilvesi, Đula Dixit, Abhishek Donoghue, Emma Dreier, Jens Dulière, Guy-Loup Esser, Patrick Ezer, Erzsébet Fabricius, Martin Feigin, Valery L. Foks, Kelly Frisvold, Shirin Furmanov, Alex Gagliardo, Pablo Galanaud, Damien Gantner, Dashiell Gao, Guoyi George, Pradeep Ghuysen, Alexandre Giga, Lelde Glocker, Ben Golubovic, Jagoš Gomez, Pedro A. Gratz, Johannes Gravesteijn, Benjamin Grossi, Francesca Gruen, Russell L. Gupta, Deepak Haagsma, Juanita A. Haitsma, Iain Helbok, Raimund Helseth, Eirik Horton, Lindsay Huijben, Jilske Hutchinson, Peter J. Jacobs, Bram Jankowski, Stefan Jarrett, Mike Jiang, Ji-Yao Johnson, Faye Jones, Kelly Karan, Mladen Kolias, Angelos G. Kompanje, Erwin Kondziella, Daniel Kornaropoulos, Evgenios Koskinen, Lars-Owe Kovács, Noémi Kowark, Ana Lagares, Alfonso Lanyon, Linda Laureys, Steven Lecky, Fiona Ledoux, Didier Lefering, Rolf Legrand, Valerie Lejeune, Aurelie Levi, Leon Lightfoot, Roger Lingsma, Hester Maas, Andrew I.R. Castaño-León, Ana M. Maegele, Marc Majdan, M Division of Anaesthesia University of Cambridge Addenbrooke's Hospital Cambridge United Kingdom Division of Neurosurgery Department of Clinical Neurosciences Addenbrooke's Hospital University of Cambridge Cambridge United Kingdom Laboratory of Computational Intensive Care Medicine Johns Hopkins University BaltimoreMD United States Division of Psychology University of Stirling Stirling United Kingdom Department of Anesthesiology and Critical Care Medicine Johns Hopkins University BaltimoreMD United States Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Department of Physiology and Pharmacology Section for Perioperative Medicine and Intensive Care Karolinska Institutet Stockholm Sweden Cambridge Centre for Artificial Intelligence in Medicine Cambridge United Kingdom Division of Surgery and Clinical Neuroscience Department of Physical Medicine and Rehabilitation Oslo University Hospital University of Oslo Oslo Norway Department of Neurosurgery University Hospital Northern Norway Tromso Norway Department of Physical Medicine and Rehabilitation University Hospital Northern Norway Tromso Norway Trauma Surgery Medical University Vienna Vienna Austria Department of Anesthesiology & Intensive Care University Hospital Nancy Nancy France Raymond Poincare Hospital Assistance Publique - Hopitaux de Paris Paris France Department of Anesthesiology & Intensive Care S Raffaele University Hospital Milan Italy Department of Neurosurgery Radboud University Medical Center Nijmegen Netherlands Department of Neurosurgery University of Szeged Szeged Hungary International Projects Management ARTTIC Munchen Germany Department of Neurology Neurological Intensive Care Unit Medical University of Innsbruck Innsbruck Austria Department of Neurosurgery & Anesthesia & Intensive Care Medicine Karolinska University Hospital Stockholm Sweden NIHR Surgical Reconstruction and Microbiology Research Centre Birmingham United Kingdom Anesthesi

When a patient is admitted to the intensive care unit (ICU) after a traumatic brain injury (TBI), an early prognosis is essential for baseline risk adjustment and shared decision making. TBI outcomes are commonly categorised by the Glasgow Outcome Scale - Extended (GOSE) into eight, ordered levels of functional recovery at 6 months after injury. Existing ICU prognostic models predict binary outcomes at a certain threshold of GOSE (e.g., prediction of survival [GOSE > 1]). We aimed to develop ordinal prediction models that concurrently predict probabilities of each GOSE score. From a prospective cohort (n = 1,550, 65 centres) in the ICU stratum of the Collaborative European NeuroTrauma Effectiveness research in TBI (center-TBI) patient dataset, we extracted all clinical information within 24 hours of ICU admission (1,151 predictors) and 6-month GOSE scores. We analysed the effect of two design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of ten validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning. With repeated k-fold cross-validation, we found that expanding the baseline predictor set significantly improved ordinal prediction performance while increasing analytical complexity did not. Half of these gains could be achieved with the addition of eight high-impact predictors to the concise set. At best, ordinal models achieved 0.76 (95% CI: 0.74 - 0.77) ordinal discrimination ability (ordinal c-index) and 57% (95% CI: 54% - 60%) explanation of ordinal variation in 6-month GOSE (Somers' Dxy). Model performance and the effect of expanding the predictor set decreased at higher GOSE thresholds, indicating the difficulty of predicting better functional outcomes shortly after ICU admission. Our results motivate the search for informative predictors that improve confidence in prognosis of higher GOSE a

关键词： Neural network models

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