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arXiv 2024年

作者： Paraschos, Georgios Filippos Kim, J.-Y. Wielgus, M. Röder, J. Krichbaum, T.P. Ros, E. Agudo, I. Myserlis, I. Moscibrodzka, M. Traianou, E. Zensus, J.A. Blackburn, L. Chan, C.-K. Issaoun, S. Janssen, M. Johnson, M.D. Fish, V.L. Akiyama, K. Alberdi, A. Alef, W. Algaba, J.C. Anantua, R. Asada, K. Azulay, R. Bach, U. Baczko, A.-K. Ball, D. Baloković, M. Barrett, J. Bauböck, M. Benson, B.A. Bintley, D. Blundell, R. Bouman, K.L. Bower, G.C. Boyce, H. Bremer, M. Brinkerink, C.D. Brissenden, R. Britzen, S. Broderick, A.E. Broguiere, D. Bronzwaer, T. Bustamante, S. Byun, D.-Y. Carlstrom, J.E. Ceccobello, C. Chael, A. Chang, D.O. Chatterjee, K. Chatterjee, S. Chen, M.T. Chen, Y. Cheng, X. Cho, I. Christian, P. Conroy, N.S. Conway, J.E. Cordes, J.M. Crawford, T.M. Crew, G.B. Cruz-Osorio, A. Cui, Y. Dahale, R. Davelaar, J. De Laurentis, M. Deane, R. Dempsey, J. Desvignes, G. Dexter, J. Dhruv, V. Doeleman, S.S. Dougal, S. Dzib, S.A. Eatough, R.P. Emami, R. Falcke, H. Farah, J. Fomalont, E. Ford, H.A. Foschi, M. Fraga-Encinas, R. Freeman, W.T. Friberg, P. Fromm, C.M. Fuentes, A. Galison, P. Gammie, C.F. García, R. Gentaz, O. Georgiev, B. Goddi, C. Gold, R. Gómez-Ruiz, A.I. Gómez, J.L. Gu, M. Gurwell, M. Hada, K. Haggard, D. Haworth, K. Hecht, M.H. Hesper, R. Heumann, D. Ho, L.C. Ho, P. Honma, M. Huang, C.L. Huang, L. Hughes, D.H. Ikeda, S. Impellizzeri, C.M.V. Inoue, M. James, D.J. Jannuzi, B.T. Jeter, B. Jaing, W. Jiménez-Rosales, A. Jorstad, S. Joshi, A.V. Jung, T. Karami, M. Karuppusamy, R. Kawashima, T. Keating, G.K. Kettenis, M. Kim, D.-J. Kim, J. Kim, J. Kino, M. Koay, J.Y. Kocherlakota, P. Kofuji, Y. Koch, P.M. Koyama, S. Kramer, C. Kramer, J.A. Kramer, M. Kuo, C.-Y. La Bella, N. Lauer, T.R. Lee, D. Lee, S.-S. Leung, P.K. Levis, A. Li, Z. Lico, R. Lindahl, G. Lindqvist, M. Lisakov, M. Liu, J. Liu, K. Liuzzo, E. Lo, W.-P. Lobanov, A.P. Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Department of Astronomy and Atmospheric Sciences Kyungpook National University Daegu702-701 Korea Republic of Institute of Physics Silesian University in Opava Bezručovo nám. 13 OpavaCZ-746 01 Czech Republic Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n GranadaE-18008 Spain Avenida Divina Pastora 7 Local 20 GranadaE-18012 Spain Radboud University P.O. Box 9010 Nijmegen6500 GL Netherlands 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 NASA Hubble Fellowship Program Einstein Fellow 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 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 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 València PaternaE-46980 Spain Department of Space Earth and Environment Chalmers University of Technology Onsala Space Observatory OnsalaSE-43992 Sweden Yale C

