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arXiv 2022年

作者： Chichura, P.M. Foster, A. Patel, C. Ossa-Jaen, N. Ade, P.A.R. Ahmed, Z. Anderson, A.J. Archipley, M. Austermann, J.E. Avva, J.S. Balkenhol, L. Barry, P.S. Thakur, R. Basu Beall, J.A. Benabed, K. Bender, A.N. Benson, B.A. Bianchini, F. Bleem, L.E. Bouchet, F.R. Bryant, L. Byrum, K. Carlstrom, J.E. Carter, F.W. Cecil, T.W. Chang, C.L. Chaubal, P. Chen, G. Chiang, H.C. Cho, H.-M. Chou, T.-L. Citron, R. Cliche, J.-F. Crawford, T.M. Crites, A.T. Cukierman, A. Daley, C.M. Denison, E.V. Dibert, K. Ding, J. Dobbs, M.A. Dutcher, D. Everett, W. Feng, C. Ferguson, K.R. Fu, J. Galli, S. Gallicchio, J. Gambrel, A.E. Gardner, R.W. George, E.M. Goeckner-Wald, N. Gualtieri, R. Guns, S. Gupta, N. Guyser, R. de Haan, T. Halverson, N.W. Harke-Hosemann, A.H. Harrington, N.L. Henning, J.W. Hilton, G.C. Hivon, E. Holder, G.P. Holzapfel, W.L. Hood, J.C. Howe, D. Hrubes, J.D. Huang, N. Hubmayr, J. Irwin, K.D. Jeong, O.B. Jonas, M. Jones, A. Khaire, T.S. Knox, L. Kofman, A.M. Korman, M. Kubik, D.L. Kuhlmann, S. Kuo, C.-L. Lee, A.T. Leitch, E.M. Li, D. Lowitz, A. Lu, C. Marrone, D.P. McMahon, J.J. Meyer, S.S. Michalik, D. Millea, M. Mocanu, L.M. Montgomery, J. Moran, C. Corbett Nadolski, A. Natoli, T. Nguyen, H. Nibarger, J.P. Noble, G. Novosad, V. Omori, Y. Padin, S. Pan, Z. Paschos, P. Patil, S. Pearson, J. Phadke, K.A. Posada, C.M. Prabhu, K. Pryke, C. Quan, W. Rahlin, A. Reichardt, C.L. Riebel, D. Riedel, B. Rouble, M. Ruhl, J.E. Saliwanchik, B.R. Sayre, J.T. Schaffer, K.K. Schiappucci, E. Shirokoff, E. Sievers, C. Smecher, G. Sobrin, J.A. Springmann, A. Stark, A.A. Stephen, J. Story, K.T. Suzuki, A. Tandoi, C. Thompson, K.L. Thorne, B. Tucker, C. Umilta, C. Vale, L.R. Veach, T. Vieira, J.D. Wang, G. Whitehorn, N. Wu, W.L.K. Yefremenko, V. Yoon, K.W. Young, M.R. Department of Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics Case Western Reserve University ClevelandOH44106 United States Department of Physics University of Illinois Urbana-Champaign 1110 West Green Street UrbanaIL61801 United States Department of Physics University of Redlands 1267 East Colton Ave RedlandsCA92374 United States School of Physics and Astronomy Cardiff University CardiffCF24 3YB United Kingdom Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall StanfordCA94305 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 West Green Street UrbanaIL61801 United States Center for AstroPhysical Surveys National Center for Supercomputing Applications UrbanaIL61801 United States NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Department of Physics University of Colorado BoulderCO80309 United States Department of Physics University of California BerkeleyCA94720 United States School of Physics University of Melbourne ParkvilleVIC3010 Australia High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue. ArgonneIL60439 United States California Institute of Technology 1200 East California Boulevard. PasadenaCA91125 United States Institut d'Astrophysique de Paris UMR 7095 CNRS Sorbonne Université 98 bis boulevard Arago Paris75014 France Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics Stanford University 382 Via Pueblo Mall StanfordCA94305 United St

