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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

Predicting ICU mortality by supervised bidirectional LSTM networks 1

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Predicting ICU mortality by supervised bidirectional LSTM ne...

1st Joint Workshop on AI in Health, AIH 2018

作者： Zhu, Yao Fan, Xiaoliang Wu, Jinzhun Liu, Xiao Shi, Jia Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University Xiamen China First Affiliated Hospital Xiamen University Xiamen China School of Information Technology Deakin University Melbourne Australia Digital Fujian Institute of Healthcare and Biomedical Big Data Research Xiamen University Xiamen China School of Information Science and Engineering Lanzhou University Lanzhou China

Mortality prediction in the Intensive Care Unit (ICU) is considered as one of critical steps for the treatment of patients in serious condition. It is a big challenge to model time-series variables for mortality prediction in ICU, because physiological variables such as heart rate and blood pressure are sampled with inconsistent time frequencies. In addition, it is difficult to capture the timing changes of clinical data and to interpret the prediction result of ICU mortality. To deal with these challenges, in this paper, we propose a novel ICU mortality prediction algorithm combining bidirectional LSTM (Long Short-Term Memory) model with supervised learning. First, we preprocess 37 time-series variables related to patients’ signs. Second, we construct the Bidirectional LSTM model with supervision technique to accurately reflect significant changes in patients’ signs. Finally, we train and evaluate our model using a real-world dataset containing 4,000 ICU patients. Experimental results show that our proposed method can significantly outperform many baseline methods. © CEUR-WS. All rights reserved.

关键词： Forecasting

Gammapy: A Python package for gamma-ray astronomy

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arXiv 2023年

作者： Donath, Axel Terrier, Régis Remy, Quentin Sinha, Atreyee Nigro, Cosimo Pintore, Fabio Khélifi, Bruno Olivera-Nieto, Laura Ruiz, Jose Enrique Brügge, Kai Linhoff, Maximilian Contreras, Jose Luis Acero, Fabio Aguasca-Cabot, Arnau Berge, David Bhattacharjee, Pooja Buchner, Johannes Boisson, Catherine Fidalgo, David Carreto Chen, Andrew de Lavergne, Mathieu de Bony de Miranda Cardoso, José Vinícius Deil, Christoph Füßling, Matthias Funk, Stefan Giunti, Luca Hinton, Jim Jouvin, Léa King, Johannes Lefaucheur, Julien Lemoine-Goumard, Marianne Lenain, Jean-Philippe López-Coto, Rubén Mohrmann, Lars Morcuende, Daniel Panny, Sebastian Regeard, Maxime Saha, Lab Siejkowski, Hubert Siemiginowska, Aneta Sipőcz, Brigitta M. Unbehaun, Tim van Eldik, Christopher Vuillaume, Thomas Zanin, Roberta Center for Astrophysics Harvard and Smithsonian United States Université de Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France Max-Planck-Institut für Kernphysik P.O. Box 103980 HeidelbergD 69029 Germany IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain INAF/IASF Palermo Via U. La Malfa 153 PA Palermo90146 Italy Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Point8 GmbH Germany Astroparticle Physics Department of Physics TU Dortmund University Otto-Hahn-Str. 4a DortmundD-44227 Germany Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-YvetteF-91191 France c. Martí i Franqués 1 Barcelona08028 Spain c. Martí i Franqués 1 Barcelona08028 Spain c. Gran Capità 2-4 Barcelona08034 Spain Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Martí i Franquès 1 Barcelona08028 Spain ZeuthenD-15738 Germany Institute of physics Humboldt-University of Berlin BerlinD-12489 Germany Université Savoie Mont Blanc CNRS Laboratoire d’Annecy de Physique des Particules - IN2P3 Annecy74000 France Max Planck Institute for extraterrestrial Physics Giessenbachstrasse Garching85748 Germany Laboratoire Univers et Théories Observatoire de Paris Université PSL Université Paris Cité CNRS MeudonF-92190 France Max Planck Computing and Data Facility Gießenbachstraße 2 Garching85748 Germany School of Physics University of the Witwatersrand 1 Jan Smuts Avenue Braamfontein Johannesburg2050 South Africa The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Saupfercheckweg Heidelberg1 69117 Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Nikolaus-Fiebiger Strasse 2 Erlangen91058 Germany IRFU CEA Université Pa

