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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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New families in our Solar neighborhood: applying Gaussian Mixture models for objective classification of structures in the Milky Way and in simulations

arXiv

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arXiv 2021年

作者： Nikakhtar, Farnik Sanderson, Robyn E. Wetzel, Andrew Loebman, Sarah Sharma, Sanjib Beaton, Rachael Ted Mackereth, J. Poovelil, Vijith Jacob Zasowski, Gail Bonaca, Ana Martell, Sarah Jönsson, Henrik Faucher-Giguère, Claude-André Department of Physics & Astronomy University of Pennsylvania 209 S 33rd St. PhiladelphiaPA19104 United States Center for Computational Astrophysics Flatiron Institute 162 5th Ave New YorkNY10010 United States Department of Physics & Astronomy University of California DavisCA95616 United States Department of Physics University of California Merced 5200 North Lake Rd. MercedCA95343 United States Sydney Institute for Astronomy School of Physics University of Sydney A28 NSW2006 Australia Australia Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Canadian Institute for Theoretical Astrophysics University of Toronto 60 St. George Street TorontoONM5S 3H8 Canada Dunlap Institute for Astronomy and Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Department of Physics & Astronomy University of Utah Salt Lake CityUT84112 United States Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States School of Physics UNSW SydneyNSW2052 Australia Materials Science and Applied Mathematics Malmö University MalmöSE-205 06 Sweden Department of Physics and Astronomy CIERA Northwestern University 2145 Sheridan Road EvanstonIL60208 United States

The standard picture of galaxy formation motivates the decomposition of the Milky Way into 3–4 stellar populations with distinct kinematic and elemental abundance distributions: the thin disk, thick disk, bulge, and stellar halo. To test this idea, we construct a Gaussian mixture model (GMM) for both simulated and observed stars in the Solar neighborhood, using measured velocities and iron abundances (i.e., an augmented Toomre diagram) as the distributions to be decomposed. We compare results for the Gaia-APOGEE DR16 crossmatch catalog of the Solar neighborhood with those from a suite of synthetic Gaia-APOGEE crossmatches constructed from FIRE-2 cosmological simulations of Milky Way-mass galaxies. We find that in both the synthetic and real data, the best-fit GMM uses five independent components, some of whose properties resemble the standard populations predicted by galaxy formation theory. Two components can be identified unambiguously as the thin disk and another as the halo. However, instead of a single counterpart to the thick disk, there are three intermediate components with different age and alpha abundance distributions (although these data are not used to construct the model). We use decompositions of the synthetic data to show that the classified components indeed correspond to stars with different origins. By analogy with the simulated data, we show that our mixture model of the real Gaia-APOGEE crossmatch distinguishes the following components: (1) a classic thin disk of young stars on circular orbits (46%), (2) thin disk stars heated by interactions with satellites (22%), (3, 4) two components representing the velocity asymmetry of the alpha-enhanced thick disk (27%), and (5) a stellar halo consistent with early, massive accretion (4%). Copyright © 2021, The Authors. All rights reserved.

关键词： Galaxies

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

arXiv

引用

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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2022 Review of Data-Driven Plasma science