We present the first measurements of asteroids in millimeter wavelength (mm) data from the South Pole Telescope (SPT), which is used primarily to study the cosmic microwave background (CMB). We analyze maps of two ∼ 270 deg2 sky regions near the ecliptic plane, each observed with the SPTpol camera ∼ 100 times over one month. We subtract the mean of all maps of a given field, removing static sky signal, and then average the mean-subtracted maps at known asteroid locations. We detect three asteroids-(324) Bamberga, (13) Egeria, and (22) Kalliope-with signal-to-noise ratios (S/N) of 11.2, 10.4, and 6.1, respectively, at 2.0 mm (150 GHz);we also detect (324) Bamberga with S/N of 4.1 at 3.2 mm (95 GHz). We place constraints on these asteroids' effective emissivities, brightness temperatures, and light curve modulation amplitude. Our flux density measurements of (324) Bamberga and (13) Egeria roughly agree with predictions, while our measurements of (22) Kalliope suggest lower flux, corresponding to effective emissivities of 0.64±0.11 at 2.0 mm and Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

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Observatory science with eXTP

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science China(Physics,Mechanics & Astronomy) 2019年第2期62卷 97-138页

作者： Jean J.M.in 't Zand Enrico Bozzo JinLu Qu Xiang-Dong Li Lorenzo Amati Yang Chen Immacolata Donnarumma Victor Doroshenko Stephen A.Drake Margarita Hernanz Peter A.Jenke Thomas J.Maccarone Simin Mahmoodifar Domitilla de Martino Alessandra De Rosa Elena M.Rossi Antonia Rowlinson Gloria Sala Giulia Stratta Thomas M.Tauris Joern Wilms XueFeng Wu Ping Zhou Iván Agudo Diego Altamirano Jean-Luc Atteia Nils A.andersson M.Cristina Baglio David R.Ballantyne Altan Baykal Ehud Behar Tomaso Belloni Sudip Bhattacharyya Stefano Bianchi Anna Bilous Pere Blay Joao Braga Sφren Brandt Edward F.Brown Niccolo Bucciantini Luciano Burderi Edward M.Cackett Riccardo Campana Sergio Campana Piergiorgio Casella Yuri Cavecchi Frank Chambers Liang Chen Yu-Peng Chen Jér?me Chenevez Maria Chernyakova ChiChuan Jin Riccardo Ciolfi Elisa Costantini Andrew Cumming Antonino D'Aì Zi-Gao Dai Filippo D'Ammando Massimiliano De Pasquale Nathalie Degenaar Melania Del Santo Valerio D'Elia Tiziana Di Salvo Gerry Doyle Maurizio Falanga XiLong Fan Robert D.Ferdman Marco Feroci Federico Fraschetti Duncan K.Galloway Angelo F.Gambino Poshak Gandhi MingYu Ge Bruce Gendre Ramandeep Gill Diego G?tz Christian Gouiffès Paola Grandi Jonathan Granot Manuel Güdel Alexander Heger Craig O.Heinke Jeroen Homan Rosario Iaria Kazushi Iwasawa Luca Izzo Long Ji Peter G.Jonker Jordi José Jelle S.Kaastra Emrah Kalemci Oleg Kargaltsev Nobuyuki Kawai Laurens Keek Stefanie Komossa Ingo Kreykenbohm Lucien Kuiper Devaky Kunneriath Gang Li En-Wei Liang Manuel Linares Francesco Longo FangJun Lu Alexander A.Lutovinov Denys Malyshev Julien Malzac Antonios Manousakis Ian McHardy Missagh Mehdipour YunPeng Men Mariano Méndez Roberto P.Mignani Romana Mikusincova M.Coleman Miller Giovanni Miniutti Christian Motch Joonas Nättilä Emanuele Nardini Torsten Neubert Paul T.O'Brien Mauro Orlandini Julian P.Osborne Luigi Pacciani Stéphane Paltani Maurizio Paolillo Iossif E.Papadakis Biswajit Paul Alberto Pellizzoni Uria Peretz Miguel A.Pérez Torres Emanuele Perinati Chanda Prescod-Weinstein Pablo Reig SRON Netherlands Institute for Space Research Department of Astronomy University of Geneva Key Laboratory for Particle Astrophysics Institute of High Energy PhysicsChinese Academy of Sciences School of Astronomy and Space Science Nanjing University INAF-IASF Bologna ASI Via del Politecnico snc INAF-Istituto di Astrofisica e Planetologie Spaziali Institut für Astronomie und Astrophysik Tübingen Universit?t Tübingen NASA Goddard Space Flight Center ICREA&Institute of Space Sciences(IEEC-CSIC) Campus UABCarrer de Can Magrans CSPAR SPA University of Alabama in Huntsville Department of Physics Texas Tech University INAF Osservatorio Astronomico di Capodimonte Leiden Observatory Leiden University Anton Pannekoek Institute University of Amsterdam ASTRON Netherlands Institute for Radio Astronomy Universitat Politecnica de Catalunya(UPC-IEEC) Universitádegli Studi di Urbino‘Carlo Bo’ Max-Planck-Institut für Radioastronomie Dr.Karl-Remeis-Sternwarte and Erlangen Centre for Astroparticle Physics(ECAP) Friedrich Alexander Universit?t Erlangen-Nurnberg Purple Mountain Observatory Chinese Academy of Sciences Instituto de Astrofysíca de Andalucia-CSIC Department of Physics and Astronomy University of Southampton IRAP Universitéde ToulouseCNRSUPSCNES Mathematical Sciences and STAG Research Centre University of Southampton New York University Abu Dhabi Center for Relativistic Astrophysics School of PhysicsGeorgia Institute of Technology Physics Department Middle East Technical University Department of Physics Technion-Israel Institute of Technology INAF Osservatorio Astronomico di Brera Tata Institute of Fundamental Research Dipartimento di Matematica e Fisica Universitàdegli Studi Roma Tre Instituto de Astrofísica de Canarias Instituto Nacional de Pesquisas Espaciais-INPE National Space Institute Technical University of Denmark(DTU Space) Department of Physics and Astronomy Michigan State University INAF Osservatorio Astrofisico di Arcetri Dipartimento di Fisica Universitádegli Studi di Cagliari Department of Physics and A

