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Low-resolution face recognition in the wild via selective knowledge distillation

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arXiv 2018年

作者： Ge, Shiming Zhao, Shengwei Li, Chenyu Li, Jia Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security at University of Chinese Academy of Sciences China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Typically, the deployment of face recognition models in the wild needs to identify low-resolution faces with extremely low computational cost. To address this problem, a feasible solution is compressing a complex face model to achieve higher speed and lower memory at the cost of minimal performance drop. Inspired by that, this paper proposes a learning approach to recognize low-resolution faces via selective knowledge distillation. In this approach, a two-stream convolutional neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively. The teacher stream is represented by a complex CNN for high-accuracy recognition, and the student stream is represented by a much simpler CNN for low-complexity recognition. To avoid significant performance drop at the student stream, we then selectively distil the most informative facial features from the teacher stream by solving a sparse graph optimization problem, which are then used to regularize the fine-tuning process of the student stream. In this way, the student stream is actually trained by simultaneously handling two tasks with limited computational resources: approximating the most informative facial cues via feature regression, and recovering the missing facial cues via low-resolution face classification. Experimental results show that the student stream performs impressively in recognizing low-resolution faces and costs only 0.15MB memory and runs at 418 faces per second on CPU and 9, 433 faces per second on GPU. Copyright © 2018, The Authors. All rights reserved.

关键词： Students

Erratum: “The NANOGrav 15 yr data Set: Search for Signals from New Physics” (2023, ApJL 951 L11)

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Astrophysical Journal. Letters 2024年第1期971卷 L27页

作者： Afzal, Adeela Agazie, Gabriella Anumarlapudi, Akash Freedman, Gabriel E Islo, Kristina Jones, Megan L Kaplan, David L McEwen, Alexander Sardesai, Shashwat C Susobhanan, Abhimanyu Vigeland, Sarah J Archibald, Anne M Arzoumanian, Zaven Baker, Paul T Bécsy, Bence Burnette, Rand Case, Robin DeGan, Dallas Deng, Heling Hazboun, Jeffrey S Laal, Nima Sun, Jerry P Taylor, Jacob Jose Juan Blanco-Pillado Blecha, Laura Boddy, Kimberly K Nay, Jonathan Brazier, Adam Brook, Paul R Burke-Spolaor, Sarah Cheeseboro, Belinda D Fiore, William Fonseca, Emmanuel Garver-Daniels, Nate Gentile, Peter A Glaser, Joseph Jennings, Ross J Kaiser, Andrew R Liu, Tingting Lorimer, Duncan R McLaughlin, Maura A Turner, Jacob E Wahl, Haley M Charisi, Maria Gersbach, Kyle A Lamb, William G McMann, Natasha Petrov, Polina Pol, Nihan S Schult, Levi Taylor, Stephen R