Context. Traditionally, TeV-γ-ray astronomy has been conducted by experiments employing proprietary data and analysis software. However, the next generation of γ-ray instruments, such as the Cherenkov Telescope Array Observatory (CTAO), will be operated as open observatories. Alongside the data, they will also make the associated software tools available to a wider community. This necessity prompted the development of open, high-level, astronomical software customized for high-energy astrophysics. Aims. In this article, we present Gammapy, an open-source Python package for the analysis of astronomical γ-ray data, and illustrate the functionalities of its first long-term-support release, version 1.0. Built on the modern Python scientific ecosystem, Gammapy provides a uniform platform for reducing and modeling data from different γ-ray instruments for many analysis scenarios. Gammapy complies with several well-established data conventions in high-energy astrophysics, providing serialized data products that are interoperable with other software packages. Methods. Starting from event lists and instrument response functions, Gammapy provides functionalities to reduce these data by binning them in energy and sky coordinates. Several techniques for background estimation are implemented in the package to handle the residual hadronic background affecting γ-ray instruments. After the data are binned, the flux and morphology of one or more γ-ray sources can be estimated using Poisson maximum likelihood fitting and assuming a variety of spectral, temporal, and spatial models. Estimation of flux points, likelihood profiles, and light curves is also supported. Results. After describing the structure of the package, we show, using publicly available gamma-ray data, the capabilities of Gammapy in multiple traditional and novel γ-ray analysis scenarios, such as spectral and spectro-morphological modeling and estimations of a spectral energy distribution and a light curve. Its flexib

关键词： Gamma rays

Relative Importance Sampling for off-Policy Actor-Critic in Deep Reinforcement Learning

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arXiv 2018年

作者： Humayoo, Mahammad Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China

Off-policy learning is more unstable compared to on-policy learning in reinforcement learning (RL). One of the reasons for instability of off-policy learning is a discrepancy between target (π) and behavior (b) policy distribution. The discrepancy between π and b distribution can be alleviated by employing the smooth variant of importance sampling (IS), such as relative importance sampling (RIS).The RIS has parameter β ∈ [0, 1] that controls the smoothness. To cope with the instability of off-policy learning, we present the first relative importance sampling-off-policy actor-critic (RIS-off-PAC) model-free algorithms in RL. In our method, A network yields the target policy (actor), the value function (critic) assessing the current policy (π) using samples drawn from the behavior policy. We use action values generated from the behavior policy in reward function to train our algorithms rather than from the target policy. We also use deep neural networks to train both the actor and critic. We evaluated our algorithms on a number of OpenAI Gym problems and demonstrated better or comparable performance to several state-of-the-art RL baselines. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

VulDeePecker: A deep learning-based system for vulnerability detection

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arXiv 2018年

作者： Li, Zhen Zou, Deqing Xu, Shouhuai Ou, Xinyu Jin, Hai Wang, Sujuan Deng, Zhijun Zhong, Yuyi Services Computing Technology and System Lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Security and Computer Hebei University Shenzhen Huazhong University of Science and Technology Research Institute Department of Computer Science University of Texas at San Antonio

The automatic detection of software vulnerabilities is an important research problem. However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negative rate). In this paper, we initiate the study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features. Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection. In particular, we need to find representations of software programs that are suitable for deep learning. For this purpose, we propose using code gadgets to represent programs and then transform them into vectors, where a code gadget is a number of (not necessarily consecutive) lines of code that are semantically related to each other. This leads to the design and implementation of a deep learning-based vulnerability detection system, called Vulnerability Deep Pecker (VulDeePecker). In order to evaluate VulDeePecker, we present the first vulnerability dataset for deep learning approaches. Experimental results show that VulDeePecker can achieve much fewer false negatives (with reasonable false positives) than other approaches. We further apply VulDeePecker to 3 software products (namely Xen, Seamonkey, and Libav) and detect 4 vulnerabilities, which are not reported in the National Vulnerability database but were "silently" patched by the vendors when releasing later versions of these products;in contrast, these vulnerabilities are almost entirely missed by the other vulnerability detection systems we experimented with. Copyright © 2018, The Authors. All rights reserved.

