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arXiv 2023年

作者： Coulombe, Louis-Philippe Benneke, Björn Challener, Ryan Piette, Anjali A.A. Wiser, Lindsey S. Mansfield, Megan MacDonald, Ryan J. Beltz, Hayley Feinstein, Adina D. Radica, Michael Savel, Arjun B. Dos Santos, Leonardo A. Bean, Jacob L. Parmentier, Vivien Wong, Ian Rauscher, Emily Komacek, Thaddeus D. Kempton, Eliza M.-R. Tan, Xianyu Hammond, Mark Lewis, Neil T. Line, Michael R. Lee, Elspeth K.H. Shivkumar, Hinna Crossfield, Ian J.M. Nixon, Matthew C. Rackham, Benjamin V. Wakeford, Hannah R. Welbanks, Luis Zhang, Xi Batalha, Natalie M. Berta-Thompson, Zachory K. Changeat, Quentin Désert, Jean-Michel Espinoza, Néstor Goyal, Jayesh M. Harrington, Joseph Knutson, Heather A. Kreidberg, Laura López-Morales, Mercedes Shporer, Avi Sing, David K. Stevenson, Kevin B. Aggarwal, Keshav Ahrer, Eva-Maria Alam, Munazza K. Bell, Taylor J. Blecic, Jasmina Caceres, Claudio Carter, Aarynn L. Casewell, Sarah L. Crouzet, Nicolas Cubillos, Patricio E. Decin, Leen Fortney, Jonathan J. Gibson, Neale P. Heng, Kevin Henning, Thomas Iro, Nicolas Kendrew, Sarah Lagage, Pierre-Olivier Leconte, Jérémy Lendl, Monika Lothringer, Joshua D. Mancini, Luigi Mikal-Evans, Thomas Molaverdikhani, Karan Nikolov, Nikolay K. Ohno, Kazumasa Palle, Enric Piaulet, Caroline Redfield, Seth Roy, Pierre-Alexis Tsai, Shang-Min Venot, Olivia Wheatley, Peter J. Department of Physics Trottier Institute for Research on Exoplanets Université de Montréal MontréalQC Canada Department of Astronomy University of Michigan Ann ArborMI United States Earth and Planets Laboratory Carnegie Institution for Science WashingtonDC United States School of Earth and Space Exploration Arizona State University TempeAZ United States Steward Observatory University of Arizona TucsonAZ United States NHFP Sagan Fellow United States Department of Astronomy Carl Sagan Institute Cornell University NY United States Department of Astronomy and Astrophysics University of Chicago ChicagoIL United States NSF Graduate Research Fellow United States Department of Astronomy University of Maryland College ParkMD United States Center for Computational Astrophysics Flatiron Institute New YorkNY United States Space Telescope Science Institute BaltimoreMD United States Université Côte d’Azur Observatoire de la Côte d’Azur CNRS Laboratoire Lagrange France NASA Goddard Space Flight Center GreenbeltMD United States NASA Postdoctoral Program Fellow United States Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China Atmospheric Oceanic and Planetary Physics Department of Physics University of Oxford Oxford United Kingdom Department of Mathematics and Statistics Faculty of Environment Science and Economy University of Exeter Exeter United Kingdom Center for Space and Habitability University of Bern Bern Switzerland Anton Pannekoek Institute for Astronomy University of Amsterdam Amsterdam Netherlands Department of Physics & Astronomy University of Kansas LawrenceKS United States Department of Earth Atmospheric and Planetary Sciences Massachusetts Institute of Technology CambridgeMA United States Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA United States 51 Pegasi b Fellow France School of