Context. 3C 84 is a nearby radio source with a complex total intensity structure, showing linear polarisation and spectral patterns. A detailed investigation of the central engine region necessitates the use of very-long-baseline interferometry (VLBI) above the hitherto available maximum frequency of 86 GHz. Aims. Using ultrahigh resolution VLBI observations at the currently highest available frequency of 228 GHz, we aim to perform a direct detection of compact structures and understand the physical conditions in the compact region of 3C 84. Methods. We used Event Horizon Telescope (EHT) 228 GHz observations and, given the limited (u, v)-coverage, applied geometric model fitting to the data. Furthermore, we employed quasi-simultaneously observed, ancillary multi-frequency VLBI data for the source in order to carry out a comprehensive analysis of the core structure. Results. We report the detection of a highly ordered, strong magnetic field around the central, supermassive black hole of 3C 84. The brightness temperature analysis suggests that the system is in equipartition. We also determined a turnover frequency of νm = (113 ± 4) GHz, a corresponding synchrotron self-absorbed magnetic field of BSSA = (2.9 ± 1.6) G, and an equipartition magnetic field of Beq = (5.2 ± 0.6) G. Three components are resolved with the highest fractional polarisation detected for this object (mnet = (17.0 ± 3.9)%). The positions of the components are compatible with those seen in low-frequency VLBI observations since 2017-2018. We report a steeply negative slope of the spectrum at 228 GHz. We used these findings to test existing models of jet formation, propagation, and Faraday rotation in 3C 84. Conclusions. The findings of our investigation into different flow geometries and black hole spins support an advection-dominated accretion flow in a magnetically arrested state around a rapidly rotating supermassive black hole as a model of the jet-launching system in the core of 3C 84. However,

关键词： Magnetic fields

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Discovering the Signal Subgraph: An Iterative Screening Approach on Graphs

arXiv

引用

arXiv 2018年

作者： Shen, Cencheng Wang, Shangsi Badea, Alexandra Priebe, Carey E. Vogelstein, Joshua T. Department of Applied Economics and Statistics University of Delaware United States Department of Applied Mathematics and Statistics Johns Hopkins University United States Center for In Vivo Microscopy Duke University United States Department of Biomedical Engineering Institute for Computational Medicine Johns Hopkins University United States

Supervised learning on graphs is a challenging task due to the high dimensionality and inherent structural dependencies in the data, where each edge depends on a pair of vertices. Existing conventional methods are designed for standard Euclidean data and do not account for the structural information inherent in graphs. In this paper, we propose an iterative vertex screening method to achieve dimension reduction across multiple graph datasets with matched vertex sets and associated graph attributes. Our method aims to identify a signal subgraph to provide a more concise representation of the full graphs, potentially benefiting subsequent vertex classification tasks. The method screens the rows and columns of the adjacency matrix concurrently and stops when the resulting distance correlation is maximized. We establish the theoretical foundation of our method by proving that it estimates the true signal subgraph with high probability. Additionally, we establish the convergence rate of classification error under the Erdos-Renyi random graph model and prove that the subsequent classification can be asymptotically optimal, outperforming the entire graph under high-dimensional conditions. Our method is evaluated on various simulated datasets and real-world human and murine graphs derived from functional and structural magnetic resonance images. The results demonstrate its excellent performance in estimating the ground-truth signal subgraph and achieving superior classification accuracy. Copyright © 2018, The Authors. All rights reserved.

关键词： Magnetic resonance

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Topology of tensor ranks

arXiv

引用

arXiv 2018年

作者： Comon, Pierre Lim, Lek-Heng Qi, Yang Ye, Ke GIPSA-Lab CNRS St Martin d’Hères CedexF-38402 France Computational and Applied Mathematics Initiative Department of Statistics University of Chicago ChicagoIL60637 United States KLMM Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

We study path-connectedness and homotopy groups of sets of tensors defined by tensor rank, border rank, multilinear rank, as well as their symmetric counterparts for symmetric tensors. We show that over C, the set of rank-r tensors and the set of symmetric rank-r symmetric tensors are both path-connected if r is not more than the complex generic rank;these results also extend to border rank and symmetric border rank over C. Over R, the set of rank-r tensors is path-connected if it has the expected dimension but the corresponding result for symmetric rank-r symmetric d-tensors depends on the order d: connected when d is odd but not when d is even. Border rank and symmetric border rank over R have essentially the same path-connectedness properties as rank and symmetric rank over R. When r is greater than the complex generic rank, we are unable to discern any general pattern: For example, we show that border-rank-three tensors in R2 ⊗ R2 ⊗ R2 fall into four connected components. For multilinear rank, the manifold of d-tensors of multilinear rank (r1, . . ., rd) in Cn1 ⊗· · ·⊗Cnd is always path-connected, and the same is true in Rn1 ⊗· · ·⊗Rnd unless ni = ri = ∏j≠i rj for some i ∈ {1, . . ., d}. Beyond path-connectedness, we determine, over both R and C, the fundamental and higher homotopy groups of the set of tensors of a fixed small rank, and, taking advantage of Bott periodicity, those of the manifold of tensors of a fixed multilinear rank. We also obtain analogues of these results for symmetric tensors of a fixed symmetric rank or a fixed symmetric multilinear rank.15A69, 54D05, 55Q05 Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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Cluster-mining: An approach for determining core structures of metallic nanoparticles from atomic pair distribution function data