Wang, Qiaohong Chatterjee, Shami Cordes, James M Cromartie, H Thankful Stella Koch Ocker Chatziioannou, Katerina Hourihane, Sophie Lee, Vincent S H Meyers, Patrick M Chen, Siyuan Cohen, Tyler Cornish, Neil J Crawford, Fronefield Crowter, Kathryn Stairs, Ingrid H Cutler, Curt J Vallisneri, Michele DeCesar, Megan E Demorest, Paul B Stovall, Kevin Dolch, Timothy Drachler, Brendan Lam, Michael T Young, Olivia Richard von Eckardstein Schmitz, Kai Schröder, Tobias Stratmann, Peter Ferrara, Elizabeth C Good, Deborah C Guertin, Lydia Gültekin, Kayhan Johnson, Aaron D Kelley, Luke Zoltan Kerr, Matthew Ray, Paul S key, Joey S Lazio, T Joseph W Lewandowska, Natalia Rafael R Lino dos Santos Littenberg, Tyson B Luo, Jing Lynch, Ryan S Chung-Pei, Ma Madison, Dustin R McKee, James W Meyers, Bradley W Mingarelli, Chiara M F Mitridate, Andrea Natarajan, Priyamvada Ng, Cherry Nice, David J Swiggum, Joseph K Olum, Ken D Pennucci, Timothy T Perera, Benetge B P Radovan, Henri A Ransom, Scott M Romano, Joseph D Schmiedekamp, Ann Schmiedekamp, Carl Shapiro-Albert, Brent J Siemens, Xavier Simon, Joseph Siwek, Magdalena S Stinebring, Daniel R Tanner Trickle Unal, Caner Verma, Sonali Witt, Caitlin A Wright, David Zu Institute for Theoretical Physics University of Münster D-48149 Münster Germany comments@*** Department of Physics Quaid-i-Azam University Islamabad 45320 Pakistan Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 Milwaukee WI 53201 USA Newcastle University NE1 7RU UK X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 Greenbelt MD 20771 USA Department of Physics and Astronomy Widener University One University Place Chester PA 19013 USA Department of Physics Oregon State University Corvallis OR 97331 USA Department of Physics University of Basque Country UPV/EHU E-48080 Bilbao Spain EHU Quantum Center University of Basque Country UPV/EHU Bilbao Spain IKERBASQUE Basque Foundation for Science E-48011 Bilbao Spain Physics Department University of Florida Gainesville FL 32611 USA Department of Physics The University of Texas at Austin Austin TX 78712 USA Cornell Center for Astrophysics and Planetary Science and Department of Astronomy Cornell University Ithaca NY 14853 USA Cornell Center for Advanced Computing Cornell University Ithaca NY 14853 USA Institute for Gravitational Wave Astronomy and School of Physics and Astronomy University of Birmingham Edgbaston Birmingham B15 2TT UK Department of Physics and Astronomy West Virginia University P.O. Box 6315 Morgantown WV 26506 USA Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building Morgantown WV 26505 USA Department of Physics and Astronomy Vanderbilt University 2301 Vanderbilt Place Nashville TN 37235 USA Cornell Center for Astrophysics and Planetary Science and Department of Astronomy Cornell University Ithaca NY 14853 USA Division of Physics Mathematics and Astronomy California Institute of Technology Pasadena CA 91125 USA Kavli Institute for Astronomy and Astrophysics Peking University Beijing 100871 People's Republi