In this White Paper we present the potential of the enhanced X-ray Timing and Polarimetry(eXTP) mission for studies related to Observatory science targets. These include flaring stars, supernova remnants, accreting white dwarfs, low and high mass X-ray binaries, radio quiet and radio loud active galactic nuclei, tidal disruption events, and gamma-ray bursts. eXTP will be excellently suited to study one common aspect of these objects: their often transient nature. Developed by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of science, the eXTP mission is expected to be launched in the mid 2020s.

关键词： space research in struments nuclear astrophysics flare stars accretion and accretion disks mass loss and stellar winds cataclysmic binaries X-ray binaries supernova remnants active galactic nuclei X-ray bursts gamma-ray bursts gravitational waves

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Guest Editorial Special Issue on Security and Privacy in Cyber-Physical Systems

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IEEE Internet of Things Journal 2017年第6期4卷 1798-1800页

作者： Yu, Wei Fu, Xinwen Song, Houbing Economides, Anastasios A. Jo, Minho Zhao, Wei Department of Computer and Information Sciences Towson University TowsonMD21252 United States Department of Computer Science University of Central Florida OrlandoFL32816 United States Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona BeachFL32114 United States Computer Networks and Telematic Applications Laboratory University of Macedonia Thessaloniki546 36 Greece Department of Computer Convergence Software Korea University Seoul Korea Republic of University of Macau China

A typical cyber-physical system (CPS) refers to a system that features a tight integration of computation, networking, and physical elements for interactions between cyber and physical spaces. The Internet of Things (IoT) is considered to be the networking infrastructure of CPS. applications of CPS cover numerous smart-world research areas that our daily life depends upon, including smart transportation, smart electrical power grid, smart cities, smart medical systems, smart manufacturing systems, and others. While major research on improving the efficiency and reliability of CPS by using advanced information and communication technologies has been conducted, the risks of cyberspace security and privacy breaches in CPS need to be seriously investigated before a massive deployment of CPS technologies can or should be realized.