arXiv

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arXiv 2022年

作者： Anirudh, Rushil Archibald, Rick Asif, M. Salman Becker, Markus M. Benkadda, Sadruddin Bremer, Peer-Timo Budé, Rick H.S. Chang, C.S. Chen, Lei Churchill, R.M. Citrin, Jonathan Gaffney, Jim A. Gainaru, Ana Gekelman, Walter Gibbs, Tom Hamaguchi, Satoshi Hill, Christian Humbird, Kelli Jalas, Sören Kawaguchi, Satoru Kim, Gon-Ho Kirchen, Manuel Klasky, Scott Kline, John L. Krushelnick, Karl Kustowski, Bogdan Lapenta, Giovanni Li, Wenting Ma, Tammy Mason, Nigel J. Mesbah, Ali Michoski, Craig Munson, Todd Murakami, Izumi Najm, Habib N. Erik J. Olofsson, K. Park, Seolhye Peterson, J. Luc Probst, Michael Pugmire, David Sammuli, Brian Sawlani, Kapil Scheinker, Alexander Schissel, David P. Shalloo, Rob J. Shinagawa, Jun Seong, Jaegu Spears, Brian K. Tennyson, Jonathan Thiagarajan, Jayaraman Ticoş, Catalin M. Trieschmann, Jan van Dijk, Jan Van Essen, Brian Ventzek, Peter Wang, Haimin Wang, Jason T.L. Wang, Zhehui Wende, Kristian Xu, Xueqiao Yamada, Hiroshi Yokoyama, Tatsuya Zhang, Xinhua Lawrence Livermore National Laboratory LivermoreCA94550 United States Oak Ridge National Laboratory Oak RidgeTN37830 United States University of California Riverside RiversideCA92521 United States Felix-Hausdorff-Str. 2 Greifswald17489 Germany Aix Marseille University CNRS PIIM UMR 7345 Marseille France Department of Applied Physics Eindhoven University of Technology Netherlands Princeton Plasma Physics Laboratory Princeton NJ08540 United States Institute of Ion Physics and Applied Physics University of Innsbruck Technikerstrasse 25 Innsbruck6020 Austria DIFFER - Dutch Institute for Fundamental Energy Research Eindhoven Netherlands Science and Technology of Nuclear Fusion Group Eindhoven University of Technology Eindhoven Netherlands Department of Physics and Astronomy University of California Los AngelesCA90095 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95051 United States Center for Atomic and Molecular Technologies Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka Japan Department of Nuclear Sciences and Applications International Atomic Energy Agency Wagramer Strasse 5 PO Box 100 Vienna1400 Austria Center for Free-Electron Laser Science Department of Physics Universität Hamburg Luruper Chaussee 149 Hamburg22761 Germany Division of Information and Electronic Engineering Graduate School of Engineering Muroran Institute of Technology Hokkaido Muroran050-8585 Japan Seoul National University Seoul151-741 Korea Republic of Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Los Alamos National Laboratory Los AlamosNM87545 United States Center for Ultrafast Optical Science University of Michigan Ann ArborMI48109-2099 United States Department of Mathematics KULeuven University of Leuven Belgium Department of Physical Sciences The University of Kent CanterburyCT2 7NH United Kingdom Department of Chemical and Biomolecular Engineering University of California Berk

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma-processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Hyperuniformity and anti-hyperuniformity in one-dimensional substitution tilings

arXiv

引用

arXiv 2018年

作者： Oguz, Erdal C. Socolar, Joshua E.S. Steinhardt, Paul J. Torquato, Salvatore School of Mechanical Engineering Sackler Center for Computational Molecular and Materials Science Tel Aviv University Tel Aviv Israel Physics Department Duke University DurhamNC27708 United States Department of Physics Princeton Center for Theoretical Science Princeton University PrincetonNJ08544 United States Center for Cosmology and Particle Physics Department of Physics New York University New YorkNY10003 United States Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States

We consider the scaling properties characterizing the hyperuniformity (or anti-hyperuniformity) of long wavelength fluctuations in a broad class of one-dimensional substitution tilings. We present a simple argument that predicts the exponent α governing the scaling of Fourier intensities at small wavenumbers, tilings with α > 0 being hyperuniform, and confirm with numerical computations that the predictions are accurate for quasiperiodic tilings, tilings with singular continuous spectra, and limit-periodic tilings. Tilings with quasiperiodic or singular continuous spectra can be constructed with α arbitrarily close to any given value between −1 and 3. Limit-periodic tilings can be constructed with α between −1 and 1 or with Fourier intensities that approach zero faster than any power law. Copyright © 2018, The Authors. All rights reserved.

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Distinct modes of mitochondrial metabolism uncouple T cell differentiation and function (vol 571, pg 403, 2019)

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NATURE 2019年第7773期573卷 E2-E2页

作者： Bailis, Will Shyer, Justin A. Zhao, Jun Canaveras, Juan Carlos Garcia Al Khazal, Fatimah J. Qu, Rihao Steach, Holly R. Bielecki, Piotr Khan, Omair Jackson, Ruaidhri Kluger, Yuval Maher, Louis J., III Rabinowitz, Joshua Craft, Joe Flavell, Richard A. Department of Immunobiology Yale School of Medicine New Haven USA Department of Pathology Children’s Hospital of Philadelphia Philadelphia USA Department of Pathology Yale School of Medicine New Haven USA Program of Computational Biology and Bioinformatics Yale University New Haven USA Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton USA Department of Chemistry Princeton University Princeton USA Diabetes Research Center University of Pennsylvania Philadelphia USA Department of Biochemistry and Molecular Biology Mayo Clinic College of Medicine and Science Rochester USA Applied Mathematics Program Yale University New Haven USA Department of Internal Medicine (Rheumatology) Yale School of Medicine New Haven USA Howard Hughes Medical Institute Chevy Chase USA