Close-in giant exoplanets with temperatures greater than 2,000 K ("ultra-hot Jupiters") have been the subject of extensive efforts to determine their atmospheric properties using thermal emission measurements from the Hubble and Spitzer Space Telescopes1;2;3. However, previous studies have yielded inconsistent results because the small sizes of the spectral features and the limited information content of the data resulted in high sensitivity to the varying assumptions made in the treatment of instrument systematics and the atmospheric retrieval analysis3;4;5;6;7;8;9;10;11;12. Here we present a dayside thermal emission spectrum of the ultrahot Jupiter WASP-18b obtained with the NIRISS13 instrument on JWST. The data span 0.85 to 2.85 µm in wavelength at an average resolving power of 400 and exhibit minimal systematics. The spectrum shows three water emission features (at >6σ confidence) and evidence for optical opacity, possibly due to H−, TiO, and VO (combined significance of 3.8σ). Models that fit the data require a thermal inversion, molecular dissociation as predicted by chemical equilibrium, a solar heavy element abundance ("metallicity", M/H = 1.03+1.11−0.51 × solar), and a carbon-to-oxygen (C/O) ratio less than unity. The data also yield a dayside brightness temperature map, which shows a peak in temperature near the sub-stellar point that decreases steeply and symmetrically with longitude toward the terminators. © 2023, CC BY.

关键词： Molecular oxygen

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

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

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

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

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Derivative-free superiorization: Principle and algorithm

arXiv

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arXiv 2019年

作者： Censor, Yair Garduño, Edgar Helou, Elias S. Herman, Gabor T. Department of Mathematics University of Haifa Mt. Carmel Haifa3498838 Israel Departamento de Ciencias de la Computación Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas Universidad Nacional Autónoma de México Cd. Universitaria Mexico CityC.P. 04510 Mexico Department of Applied Mathematics and Statistics University of São Paulo São Carlos São Paulo13566-590 Brazil Computer Science Ph.D. Program Graduate Center City University of New York New YorkNY10016 United States

The superiorization methodology is intended to work with input data of constrained minimization problems, that is, a target function and a set of constraints. However, it is based on an antipodal way of thinking to what leads to constrained minimization methods. Instead of adapting unconstrained minimization algorithms to handling constraints, it adapts feasibility-seeking algorithms to reduce (not necessarily minimize) target function values. This is done by inserting target-function-reducing perturbations into a feasibility-seeking algorithm while retaining its feasibility-seeking ability and without paying a high computational price. A superiorized algorithm that employs component-wise target function reduction steps is presented. This enables derivative-free superiorization (DFS), meaning that superiorization can be applied to target functions that have no calculable partial derivatives or subgradients. The numerical behavior of our derivative-free superiorization algorithm is illustrated on a data set generated by simulating a problem of image reconstruction from projections. The plots of proximity-target curves of our experiments demonstrate the advantage of the proposed derivative-free superiorization algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Constrained optimization

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Community Detection and Stochastic Block Models

arXiv

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arXiv 2017年

作者： Abbe, Emmanuel Program in Applied and Computational Mathematics Department of Electrical Engineering Princeton University Princeton United States

The stochastic block model (SBM) is a random graph model with different group of vertices connecting differently. It is widely employed as a canonical model to study clustering and community detection, and provides a fertile ground to study the information-theoretic and computational tradeoffs that arise in combinatorial statistics and more generally data science. This monograph surveys the recent developments that establish the fundamental limits for community detection in the SBM, both with respect to information-theoretic and computational tradeoffs, and for various recovery requirements such as exact, partial and weak recovery. The main results discussed are the phase transitions for exact recovery at the Chernoff-Hellinger threshold, the phase transition for weak recovery at the Kesten-Stigum threshold, the optimal SNR-mutual information tradeoff for partial recovery, and the gap between information-theoretic and computational thresholds. The monograph gives a principled derivation of the main algorithms developed in the quest of achieving the limits, in particular two-round algorithms via graph-splitting, semi-definite programming, (linearized) belief propagation, classical/nonbacktracking spectral methods and graph powering. Extensions to other block models, such as geometric block models, and a few open problems are also discussed. Copyright © 2017, The Authors. All rights reserved.