Study of Z boson plus jets events using variables sensitive to double-parton scattering in pp collisions at 13 TeV

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arXiv 2021年

作者： Sirunyan, A.M. Tumasyan, A. Adam, W. Bergauer, T. Dragicevic, M. Erö, J. Escalante Del Valle, A. Frühwirth, R. Jeitler, M. Krammer, N. Lechner, L. Liko, D. Madlener, T. Mikulec, I. Pitters, F.M. Rad, N. Schieck, J. Schöfbeck, R. Spanring, M. Templ, S. Waltenberger, W. Wulz, C.-E. Zarucki, M. Chekhovsky, V. Litomin, A. Makarenko, V. Suarez Gonzalez, J. Darwish, M.R. de Wolf, E.A. Di Croce, D. Janssen, X. Kello, T. Lelek, A. Pieters, M. Rejeb Sfar, H. van Haevermaet, H. van Mechelen, P. van Putte, S. van Remortel, N. Blekman, F. Bols, E.S. Chhibra, S.S. D’Hondt, J. de Clercq, J. Lontkovskyi, D. Lowette, S. Marchesini, I. Moortgat, S. Morton, A. Python, Q. Tavernier, S. van Doninck, W. van Mulders, P. Beghin, D. Bilin, B. Clerbaux, B. de Lentdecker, G. Dorney, B. Favart, L. Grebenyuk, A. Kalsi, A.K. Makarenko, I. Moureaux, L. Pétré, L. Popov, A. Postiau, N. Starling, E. Thomas, L. Vander Velde, C. Vanlaer, P. Vannerom, D. Wezenbeek, L. Cornelis, T. Dobur, D. Gruchala, M. Khvastunov, I. Niedziela, M. Roskas, C. Skovpen, K. Tytgat, M. Verbeke, W. Vermassen, B. Vit, M. Bruno, G. Bury, F. Caputo, C. David, P. Delaere, C. Delcourt, M. Donertas, I.S. Giammanco, A. Lemaitre, V. Mondal, K. Prisciandaro, J. Taliercio, A. Teklishyn, M. Vischia, P. Wertz, S. Wuyckens, S. Alves, G.A. Hensel, C. Moraes, A. Aldá Júnior, W.L. Belchior Batista Das Chagas, E. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Coelho, E. da Costa, E.M. da Silveira, G.G. de Jesus Damiao, D. Fonseca De Souza, S. Martins, J. Matos Figueiredo, D. Medina Jaime, M. Mora Herrera, C. Mundim, L. Nogima, H. Rebello Teles, P. Sanchez Rosas, L.J. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Torres Da Silva De Araujo, F. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Lemos, D.S. Mercadante, P.G. Novaes, S.F. Padula, Sandra S. Aleksandrov, A. Antchev, G. Atanasov, I. Hadjiiska, R. Iaydjiev, P. Misheva, M. Rodozov, M. Shopova, M. Sultanov, G. Bonchev, M. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Wien Austria Institute for Nuclear Problems Minsk Belarus Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista São Paulo Brazil Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China Fudan University Shanghai China Zhejiang University Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics CHEP-FU Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics University of Helsinki Helsinki Finland Helsinki Institute of Physics Helsinki Finland Lappeenranta University of Technology Lappeenranta Finland IRFU CEA Université Paris-Saclay

Double-parton scattering is investigated using events with a Z boson and jets. The Z boson is reconstructed using only the dimuon channel. The measurements are performed with proton-proton collision data recorded by the CMS experiment at the LHC at √s = 13 TeV, corresponding to an integrated luminosity of 35.9 fb−1 collected in the year 2016. Differential cross sections of Z+ ≥1 jet and Z+ ≥2 jets are measured with transverse momentum of the jets above 20 GeV and pseudorapidity |η| © 2021, CC BY.

关键词： Bosons

Sparse word embeddings using l1 regularized online learning 25

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Sparse word embeddings using l1 regularized online learning

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Sun, Fei Guo, Jiafeng Lan, Yanyan Xu, Jun Xueqi, Cheng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

Recently, Word2Vec tool has attracted a lot of interest for its promising performances in a variety of natural language processing (NLP) tasks. However, a critical issue is that the dense word representations learned in Word2Vec are lacking of interpretability. It is natural to ask if one could improve their interpretability while keeping their performances. Inspired by the success of sparse models in enhancing interpretability, we propose to introduce sparse constraint into Word2Vec. Specifically, we take the Continuous Bag of Words (CBOW) model as an example in our study and add the l regularizer into its learning objective. One challenge of optimization lies in that stochastic gradient descent (SGD) cannot directly produce sparse solutions with l regularizer in online training. To solve this problem, we employ the Regularized Dual Averaging (RDA) method, an online optimization algorithm for regularized stochastic learning. In this way, the learning process is very efficient and our model can scale up to very large corpus to derive sparse word representations. The proposed model is evaluated on both expressive power and interpretability. The results show that, compared with the original CBOW model, the proposed model can obtain state-of-the-art results with better interpretability using less than 10% non-zero elements.