arXiv

引用

arXiv 2019年

作者： Banerjee, Soham Liu, Chia-Hao Jensen, Kirsten M. Ø. Juhas, Pavol Lee, Jennifer D. Tofanelli, Marcus Ackerson, Christopher J. Murray, Christopher B. Billinge, Simon J. L. Department of Applied Physics and Applied Mathematics Columbia University New York New York10027 United States Brookhaven National Laboratory Computational Science Initiative UptonNY11973 United States Condensed Matter Physics and Materials Science Department Brookhaven National Laboratory UptonNY11973 United States Department of Chemistry University of Copenhagen CopenhagenDK-2100 Denmark Department of Chemistry University of Pennsylvania PhiladelphiaPA19104 United States Department of Materials Science and Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Chemistry Colorado State University Fort CollinsCO80523 United States Department of Chemistry University of Pittsburgh PittsburghPA15260 United States

We present a novel approach for finding and evaluating structural models of small metallic nanoparticles. Rather than fitting a single model with many degrees of freedom, the approach algorithmically builds libraries of nanoparticle clusters from multiple structural motifs, and individually fits them to experimental PDFs. Each cluster-fit is highly constrained. The approach, called cluster-mining, returns all candidate structure models that are consistent with the data as measured by a goodness of fit. It is highly automated, easy to use, and yields models that are more physically realistic and result in better agreement to the data than models based on cubic close-packed crystallographic cores, often reported in the literature for metallic nanoparticles. Copyright © 2019, The Authors. All rights reserved.

关键词： Nanoparticles

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LARGE-SCALE AIR POLLUTION MODELING IN EUROPE UNDER DIFFERENT CLIMATIC SCENARIOS

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International Journal of Big Data Mining for Global Warming 2020年第2期1卷

作者： ZAHARI ZLATEV IVAN DIMOV ISTVÁN FARAGÓ KRASSIMIR GEORGIEV ÁGNES HAVASI Department of Environmental Science Aarhus University Roskilde Denmark Institute of Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria MTA-ELTE Numerical Analysis and Large Networks Research Group Budapest Hungary and Institute of Mathematics Budapest University of Technology and Economics Egry J. u. 1. 1111 Budapest Hungary MTA-ELTE Numerical Analysis and Large Networks Research Group Budapest Hungary and Department of Applied Analysis and Computational Mathematics Eötvös Loránd University Budapest Hungary

The treatment of large-scale air pollution models is not only important for modern society, but also an extremely difficult task. Five important physical and chemical processes: (1) horizontal advection, (2) horizontal diffusion, (3) vertical exchange, (4) emission of different pollutants and (5) dry and wet deposition have to be united and handled together. This leads to huge computational problems which have to be treated on modern high-speed parallel architectures by using advanced numerical methods. The computational difficulties are vastly increased when such models are used in the investigation of the impact of climatic changes on high pollution levels that are often exceeding certain critical concentrations and, therefore, are becoming harmful for plants, animals and human beings. There are two major reasons for the great increase in computational difficulties: (a) it is necessary to run the discretized data on fine spatial grid models over very long time-periods consisting of many consecutive years and (b) different scenarios are to be used in order to investigate the sensitivity of the pollution levels to systematic variations of several carefully selected key parameters. How the major difficulties can be resolved is explained here. Furthermore, many results are presented in order to demonstrate the ability of the model to handle successfully the huge computational tasks by using fine space and time discretization and by applying a chemical scheme with many chemical species participating in several hundred chemical reactions. Our results indicate that the climatic changes will often lead to some increases in the pollution levels.

关键词： Large-scale environmental models systems of nonlinear PDEs numerical algorithms splitting techniques parallel codes high-speed computers cache memory climate changes high pollution levels

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Modeling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture models

arXiv

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arXiv 2020年

作者： Ezaki, Takahiro Himeno, Yu Watanabe, Takamitsu Masuda, Naoki Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan PRESTO JST 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan Department of Aeronautics and Astronautics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Laboratory for Cognition Circuit Dynamics RIKEN Centre for Brain Science Saitama351-0198 Japan International Research Center for Neurointelligence The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Recent studies have proposed that one can summarize brain activity into dynamics among a relatively small number of hidden states and that such an approach is a promising tool for revealing brain function. Hidden Markov models (HMMs) are a prevalent approach to inferring such neural dynamics among discrete brain states. However, the impact of assuming Markovian structure in neural time series data has not been sufficiently examined. Here, to address this situation and examine the performance of the HMM, we compare the model with the Gaussian mixture model (GMM), which is with no temporal regularization and thus a statistically simpler model than the HMM, by applying both models to synthetic time series generated from empirical resting-state functional magnetic resonance imaging (fMRI) data. We compared the GMM and HMM for various sampling frequencies, lengths of recording per participant, numbers of participants, and numbers of independent component signals. We find that the HMM attains a better accuracy of estimating the hidden state than the GMM in a majority of cases. However, we also find that the accuracy of the GMM is comparable to that of the HMM under the condition that the sampling frequency is reasonably low (e.g., TR = 2.88 or 3.60 s) or the data is relatively short. These results suggest that the GMM can be a viable alternative to the HMM for investigating hidden-state dynamics under this condition. Copyright © 2020, The Authors. All rights reserved.