关键词： Recovery

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Three-dimensional quasi-periodic shifted Green function throughout the spectrum, including Wood anomalies

Three-dimensional quasi-periodic shifted Green function thro...

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作者： Bruno, Oscar P. Shipman, Stephen P. Turc, Catalin Venakides, Stephanos Applied and Computational Mathematics Caltech PasadenaCA91125 United States Department of Mathematics Louisiana State University Baton RougeLA70803 United States Department of Mathematical Sciences New Jersey Institute of Technology NewarkNJ07102 United States Department of Mathematics Duke University DurhamNC27708 United States

This work, part II in a series, presents an efficient method for evaluation of wave scattering by doubly periodic diffraction gratings at or near what are commonly called ‘Wood anomaly frequencies’. At these frequencies, there is a grazing Rayleigh wave, and the quasi-periodic Green function ceases to exist. We present a modification of the Green function by adding two types of terms to its lattice sum. The first type are transversely shifted Green functions with coefficients that annihilate the growth in the original lattice sum and yield algebraic convergence. The second type are quasi-periodic plane wave solutions of the Helmholtz equation which reinstate certain necessary grazing modes without leading to blowup at Wood anomalies. Using the new quasi-periodic Green function, we establish, for the first time, that the Dirichlet problem of scattering by a smooth doubly periodic scattering surface at a Wood frequency is uniquely solvable. We also present an efficient high-order numerical method based on this new Green function for scattering by doubly periodic surfaces at and around Wood frequencies. We believe this is the first solver able to handle Wood frequencies for doubly periodic scattering problems in three *** demonstrate the method by applying it to acoustic scattering. © 2017 The Author(s) Published by the Royal Society. All rights reserved.

关键词： Boundary integral equations

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Numerical Modeling of Currents Circulation in Balikpapan Bay during Oil Spill Event on March 31, 2018

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

作者： A A Nur I M Radjawane T Suprijo I Mandang Department of Earth Science Faculty of Earth Science and Technology Bandung Institute of Technology Jalan Ganesha No 10 Bandung 40132 Indonesia Hydro-Atmosphere Environment Research Group Physical Oceanography and Computational Modeling Laboratory Program Study of Physics Faculty of Mathematics and Natural Sciences Mulawarman University Jalan Barong Tongkok No 04 Samarinda 75123 Indonesia

The coastal area in Balikpapan Bay and its surrounding area has been devastated after the burst of an underwater oil pipe at the bay on March 31st, 2018, and the crude oil still continues spreading for a few days later. A three-dimensional hydrodynamic model is used to simulate the currents dynamic and investigate the influence of the current circulation on the spreading of the oil spill in Balikpapan Bay from March 31th to April 15th, 2018, to cover the event i.e. several weeks after the oil spill incident. Model results are validated by calculating the RMSE, MAPE and model skill using water level between available observation data at Semayang Port, Balikpapan station and numerical model from October 1st, 2012 to January 1st, 2013. Verification result of tidal elevation data from observation and model prediction shows a good agreement with RMSE = 7.8 cm, MAPE = 14.3% and model skill = 0.995. Surface currents circulation in Balikpapan Bay can be distinguished by the currents pattern on the spring tide and neap tide condition. During the spring tide condition, the surface currents mostly move to the east after coming out from the bay. However, the surface currents are strongly going southward after come out from the bay on the neap tide condition. Based on the satellite images captured for the next days after the event, the spreading pattern of the oil spill seems to be matched to the pattern of surface currents circulation on the spring tide condition. From the analysis of the model result, it shows that the currents circulation playing the main role to disperse the oil spill in Balikpapan Bay towards the Makassar Strait.