关键词： Optimization

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Global climate

Bulletin of the American Meteorological Society 2020年第8期101卷 S9-S128页

作者： Dunn, R.J.H. Stanitski, D.M. Gobron, N. Willett, K.M. Met Office Hadley Centre Exeter United Kingdom NOAA/OAR Earth System Research Laboratories Boulder Colorado European Commission Joint Research Centre Ispra Italy Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Met Office Hadley Centre Exeter United Kingdom University of Reading Reading United Kingdom Department of Atmospheric and Planetary Science Hampton University Hampton Virginia NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina University of Bremen Bremen Germany NOAA/OAR Earth System Research Laboratories Boulder Colorado Centro de Investigaciones sobre Desertificación - Spanish National Research Council Moncada (Valencia) Spain and Regional Climate Group Department of Earth Sciences University of Gothenburg Gothenburg Sweden Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile NOAA/OAR ESRL Global Monitoring Laboratory Boulder Colorado Department of Civil and Environmental Engineering Princeton University Princeton New Jersey Global Precipitation Climatology Centre Deutscher Wetterdienst Offenbach Germany University of Reading Reading United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom National Oceanography Centre Southampton United Kingdom School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université de Paris Institut de physique du globe de Paris CNRS IGN Paris France and ENSG-Géomatique IGN Marne-la-Vallée France Global Modeling and Assimilation Office NASA Goddard Space Flight Center Greenbelt Maryland Sorbonne Université Paris France Universität Hamburg Hamburg Germany Department of Meteorology University of Reading Reading United Kingdom Geology Department University Centre in Svalbard Longyearbyen Norway Jet Propulsion Laboratory California Institute of Technology Wrightwood Calif

Local unitary classification for sets of generalized Bell states

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arXiv 2017年

作者： Wu, Bujiao Jiang, Jiaqing Zhang, Jialin Tian, Guojing Sun, Xiaoming CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

In this paper, we study the local unitary classification for pairs (triples) of generalized Bell states, based on the local unitary equivalence of two sets. In detail, we firstly introduce some general unitary operators which give us more local unitary equivalent sets besides Clifford operators. And then we present two necessary conditions for local unitary equivalent sets which can be used to examine the local inequivalence. Following this approach, we completely classify all of pairs in d ⊗ d quantum system into ∏nj=1(kj + 1) LU-inequivalent pairs when the prime factorization of d = ∏nj=1 pkjj . Moreover, all of triples in pα ⊗ pα quantum system for prime p can be partitioned into (α+3) 6 pα+O(αpα−1) LU-inequivalent triples, especially, when α = 2 and p > 2, there are exactly ⌊5/6 p2⌋ + ⌊p−2/6 + (−1)⌊ p/3⌋p/3 ⌋ + 3 LU-inequivalent triples. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum optics

Local articulation points in complex networks

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arXiv 2018年

作者： Yu, Senbin Gao, Liang Song, Rongqiu Institute of Transportation Systems Science and Engineering MOE Key Laboratory of Urban Transportation System Theory and Technology State Key Laboratory of Rail Traffic Control and Safety Center of Cooperative Innovation for Beijing Metropolitan Transportation Beijing Jiaotong University Beijing100044 China Yinchuan Municipal Bureau of Big Data Management and Service Yinchuan750011 China

An articulation point (AP) is any node whose removal increases the number of connected components of a graph. There is no doubt that this kind of node which occupies a non-ignorable fraction of real-world networks plays a key role in ensuring the connectivity. However, we should not thus neglect the impacts of non-APs nodes. In this paper, we define a local AP (LAP) whose removal will increase the number of connected components within its r-step neighborhood. Through investigating the fraction of LAPs in forty-five real networks, we find a critical proportion Scr, which is equal to 0.5 (S = r/D, D is the diameter of a network), and this result can also be turned out in ER networks. In addition, we present a unique advantage of LAPs in dismantling networks under the process of targeted attack, compared with APs, which provide another way of thinking to improve the calculation efficiency of APs and design better targeted attack strategy of network *** Codes Game theory, economics, social and behavioral sciences Copyright © 2018, The Authors. All rights reserved.