关键词： Computer software

Machine learning approaches for classifying major depressive disorder using biological and neuropsychological markers: A meta-analysis

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Neuroscience and Biobehavioral Reviews 2025年 174卷 106201页

作者： Zhang, Lin Jian, Liwen Long, Yiming Ren, Zhihong Calhoun, Vince D. Passos, Ives Cavalcante Tian, Xinyu Xiang, Yuhong School of Psychology Central China Normal University Wuhan 430079 China Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU) Ministry of Education Wuhan 430079 China Key Laboratory of Human Development and Mental Health of Hubei Province School of Psychology Central China Normal University Wuhan 430079 China Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta GA United States Klaus Advanced Computing Building 266 Ferst Drive Atlanta 30332-0765 Georgia Department of Psychiatry School of Medicine Universidade Federal do Rio Grande do Sul RS Porto Alegre Brazil Laboratory of Molecular Psychiatry Centro de Pesquisa Experimental (CPE) and Centro de Pesquisa Clínica (CPC) Hospital de Clínicas de Porto Alegre (HCPA) RS Porto Alegre Brazil Department of Psychiatry School of Medicine Universidade Federal do Rio Grande do Sul R. Ramiro Barcelos 2400 Floresta RS Porto Alegre 90035002 Brazil

Traditional diagnostic methods for major depressive disorder (MDD), which rely on subjective assessments, may compromise diagnostic accuracy. In contrast, machine learning models have the potential to classify and diagnose MDD more effectively, reducing the risk of misdiagnosis associated with conventional methods. The aim of this meta-analysis is to evaluate the overall classification accuracy of machine learning models in MDD and examine the effects of machine learning algorithms, biomarkers, diagnostic comparison groups, validation procedures, and participant age on classification performance. As of September 2024, a total of 176 studies were ultimately included in the meta-analysis, encompassing a total of 60,926 participants. A random-effects model was applied to analyze the extracted data, resulting in an overall classification accuracy of 0.825 (95 % CI [0.810;0.839]). Convolutional neural networks significantly outperformed support vector machines (SVM) when using electroencephalography and magnetoencephalography data. Additionally, SVM demonstrated significantly better performance with functional magnetic resonance imaging data compared to graph neural networks and gaussian process classification. The sample size was negatively correlated to classification accuracy. Furthermore, evidence of publication bias was also detected. Therefore, while this study indicates that machine learning models show high accuracy in distinguishing MDD from healthy controls and other psychiatric disorders, further research is required before these findings can be generalized to large-scale clinical practice. © 2025 Elsevier Ltd

关键词： Big data Biomarkers Machine Learning Major Depressive Disorder

A quantum annealing approach to ionic diffusion in solids

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arXiv 2019年

作者： Utimula, Keishu Ichibha, Tom Prayogo, Genki I. Hongo, Kenta Nakano, Kousuke Maezono, Ryo School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan

We have developed a framework for using quantum annealing computation to evaluate a key quantity in ionic diffusion in solids, the correlation factor. Existing methods can only calculate the correlation factor analytically in the case of physically unrealistic models, making it difficult to relate microstructural information about diffusion path networks obtainable by current ab initio techniques to macroscopic quantities such as diffusion coefficients. We have mapped the problem into a quantum spin system described by the Ising Hamiltonian. By applying our framework in combination with ab initio technique, it is possible to understand how diffusion coefficients are controlled by temperatures, pressures, atomic substitutions, and other factors. We have calculated the correlation factor in a simple case with a known exact result by a variety of computational methods, including simulated quantum annealing on the spin models, the classical random walk, the matrix description, and quantum annealing on D-Wave with hybrid solver. This comparison shows that all the evaluations give consistent results with each other, but that many of the conventional approaches require infeasible computational costs. Quantum annealing is also currently infeasible because of the cost and scarcity of Q-bits, but we argue that when technological advances alter this situation, quantum annealing will easily outperform all existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Hamiltonians

Stochastic estimations of a total number of classes for the clusterings with too enormous samples to be accommodate into a clustering engine

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arXiv 2019年

作者： Utimula, Keishu Prayogo, Genki I. Nakano, Kousuke Hongo, Kenta Maezono, Ryo School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan