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The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

arXiv

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arXiv 2023年

作者： Moawad, Ahmed W. Janas, Anastasia Baid, Ujjwal Ramakrishnan, Divya Saluja, Rachit Ashraf, Nader Jekel, Leon Amiruddin, Raisa Adewole, Maruf Albrecht, Jake Anazodo, Udunna Aneja, Sanjay Anwar, Syed Muhammad Bergquist, Timothy Calabrese, Evan Chiang, Veronica Chung, Verena Conte, Gian Marco Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kofler, Florian Krantchev, Kiril LaBella, Dominic Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Link, Katherine E. Liu, Xinyang Maleki, Nazanin Meier, Zeke Menze, Bjoern H. Moy, Harrison Osenberg, Klara Piraud, Marie Reitman, Zachary Shinohara, Russel Takeshi Tahon, Nourel hoda Nada, Ayman Velichko, Yuri S. Wang, Chunhao Wiestler, Benedikt Wiggins, Walter Shafique, Umber Willms, Klara Avesta, Arman Bousabarah, Khaled Chakrabarty, Satrajit Gennaro, Nicolo Holler, Wolfgang Kaur, Manpreet LaMontagne, Pamela Lin, MingDe Lost, Jan Marcus, Daniel S. Maresca, Ryan Merkaj, Sarah Nada, Ayaman Pedersen, Gabriel Cassinelli von Reppert, Marc Sotiras, Aristeidis Teytelboym, Oleg Tillmans, Niklas Westerhoff, Malte Youssef, Ayda Godfrey, Devon Floyd, Scott Rauschecker, Andreas Villanueva-Meyer, Javier Pflüger, Irada Cho, Jaeyoung Bendszus, Martin Brugnara, Gianluca Cramer, Justin Perez-Carillo, Gloria Guzman J. Johnson, Derek R. Kam, Anthony Kwan, Benjamin Yin Ming Lai, Lillian Lall, Neil U. Memon, Fatima Patro, Satya Narayana Petrovic, Bojan So, Tiffany Y. Thompson, Gerard Wu, Lei Schrickel, E. Brooke Bansal, Anu Barkhof, Frederik Besada, Cristina Chu, Sammy Druzgal, Jason Dusoi, Alexandru Farage, Luciano Feltrin, Fabricio Fong, Amy Fung, Steve H. Gray, R. Ian Ikuta, Ichiro Iv, Michael Postma, Alida A. Mahajan, Amit Joyner, David Krumpelman, Chase Letourneau-Guillon, Laurent Lincoln, Christie M. Maros, Mate E. Miller, Elka Morón, Fanny Nimchinsky, Esther A. Ozsarlak, Ozkan Patel, Uresh Rohatgi, Saurabh Saha, Atin Sayah, Anousheh Schwartz, Eric D. Shih, Robert Shiroishi, Mark Trinity health Mid Atlantic Hosiptals DarbyPA United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States ImagineQuant Children’s Hospital of Philadelphia PhiladelphiaPA United States College of Medicine Alfaisal University Riyadh Saudi Arabia DKFZ Division of Translational Neurooncology The WTZ German Cancer Consortium DKTK Partner Site University Hospital Essen Essen Germany Medical Artificial Intelligence Lab Crestview Radiology Lagos Nigeria Sage Bionetworks SeattleWA United States Montreal Neurological Institute McGill University Montreal Canada lab Crestview Radiology Lagos Nigeria Department of Therapeutic Radiology Yale School of Medicine New HavenCT United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Radiology Mayo Clinic RochesterMN United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Neurosurgery Yale School of Medicine New HavenCT United States Center for Global Health Perelman School of Medicine University of Pennsylvania PA United States Department of Informatics Technical University Munich Germany Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Children’s National Hospital WashingtonDC United States PrecisionFDA U.S. Food and Drug Administration S

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d

关键词： Image segmentation

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Scalable algorithms for learning high-dimensional linear mixed models

arXiv

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arXiv 2018年

作者： Tan, Zilong Roche, Kimberly Zhou, Xiang Mukherjee, Sayan Department of Computer Science Duke University Program in Computational Biology & Bioinformatics Duke University Department of Biostatistics Center for Statistical Genetics University of Michigan Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University