关键词： Complex networks

Finding the proper node ranking method for complex networks

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arXiv 2018年

作者： Yu, Senbin Gao, Liang Wang, Yi-Fan Institute of Transportation Systems Science and Engineering MOE Key Laboratory of Urban Transportation System Theory and Technology State Key Laboratory of Rail Traffic Control and Safety Center of Cooperative Innovation for Beijing Metropolitan Transportation Beijing Jiaotong University Beijing100044 China Yinchuan Municipal Bureau of Big Data Management and Service Yinchuan750011 China

Ranking node importance is crucial in understanding network structure and function on complex networks. Degree, h-index and coreness are widely used, but which one is more proper to a network associated with a dynamical process, e.g. SIR spreading process, is still unclear. To fill this gap, a, which is extracted from the fitting 1 function (f(x) = 1 − e2a(x−b)+1) of the average number of nodes in each radius of the neighborhood of a node, is proposed. Experiment results which are carried out on twenty real-world networks show that a can classify which of the three measures (degree, h-index and coreness) is more proper to a network in ranking node importance. We also find that [b/3] is a good indicator for forecasting the optimal radius of the neighborhood of a node in ranking node importance for a given network. To the best of our knowledge, it is the first solution of this interesting and open issue. Furthermore, by extending the range of neighborhood where we construct an operator ℋ on of a node, we propose a new method to quantify the importance of a node. The ranking accuracies of most networks can be improved when the radius is increased from 0 to its forecasting optimal radius and the improvement, for the best case, reaches up to 111%. The performances will reduce on half of the networks studied in this paper if we roughly extend the radius of the neighborhood. Our work deepens the understanding of how to find out the proper node ranking method for complex networks. The proposed methods bridge the gaps among network structure and node importance, and may have potential applications in controlling the outbreak of disease, designing of optimal information spreading *** Codes Game theory, economics, social and behavioral sciences Copyright © 2018, The Authors. All rights reserved.

关键词： Complex networks

First Sagittarius A* Event Horizon Telescope Results. VIII. Physical Interpretation of the Polarized Ring

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The Astrophysical Journal Letters 2024年第2期964卷 L26-L26页