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Snowmass Theory Frontier: Astrophysics and Cosmology

arXiv

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arXiv 2022年

作者： Green, Daniel Ruderman, Joshua T. Safdi, Benjamin R. Shelton, Jessie Achúcarro, Ana Adshead, Peter Akrami, Yashar Baryakhtar, Masha Baumann, Daniel Berlin, Asher Blinov, Nikita Boddy, Kimberly K. Buschmann, Malte Cabass, Giovanni Caldwell, Robert Castorina, Emanuele Chen, Thomas Y. Chen, Xingang Coulton, William Croon, Djuna Cui, Yanou Curtin, David Cyr-Racine, Francis-Yan Dessert, Christopher Dienes, Keith R. Draper, Patrick Du, Peizhi Ellis, Sebastian A.R. Essig, Rouven Flauger, Raphael Fong, Chee Sheng Foster, Joshua W. Fumagalli, Jacopo Harigaya, Keisuke Horiuchi, Shunsaku Ivanov, Mikhail M. Kahn, Yonatan Knapen, Simon Leane, Rebecca K. Lee, Hayden Lentz, Erik W. Lewandowski, Matthew Lisanti, Mariangela Long, Andrew J. Loverde, Marilena Maleknejad, Azadeh McAllister, Liam McDermott, Samuel D. McGehee, Robert Meerburg, P. Daniel Meyers, Joel Dizgah, Azadeh Moradinezhad Münchmeyer, Moritz Outmezguine, Nadav Joseph Pajer, Enrico Palma, Gonzalo A. Parikh, Aditya Park, Jong-Chul Peter, Annika H.G. Pimentel, Guilherme L. Renaux-Petel, Sébastien Rodd, Nicholas L. Shakya, Bibhushan Shiu, Gary Silverstein, Eva Simonovic, Marko Singh, Rajeev Sleight, Charlotte Takhistov, Volodymyr Tanedo, Philip Taronna, Massimo Thomas, Brooks Toro, Natalia Tsai, Yu-Dai Vitagliano, Edoardo Vogelsberger, Mark Wallisch, Benjamin Wandelt, Benjamin D. Wechsler, Risa H. Weniger, Christoph Wu, W.L. Kimmy Xu, Weishuang Linda Yamada, Masaki Yu, Hai-Bo Zhang, Zhengkang Zhong, Yi-Ming Zurek, Kathryn Department of Physics University of California San Diego La Jolla CA92093 United States Center for Cosmology and Particle Physics Department of Physics New York University New YorkNY10003 United States Department of Physics University of California BerkeleyCA94720 United States Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Illinois Center for Advanced Studies of the Universe Department of Physics University of Illinois Urbana-Champaign UrbanaIL61801 United States Lorentz Institute for Theoretical Physics Leiden University CA Leiden2333 Netherlands Illinois Center for Advanced Studies of the Universe Department of Physics University of Illinois Urbana-Champaign UrbanaIL61801 United States CERCA/ISO Department of Physics Case Western Reserve University 10900 Euclid Avenue ClevelandOH44106 United States Department of Physics Imperial College London Blackett Laboratory Prince Consort Road LondonSW7 2AZ United Kingdom Department of Physics University of Washington SeattleWA98195 United States Center for Theoretical Physics National Taiwan University Taipei10617 Taiwan Institute of Physics University of Amsterdam Amsterdam1098 XH Netherlands Fermi National Accelerator Laboratory Theory Division BataviaIL60510 United States Department of Physics and Astronomy University of Victoria VictoriaBC Canada Department of Physics The University of Texas at Austin AustinTX78712 United States Department of Physics Princeton University PrincetonNJ08544 United States School of Natural Sciences Institute for Advanced Study PrincetonNJ08540 United States Department of Physics and Astronomy Dartmouth College 6127 Wilder Laboratory HanoverNH03755 United States Department of Physics "Aldo Pontremoli" University of Milan Milan Italy Fu Foundation School of Engineering and Applied Science Columbia University New YorkNY10027 United States Institute for Theory and Computation Harvard-Smithsonian Center

We summarize progress made in theoretical astrophysics and cosmology over the past decade and areas of interest for the coming decade. This Report is prepared as the TF09 "Astrophysics and Cosmology" topical group summary for the Theory Frontier as part of the Snowmass 2021 process. Copyright © 2022, The Authors. All rights reserved.