Linear mixed models (LMMs) are used extensively to model dependecies of observations in linear regression and are used extensively in many application areas. Parameter estimation for LMMs can be computationally prohibitive on big data. State-of-the-art learning algorithms require computational complexity which depends at least linearly on the dimension p of the covariates, and often use heuristics that do not offer theoretical guarantees. We present scalable algorithms for learning high-dimensional LMMs with sublinear computational complexity dependence on p. Key to our approach are novel dual estimators which use only kernel functions of the data, and fast computational techniques based on the subsampled randomized Hadamard transform. We provide theoretical guarantees for our learning algorithms, demonstrating the robustness of parameter estimation. Finally, we complement the theory with experiments on large synthetic and real data. Copyright © 2018, The Authors. All rights reserved.

关键词： Learning algorithms

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Extended calculations of energy levels, radiative properties, and lifetimes for oxygen-like Mo XXXV

arXiv

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arXiv 2019年

作者： Wang, Kai Zheng, Wei Zhao, Xiao Hui Chen, Zhan Bin Chen, Chong Yang Yan, Jun Hebei Key Lab of Optic-electronic Information and Materials College of Physics Science and Technology Hebei University Baoding071002 China Shanghai EBIT Lab Key Laboratory of Nuclear Physics and Ion-beam Application Institute of Modern Physics Department of Nuclear Science and Technology Fudan University Shanghai200433 China School of Science Hunan University of Technology Zhuzhou412007 China Institute of Applied Physics and Computational Mathematics Beijing100088 China

Employing two state-of-the-art methods, second-order many-body perturbation theory and multiconfiguration Dirac-Fock, highly accurate calculations are performed for the lowest 318 fine-structure levels arising from the 2s22p4, 2s2p5, 2p6, 2s22p33l, 2s2p43l, 2p53l, and 2s22p34l configurations in O-like Mo XXXV. Complete and consistent atomic data, including excitation energies, lifetimes, wavelengths, and E1, E2, M1 line strengths, oscillator strengths, and transition rates among these 318 levels are provided. Comparisons are made between the present two data sets, as well as with other available experimental and theoretical values. The present data are accurate enough for identification and deblending of emission lines involving the n = 3, 4 levels and are also useful for modeling and diagnosing fusion plasmas. These data can be considered as a benchmark for other calculations. Copyright © 2019, The Authors. All rights reserved.

关键词： Oxygen

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The persistent shadow of the supermassive black hole of M 87: I. Observations, calibration, imaging, and analysis*

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Astronomy and Astrophysics 2024年 681卷