作者： The Event Horizon Telescope Collaboration Kazunori Akiyama Antxon Alberdi Walter Alef Juan Carlos Algaba Richard Anantua Keiichi Asada Rebecca Azulay Uwe Bach Anne-Kathrin Baczko David Ball Mislav Baloković Bidisha Bandyopadhyay John Barrett Michi Bauböck Bradford A. Benson Dan Bintley Lindy Blackburn Raymond Blundell Katherine L. Bouman Geoffrey C. Bower Hope Boyce Michael Bremer Christiaan D. Brinkerink Roger Brissenden Silke Britzen Avery E. Broderick Dominique Broguiere Thomas Bronzwaer Sandra Bustamante Do-Young Byun John E. Carlstrom Chiara Ceccobello Andrew Chael Chi-kwan Chan Dominic O. Chang Koushik Chatterjee Shami Chatterjee Ming-Tang Chen Yongjun Chen Xiaopeng Cheng Ilje Cho Pierre Christian Nicholas S. Conroy John E. Conway James M. Cordes Thomas M. Crawford Geoffrey B. Crew Alejandro Cruz-Osorio Yuzhu Cui Rohan Dahale Jordy Davelaar Mariafelicia De Laurentis Roger Deane Jessica Dempsey Gregory Desvignes Jason Dexter Vedant Dhruv Indu K. Dihingia Sheperd S. Doeleman Sean Dougall Sergio A. Dzib Ralph P. Eatough Razieh Emami Heino Falcke Joseph Farah Vincent L. Fish Edward Fomalont H. Alyson Ford Marianna Foschi Raquel Fraga-Encinas William T. Freeman Per Friberg Christian M. Fromm Antonio Fuentes Peter Galison Charles F. Gammie Roberto García Olivier Gentaz Boris Georgiev Ciriaco Goddi Roman Gold Arturo I. Gómez-Ruiz José L. Gómez Minfeng Gu Mark Gurwell Kazuhiro Hada Daryl Haggard Kari Haworth Michael H. Hecht Ronald Hesper Dirk Heumann Luis C. Ho Paul Ho Mareki Honma Chih-Wei L. Huang Lei Huang David H. Hughes Shiro Ikeda C. M. Violette Impellizzeri Makoto Inoue Sara Issaoun David J. James Buell T. Jannuzi Michael Janssen Britton Jeter Wu Jiang Alejandra Jiménez-Rosales Michael D. Johnson Svetlana Jorstad Abhishek V. Joshi Taehyun Jung Mansour Karami Ramesh Karuppusamy Tomohisa Kawashima Garrett K. Keating Mark Kettenis Dong-Jin Kim Jae-Young Kim Jongsoo Kim Junhan Kim Motoki Kino Jun Yi Koay Prashant Kocherlakota Yutaro Kofuji Patrick M. Koch Shoko Koyama Carsten Kramer Joana A. Kramer Michael Kram Massachusetts Institute of Technology Haystack Observatory 99 Millstone Road Westford MA 01886 USA National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo 181-8588 Japan Black Hole Initiative at Harvard University 20 Garden Street Cambridge MA 02138 USA Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n E-18008 Granada Spain Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 D-53121 Bonn Germany Department of Physics Faculty of Science Universiti Malaya 50603 Kuala Lumpur Malaysia Center for Astrophysics Harvard & Smithsonian 60 Garden Street Cambridge MA 02138 USA Department of Physics & Astronomy The University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA Institute of Astronomy and Astrophysics Academia Sinica 11F of Astronomy-Mathematics Building AS/NTU No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan R.O.C. Departament d’Astronomia i Astrofísica Universitat de Valéncia C. Dr. Moliner 50 E-46100 Burjassot Valéncia Spain Observatori Astronómic Universitat de Valéncia C. Catedrático José Beltrán 2 E-46980 Paterna Valéncia Spain Department of Space Earth and Environment Chalmers University of Technology Onsala Space Observatory SE-43992 Onsala Sweden Steward Observatory and Department of Astronomy University of Arizona 933 N. Cherry Avenue Tucson AZ 85721 USA Yale Center for Astronomy & Astrophysics Yale University 52 Hillhouse Avenue New Haven CT 06511 USA Astronomy Department Universidad de Concepción Casilla 160-C Concepción Chile Department of Physics University of Illinois 1110 West Green Street Urbana IL 61801 USA Fermi National Accelerator Laboratory MS209 P.O. Box 500 Batavia IL 60510 USA Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue Chicago IL 60637 USA East Asian Observatory 660 N. A’ohoku Place Hilo HI 96720 USA James Clerk Maxwell Telescope (JCMT) 660 N. A’ohoku Place Hilo HI 96720 USA California Institute of Te

In a companion paper, we present the first spatially resolved polarized image of Sagittarius A* on event horizon scales, captured using the Event Horizon Telescope, a global very long baseline interferometric array operating at a wavelength of 1.3 mm. Here we interpret this image using both simple analytic models and numerical general relativistic magnetohydrodynamic (GRMHD) simulations. The large spatially resolved linear polarization fraction (24%–28%, peaking at ∼40%) is the most stringent constraint on parameter space, disfavoring models that are too Faraday depolarized. Similar to our studies of M87*, polarimetric constraints reinforce a preference for GRMHD models with dynamically important magnetic fields. Although the spiral morphology of the polarization pattern is known to constrain the spin and inclination angle, the time-variable rotation measure (RM) of Sgr A* (equivalent to ≈46° ± 12° rotation at 228 GHz) limits its present utility as a constraint. If we attribute the RM to internal Faraday rotation, then the motion of accreting material is inferred to be counterclockwise, contrary to inferences based on historical polarized flares, and no model satisfies all polarimetric and total intensity constraints. On the other hand, if we attribute the mean RM to an external Faraday screen, then the motion of accreting material is inferred to be clockwise, and one model passes all applied total intensity and polarimetric constraints: a model with strong magnetic fields, a spin parameter of 0.94, and an inclination of 150°. We discuss how future 345 GHz and dynamical imaging will mitigate our present uncertainties and provide additional constraints on the black hole and its accretion flow.

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