We considered the problem how to handle the exploding number of possibilities to be sorted into irreducible classes by using a clustering tool when its input capacity cannot accommodate the total number of the possibility. Concrete situations are explained taking examples of atomic substitutions in the supercell modeling of alloys. The number of the possibility sometimes amounts to ∼ trillion being too large to be accommodate. It is hence not practically feasible to identify how many irreducible classes exist by straightforward manner even though there are several tools available to perform the clustering. We have developed a stochastic framework to avoid the shortage of capacity, providing a method to estimate the total number of irreducible classes (the order of the classes) as a statistical estimate. A prominent conclusion derived here is that the statistical variation of the number of classes at each sampling trial is working as a promising measure to estimate the order. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

The putative center in NGC 1052

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arXiv 2025年

作者： Baczko, Anne-Kathrin Kadler, Matthias Ros, Eduardo Fromm, Christian M. Wielgus, Maciek Perucho, Manel Krichbaum, Thomas P. Baloković, Mislav Blackburn, Lindy Chan, Chi-Kwan Issaoun, Sara Janssen, Michael Ricci, Luca Akiyama, Kazunori Albentosa-Ruíz, Ezequiel Alberdi, Antxon Alef, Walter Algaba, Juan Carlos Anantua, Richard Asada, Keiichi Azulay, Rebecca Bach, Uwe Ball, David Bandyopadhyay, Bidisha Barrett, John Bauböck, Michi Benson, Bradford A. Bintley, Dan Blundell, Raymond Bouman, Katherine L. Bower, Geoffrey C. Boyce, Hope Bremer, Michael Brinkerink, Christiaan D. Brissenden, Roger Britzen, Silke Broderick, Avery E. Broguiere, Dominique Bronzwaer, Thomas Bustamante, Sandra Byun, Do-Young Carlstrom, John E. Ceccobello, Chiara Chael, Andrew Chang, Dominic O. Chatterjee, Koushik Chatterjee, Shami Chen, Ming-Tang Chen, Yongjun Cheng, Xiaopeng Cho, Ilje Christian, Pierre Conroy, Nicholas S. Conway, John E. Cordes, James M. 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. Dougall, Sean Taylor 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 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 Haworth, Kari Hecht, Michael H. Hesper, Ronald Heumann, Dirk Ho, Luis C. Ho, Paul Honma, Mareki Huang, Chih-Wei L. Huang, Lei Hughes, David H. Impellizzeri, C.M. Violette Inoue, Makoto James, David J. Jannuzi, Buell T. Jeter, Britton Jiang, Wu Jiménez-Rosales, Alejandra Johnson, Michael D. Jorstad, Svetlana Joshi, Abhishek V. Jung, Taehyun Karami, Mansour Karuppusamy, Ramesh Kawashima, Tomohisa Keating, Garrett K. Kettenis, M Department of Space Earth and Environment Chalmers University of Technology GothenburgSE-41296 Sweden Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Institut für Theoretische Physik und Astrophysik Universität Würzburg Emil-Fischer-Str. 31 WürzburgD-97074 Germany Institut für Theoretische Physik Goethe-Universität Frankfurt Max-von-Laue-Straße 1 Frankfurt am MainD-60438 Germany Departament d'Astronomia i Astrofísica Universitat de València C. Dr. Moliner 50 Burjassot ValènciaE-46100 Spain Observatori Astronòmic Universitat de València C. Catedrático José Beltrán 2 Paterna ValènciaE-46980 Spain Yale Center for Astronomy & Astrophysics Yale University 52 Hillhouse Avenue New HavenCT06511 United States Black Hole Initiative Harvard University 20 Garden Street CambridgeMA02138 United States Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States Radboud University PO Box 9010 Nijmegen6500 GL Netherlands 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 Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n GranadaE-18008 Spain Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia 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 Astronomy Depart

Context. Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. Aims. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. Methods. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. Our study is focused on the size and continuum spectrum of the innermost region containing the central engine and the footpoints of both jets. We employed a synchrotron-self absorption model to fit the continuum radio spectrum and we combined the size measurements from close to the central engine out to ∼1 pc to study the jet collimation. Results. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 43 µas perpendicular to the jet axes, corresponding to just around 250 RS (Schwarzschild radii). This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 3 × 103 RS and 1 × 104 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 µas from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at ∼130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 2.6 × 104 Gauss, which is consistent with previous measurements. Conclusions. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dens

关键词： Synchrotrons