关键词： Hidden Markov models

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Unsupervised extraction of phenotypes from cancer clinical notes for association studies

arXiv

引用

arXiv 2019年

作者： Stark, Stefan G. Hyland, Stephanie L. Pradier, Melanie F. Lehmann, Kjong-Van Wicki, Andreas Perez-Cruz, Fernando Vogt, Julia E. Rätsch, Gunnar Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional Ph.D. Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Department of Computer Science ETH Zürich Zürich Switzerland Medical Informatics Group University Hospital Zürich Zürich Switzerland Swiss Institute for Bioinformatics Zurich Switzerland Department of Biology ETH Zürich Zürich Switzerland Department of Signal Processing and Information Theory University Carlos III in Madrid Leganés Spain School of Engineering and Applied Sciences Harvard University CambridgeMA United States Department of Biomedicine University of Basel Basel Switzerland Tumorzentrum University Hospital Basel Basel Switzerland Swiss Data Science Center ETH Zürich and EPFL Lausanne Switzerland Department of Mathematics and Computer Science University of Basel Basel Switzerland

The recent adoption of Electronic Health Records (EHRs) by healthcare providers has introduced an important source of data that provides detailed and highly specific insights into patient phenotypes over large cohorts. These datasets, in combination with machine learning and statistical approaches, generate new opportunities for research and clinical care. However, many methods require the patient representations to be in structured formats, while the information in the EHR is often locked in unstructured text designed for human readability. In this work, we develop the methodology to automatically extract clinical features from clinical narratives from large EHR corpora without the need for prior knowledge. We consider medical terms and sentences appearing in clinical narratives as atomic information units. We propose an efficient clustering strategy suitable for the analysis of large text corpora and utilize the clusters to represent information about the patient compactly. Additionally, we define the sentences on ontologic and natural language vocabularies to automatically detect pertinent combinations of concepts present in the corpus, even when an ontology is not available. To demonstrate the utility of our approach, we perform an association study of clinical features with somatic mutation profiles from 4,007 cancer patients and their tumors. We apply the proposed algorithm to a dataset consisting of .65 thousand documents with a total of .3.2 million sentences. After correcting for cancer type and other confounding factors, we identify a total of 340 significant statistical associations between the presence of somatic mutations and clinical features. We annotated these associations according to their novelty and we report several known associations. We also propose 37 plausible, testable hypothesis for associations where the underlying biological mechanism does not appear to be known. These results illustrate that the automated discovery of clinical features

关键词： Diseases

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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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Large-scale multiconfiguration Dirac-Hartree-Fock calculations for astrophysics: Cl-like ions from Cr VIII to Zn XIV

arXiv

引用

arXiv 2019年

作者： Wang, K. Jönsson, P. Del Zanna, G. Godefroid, M. Chen, Z.B. Chen, C.Y. Yan, J. Hebei Key Lab of Optic-electronic Information and Materials College of Physics Science and Technology Hebei University Baoding071002 China Chimie Quantique et Photophysique CP160/09 Université libre de Bruxelles Av. F.D. Roosevelt 50 Brussels1050 Belgium 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 Group for Materials Science and Applied Mathematics Malmö University MalmöSE-20506 Sweden DAMTP Centre for Mathematical Sciences University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Department of Applied Physics School of Science Hunan University of Technology Zhuzhou412007 China Institute of Applied Physics and Computational Mathematics Beijing100088 China

We use the multiconfiguration Dirac-Hartree-Fock (MCDHF) method combined with the relativistic configuration interaction (RCI) approach (GRASP2K) to provide a consistent set of transition energies and radiative transition data for the lower n = 3 states in all Cl-like ions of astrophysical importance, from Cr VIII to Zn XIV. We also provide excitation energies calculated for Fe X using the many-body perturbation theory (MBPT, implemented within FAC). The comparison of the present MCDHF results with MBPT and with the available experimental energies indicates that the theoretical excitation energies are highly accurate, with uncertainties of only a few hundred cm−1. Detailed comparisons for Fe X and Ni XII highlight discrepancies in the experimental energies found in the literature. Several new identifications are proposed. Copyright © 2019, The Authors. All rights reserved.