作者： Akiyama, Kazunori Alberdi, Antxon Alef, Walter Algaba, Juan Carlos Anantua, Richard Asada, Keiichi Azulay, Rebecca Bach, Uwe Baczko, Anne-Kathrin Ball, David Baloković, Mislav Bandyopadhyay, Bidisha Barrett, John Bauböck, Michi Benson, Bradford A. Bintley, Dan Blackburn, Lindy Blundell, Raymond Bouman, Katherine L. Bower, Geoffrey C. Boyce, Hope Bremer, Michael Brissenden, Roger Britzen, Silke Broderick, Avery E. Broguiere, Dominique Bronzwaer, Thomas Bustamante, Sandra Carlstrom, John E. Chael, Andrew Chan, Chi-Kwan Chang, Dominic O. Chatterjee, Koushik Chatterjee, Shami Chen, Ming-Tang Chen, Yongjun Cheng, Xiaopeng Cho, Ilje Christian, Pierre Conroy, Nicholas S. Conway, John E. Crawford, Thomas M. Crew, Geoffrey B. Cruz-Osorio, Alejandro Cui, Yuzhu Dahale, Rohan Davelaar, Jordy De Laurentis, Mariafelicia Deane, Roger Dempsey, Jessica Desvignes, Gregory Dexter, Jason Dhruv, Vedant Dihingia, Indu K. Doeleman, Sheperd S. Dzib, Sergio A. Eatough, Ralph P. Emami, Razieh Falcke, Heino Farah, Joseph Fish, Vincent L. Fomalont, Edward Ford, H. Alyson Foschi, Marianna Fraga-Encinas, Raquel Freeman, William T. Friberg, Per Fromm, Christian M. Fuentes, Antonio Galison, Peter Gammie, Charles F. García, Roberto Gentaz, Olivier Georgiev, Boris Goddi, Ciriaco Gold, Roman Gómez-Ruiz, Arturo I. Gómez, José L. Gu, Minfeng Gurwell, Mark Hada, Kazuhiro Haggard, Daryl Hesper, Ronald Heumann, Dirk Ho, Luis C. Ho, Paul Honma, Mareki Huang, Chih-Wei L. Huang, Lei Hughes, David H. Ikeda, Shiro Violette Impellizzeri, C.M. Inoue, Makoto Issaoun, Sara James, David J. Jannuzi, Buell T. Janssen, Michael Jeter, Britton Massachusetts Institute of Technology Haystack Observatory 99 Millstone Road WestfordMA01886 United States National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Black Hole Initiative Harvard University 20 Garden Street CambridgeMA02138 United States Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 Bonn53121 Germany Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Department of Physics & Astronomy The University of Texas at San Antonio One UTSA Circle San AntonioTX78249 United States Institute of Astronomy and Astrophysics Academia Sinica 11F of Astronomy-Mathematics Building AS/NTU No. 1 Sec. 4 Roosevelt Rd. Taipei10617 Taiwan Departament d'Astronomia i Astrofísica Universitat de València C. Dr. Moliner 50 València Burjassot46100 Spain Observatori Astronòmic Universitat de València C. Catedrático José Beltrán 2 València Paterna46980 Spain Department of Space Earth and Environment Chalmers University of Technology Onsala Space Observatory Onsala43992 Sweden Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States Yale Center for Astronomy & Astrophysics Yale University 52 Hillhouse Avenue New HavenCT06511 United States Astronomy Department Universidad de Concepción Casilla 160 Concepción Chile Department of Physics University of Illinois 1110 West Green Street UrbanaIL61801 United States Fermi National Accelerator Laboratory MS209 PO Box 500 BataviaIL60510 United States Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States East Asian Observatory 660 N. A'ohoku Place HiloHI96720 United States 660 N. A'ohoku Place HiloHI96720 Uni

In April 2019, the Event Horizon Telescope (EHT) Collaboration reported the first-ever event-horizon-scale images of a black hole, resolving the central compact radio source in the giant elliptical galaxy M 87. These images reveal a ring with a southerly brightness distribution and a diameter of ∼42 μas, consistent with the predicted size and shape of a shadow produced by the gravitationally lensed emission around a supermassive black hole. These results were obtained as part of the April 2017 EHT observation campaign, using a global very long baseline interferometric radio array operating at a wavelength of 1.3 mm. Here, we present results based on the second EHT observing campaign, taking place in April 2018 with an improved array, wider frequency coverage, and increased bandwidth. In particular, the additional baselines provided by the Greenland telescope improved the coverage of the array. Multiyear EHT observations provide independent snapshots of the horizon-scale emission, allowing us to confirm the persistence, size, and shape of the black hole shadow, and constrain the intrinsic structural variability of the accretion flow. We have confirmed the presence of an asymmetric ring structure, brighter in the southwest, with a median diameter of 43.3-3.1+1.5 μas. The diameter of the 2018 ring is remarkably consistent with the diameter obtained from the previous 2017 observations. On the other hand, the position angle of the brightness asymmetry in 2018 is shifted by about 30 relative to 2017. The perennial persistence of the ring and its diameter robustly support the interpretation that the ring is formed by lensed emission surrounding a Kerr black hole with a mass ∼6.5× 109M. The significant change in the ring brightness asymmetry implies a spin axis that is more consistent with the position angle of the large-scale jet. © 2023 The Author(s).

关键词： Gravitation

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