关键词： Group theory

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Novel Polarimetric Analysis of Near Horizon Flaring Episodes in M87* in Millimeter Wavelength

arXiv

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arXiv 2025年

作者： Emami, Razieh Liska, Matthew Chatterjee, Koushik Bower, Geoffrey C. Benbow, Wystan Finkbeiner, Douglas Wielgus, Maciek Hernquist, Lars Smith, Randall Tremblay, Grant Ricarte, Angelo Steiner, James F. Broderick, Avery E. Saurabh Davelaar, Jordy Grindlay, Josh Vogelsberger, Mark Chan, Chi-Kwan Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Center for Relativistic Astrophysics Georgia Institute of Technology Howey Physics Bldg 837 State Street NW AtlantaGA30332 United States Department of Physics University of Maryland College ParkMD United States Academia Sinica Institute of Astronomy and Astrophysics 645 N. A’ohoku Place HiloHI96720 United States Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n GranadaE-18008 Spain Black Hole Initiative Harvard University 20 Garden Street CambridgeMA02138 United States Perimeter Institute for Theoretical Physics 31 Caroline Street North WaterlooONN2L 2Y5 Canada Department of Physics and Astronomy University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Department of Astrophysical Sciences Peyton Hall Princeton University PrincetonNJ08544 United States NASA Hubble Fellowship Program Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Department of Astronomy and Steward Observatory University of Arizona 933 North Cherry Avenue 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

Recent multi-wavelength observations of M87* (Algaba et al. 2024) revealed a high-energy γ-ray flare without a corresponding millimeter counterpart. We present a theoretical polarimetric study to evaluate the presence and nature of a potential millimeter flare in M87*, using a suite of general relativistic magnetohydrodynamical simulations with varying black hole (BH) spins and magnetic field configurations. We find that the emergence of a millimeter flare is strongly influenced by both spin and magnetic structure, with limited sensitivity to the electron distribution (thermal vs. non-thermal). We model the intensity light curve with a damped random walk (DRW) and compare the characteristic timescale (τ) with recent SMA observations, finding that the simulated τ exceeds observed values by over an order of magnitude. In a flaring case with BH spin a=+0.5, we identify a distinct millimeter flare followed by an order-of-magnitude flux drop. All Stokes parameters show variability near the flare, including a sign reversal in the electric vector position angle. While most βm modes remain stable, the EB-correlation phase is highly sensitive to both the flare peak and decay. We examine polarimetric signatures in photon sub-rings, focusing on modes ns=0 and ns=1. The ns=0 signal closely matches the full image, while ns=1 reveals distinct behaviors, highlighting the potential of space VLBI to isolate sub-ring features. Finally, we analyze the magnetic and velocity field evolution during the flare, finding that magnetic reconnection weakens during the flux decay, and the clockwise velocity flow transitions into an outflow-dominated regime. These results suggest that transient radio variability near flares encodes key information about BH spin and magnetic field structure, offering a novel probe into the physics of active galactic nuclei. © 2025, CC BY.

关键词： Magnetohydrodynamics

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Manifold learning for organizing unstructured sets of process observations

arXiv

引用

arXiv 2018年

作者： Dietrich, Felix Kooshkbaghi, Mahdi Bollt, Erik M. Kevrekidis, Ioannis G. Department of Chemical and Biomolecular Engineering Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD21218 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mathematics Department of Electrical and Computer Engineering Clarkson Center for Complex Systems Science Clarkson University PotsdamNY13699-5815 United States

Data mining is routinely used to organize ensembles of short temporal observations so as to reconstruct useful, low-dimensional realizations of an underlying dynamical system. In this paper, we use manifold learning to organize unstructured ensembles of observations ("trials") of a system's response surface. We have no control over where every trial starts;and during each trial operating conditions are varied by turning "agnostic" knobs, which change system parameters in a systematic but unknown way. As one (or more) knobs "turn" we record (possibly partial) observations of the system response. We demonstrate how such partial and disorganized observation ensembles can be integrated into coherent response surfaces whose dimension and parametrization can be systematically recovered in a data-driven fashion. The approach can be justified through the Whitney and Takens embedding theorems, allowing reconstruction of manifolds/attractors through different types of observations. We demonstrate our approach by organizing unstructured observations of response surfaces, including the reconstruction of a cusp bifurcation surface for Hydrogen combustion in a Continuous Stirred Tank Reactor. Finally, we demonstrate how this observation-based reconstruction naturally leads to informative transport maps between input parameter space and output/state variable *** codes 37M20 Copyright © 2018, The Authors. All rights reserved.