关键词： Astrophysics

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Interacting dark sector within ETHOS: Cosmological constraints from SPT cluster abundance with DES and HST weak lensing data

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Physical Review D 2025年第8期111卷 083543-083543页

作者： A. Mazoun S. Bocquet J. J. Mohr M. Garny H. Rubira M. Klein L. E. Bleem S. Grandis T. Schrabback M. Aguena S. Allam S. W. Allen O. Alves F. Andrade-Oliveira D. Brooks A. Carnero Rosell M. Carrasco Kind J. Carretero M. Costanzi L. N. da Costa T. M. Davis S. Desai J. De Vicente H. T. Diehl S. Dodelson P. Doel S. Everett B. Flaugher J. Frieman J. García-Bellido R. Gassis G. Giannini D. Grün G. Gutierrez S. R. Hinton D. L. Hollowood D. J. James K. Kuehn O. Lahav S. Lee M. Lima G. Mahler J. L. Marshall R. Miquel J. Myles R. L. C. Ogando M. E. S. Pereira A. Pieres A. A. Plazas Malagón A. Porredon C. L. Reichardt A. K. Romer S. Samuroff E. Sanchez D. Sanchez Cid I. Sevilla-Noarbe M. Schubnell M. Smith E. Suchyta M. E. C. Swanson A. Tamošiūnas G. Tarle D. L. Tucker V. Vikram N. Weaverdyck J. Weller P. Wiseman Physik Department T31 Technische Universität München James-Franck-Straße 1 D-85748 Garching Germany University Observatory Faculty of Physics Ludwig-Maximilians-Universität Scheinerstraße 1 D-81679 München Germany Excellence Cluster ORIGINS Boltzmannstraße 2 D-85748 Garching Germany Max Planck Institute for Extraterrestrial Physics Gießenbachstraße 1 D-85748 Garching Germany Kavli Institute for Cosmology Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge CB3 0WA United Kingdom High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue Chicago Illinois 60637 USA Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstraße 25/8 6020 Innsbruck Austria Argelander-Institut für Astronomie Auf dem Hügel 71 53121 Bonn Germany Laboratório Interinstitucional de e-Astronomia—LIneA Rua General José Cristino 77 Rio de Janeiro 20921-40 Brazill Fermi National Accelerator Laboratory P.O. Box 500 Batavia Illinois 60510 USA Kavli Institute for Particle Astrophysics and Cosmology Stanford University Stanford California 94305 USA SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park California 94025 USA Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Department of Physics and Astronomy University College London Gower Street London WC1E 6BT United Kingdom Instituto de Astrofisica de Canarias E-38205 La Laguna Tenerife Spain Universidad de La Laguna Departamento de Astrofísica E-38206 La Laguna Tenerife Spain Center for Astrophysical Surveys National Center for Supercomputing Applications 1205 West Clark Street Urbana Illinois 61801 USA Institut de Física d’Altes Energies (IFAE) The Barcelona Institute of Science and Technology Campus UAB 08193 Bellaterra (Barcelona) Spain

We use galaxy cluster abundance measurements from the South Pole Telescope enhanced by multicomponent matched filter confirmation and complemented with mass information obtained using weak-lensing data from Dark Energy Survey Year 3 (DES Y3) and targeted Hubble Space Telescope observations for probing deviations from the cold dark matter paradigm. Concretely, we consider a class of dark sector models featuring interactions between dark matter (DM) and a dark radiation (DR) component within the framework of the effective theory of structure formation (ETHOS). We focus on scenarios that lead to power suppression over a wide range of scales, and thus can be tested with data sensitive to large scales, as realized, for example, for DM–DR interactions following from an unbroken non-Abelian SU(N) gauge theory (interaction rate with power-law index n=0 within the ETHOS parametrization). Cluster abundance measurements are mostly sensitive to the amount of DR interacting with DM, parametrized by the ratio of DR temperature to the cosmic microwave background (CMB) temperature, ξDR=TDR/TCMB. We find an upper limit ξDR<17% at 95% credibility. When the cluster data are combined with Planck 2018 CMB data along with baryon acoustic oscillation (BAO) measurements we find ξDR<10%, corresponding to a limit on the abundance of interacting DR that is around 3 times tighter than that from CMB + BAO data alone. We also discuss the complementarity of weak lensing informed cluster abundance studies with probes sensitive to smaller scales, explore the impact on our analysis of massive neutrinos, and comment on a slight preference for the presence of a nonzero interacting DR abundance, which enables a physical solution to the S8 tension.

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