关键词： Dynamical systems

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Comparison of Polarized Radiative Transfer Codes used by the EHT Collaboration

arXiv

引用

arXiv 2023年

作者： Prather, Ben S. Dexter, Jason Moscibrodzka, Monika Pu, Hung-Yi Bronzwaer, Thomas Davelaar, Jordy Younsi, Ziri Gammie, Charles F. Gold, Roman Wong, And George N. Akiyama, Kazunori Alberdi, Antxon Alef, Walter Algaba, Juan Carlos Anantua, Richard Asada, Keiichi Azulay, Rebecca Bach, Uwe Baczko, Anne-Kathrin Ball, David Baloković, Mislav Barrett, John Bauböck, Michi Benson, Bradford A. Bintley, Dan Blackburn, Lindy Blundell, Raymond Bouman, Katherine L. Bower, Geoffrey C. Boyce, Hope Bremer, Michael Brinkerink, Christiaan D. Brissenden, Roger Britzen, Silke Broderick, Avery E. Broguiere, Dominique Bustamante, Sandra Byun, Do-Young Carlstrom, John E. Ceccobello, Chiara 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. Cordes, James M. Crawford, Thomas M. Crew, Geoffrey B. Cruz-Osorio, Alejandro Cui, Yuzhu De Laurentis, Mariafelicia Deane, Roger Dempsey, Jessica Desvignes, Gregory Dhruv, Vedant Doeleman, Sheperd S. Dougal, Sean Dzib, Sergio A. Eatough, Ralph P. Emami, Razieh Falcke, Heino Farah, Joseph Fish, Vincent L. Fomalont, Ed Alyson Ford, H. Fraga-Encinas, Raquel Freeman, William T. Friberg, Per Fromm, Christian M. Fuentes, Antonio Galison, Peter García, Roberto Gentaz, Olivier Georgiev, Boris Goddi, Ciriaco 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. Ikeda, Shiro Violette Impellizzeri, C.M. Inoue, Makoto Issaoun, Sara James, David J. Jannuzi, Buell T. Janssen, Michael 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, Mark Kim, Dong-Jin Kim, Jae-Young Kim, Jongsoo Kim, Junhan Kino, Motoki Koay, Jun Yi Kocherlakota, Prashant K CCS-2 Los Alamos National Laboratory P.O. Box 1663 Los AlamosNM87545 United States JILA Department of Astrophysical and Planetary Sciences University of Colorado BoulderCO80309 United States Department of Astrophysics IMAPP Radboud University P.O. Box 9010 Nijmegen6500 GL Netherlands Department of Physics National Taiwan Normal University No. 88 Sec.4 Tingzhou Rd. Taipei116 Taiwan Center of Astronomy and Gravitation National Taiwan Normal University No. 88 Sec. 4 Tingzhou Road Taipei116 Taiwan Institute of Astronomy and Astrophysics Academia Sinica 11F of Astronomy-Mathematics Building AS/NTU No. 1 Sec. 4 Roosevelt Rd Taipei10617 Taiwan Department of Astronomy Columbia Astrophysics Laboratory Columbia University 550 W 120th Street New YorkNY10027 United States Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New YorkNY10010 United States Radboud University P.O. Box 9010 Nijmegen6500 GL Netherlands Mullard Space Science Laboratory University College London Holmbury St. Mary Dorking SurreyRH5 6NT United Kingdom Institut für Theoretische Physik Goethe-Universität Frankfurt Max-von-Laue-Straße 1 Frankfurt am MainD-60438 Germany Department of Physics University of Illinois 1110 West Green Street UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 West Green Street UrbanaIL61801 United States NCSA University of Illinois 1205 W Clark St UrbanaIL61801 United States CP3-Origins University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark School of Natural Sciences Institute for Advanced Study 1 Einstein Drive PrincetonNJ08540 United States Princeton Gravity Initiative Princeton University PrincetonNJ08544 United States 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 Harvard University 20 Gar

Interpretation of resolved polarized images of black holes by the Event Horizon Telescope (EHT) requires predictions of the polarized emission observable by an Earth-based instrument for a particular model of the black hole accretion system. Such predictions are generated by general relativistic radiative transfer (GRRT) codes, which integrate the equations of polarized radiative transfer in curved spacetime. A selection of ray-tracing GRRT codes used within the EHT collaboration is evaluated for accuracy and consistency in producing a selection of test images, demonstrating that the various methods and implementations of radiative transfer calculations are highly consistent. When imaging an analytic accretion model, we find that all codes produce images similar within a pixel-wise normalized mean squared error (NMSE) of 0.012 in the worst case. When imaging a snapshot from a cell-based magnetohydrodynamic simulation, we find all test images to be similar within NMSEs of 0.02, 0.04, 0.04, and 0.12 in Stokes I, Q, U, and V respectively. We additionally find the values of several image metrics relevant to published EHT results to be in agreement to much better precision than measurement uncertainties. © 2023, CC BY.

关键词： Mean square error

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