关键词： Special issues and sections Cyber-physical systems Smart cities Smart grids Manufacturing processes

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Supporting high-performance and high-throughput computing for experimental science

arXiv

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arXiv 2018年

作者： Huerta, E.A. Haas, Roland Jha, Shantenu Neubauer, Mark Katz, Daniel S. Huerta National Center for Supercomputing Applications Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States Roland Haas National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Shantenu Jha Brookhaven National Laboratory and Rutgers State University of New Jersey PiscatawayNJ08854 United States Department of Physics National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications Department of Computer Science Department of Electrical and Computer Engineering School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States

The advent of experimental science facilities-instruments and observatories, such as the Large Hadron Collider (LHC), the Laser Interferometer Gravitational Wave Observatory (LIGO), and the upcoming Large Synoptic Survey Telescope (LSST)-has brought about challenging, large-scale computational and data processing requirements. Traditionally, the computing infrastructures to support these facility's requirements were organized into separate infrastructure that supported their high-throughput needs and those that supported their high-performance computing needs. We argue that in order to enable and accelerate scientific discovery at the scale and sophistication that is now needed, this separation between High-Performance Computing (HPC) and High-Throughput Computing (HTC) must be bridged and an integrated, unified infrastructure must be provided. In this paper, we discuss several case studies where such infrastructures have been implemented. These case studies span different science domains, software systems, and application requirements as well as levels of sustainability. A further aim of this paper is to provide a basis to determine the common characteristics and requirements of such infrastructures, as well as to begin a discussion of how best to support the computing requirements of existing and future experimental science facilities.90C06, 68Q85 Copyright © 2018, The Authors. All rights reserved.

关键词： Application programs

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Network-based protein structural classification

arXiv

引用

arXiv 2018年

作者： Newaz, Khalique Ghalehnovi, Mahboobeh Rahnama, Arash Antsaklis, Panos J. Milenković, Tijana Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States Interdisciplinary Center for Network Science and Applications University of Notre Dame Notre DameIN46556 United States Eck Institute for Global Health University of Notre Dame Notre DameIN46556 United States Department of Electrical Engineering University of Notre Dame Notre DameIN46556 United States

Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification \(PSC\) can help, by allowing for determining structural classes of currently unclassified proteins based on their features, and then relying on the fact that proteins with similar structures have similar functions. Existing PSC approaches rely on sequence-based or direct 3-dimensional \(3D\) structure-based protein features. In contrast, we first model 3D structures of proteins as protein structure networks \(PSNs\). Then, we use network-based features for PSC. We propose the use of graphlets, state-of-the-art features in many research areas of network science, in the task of PSC. Moreover, because graphlets can deal only with unweighted PSNs, and because accounting for edge weights when constructing PSNs could improve PSC accuracy, we also propose a deep learning framework that automatically learns network features from weighted PSNs. When evaluated on a large set of ∼9,400 CATH and ∼12,800 SCOP protein domains \(spanning 36 PSN sets\), the best of our proposed approaches are superior to existing PSC approaches in terms of accuracy, with comparable running times. Copyright © 2018, The Authors. All rights reserved.

关键词： Proteins

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Interuniversity Research Center “STEERING” - STatistics for EnginERING: Design, Quality and Reliability

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Procedia Structural Integrity 2018年 8卷 168-173页

作者： G. Arcidiacono R. Berni N. Bonora M. Catelani M. Pierini Department of Innovation and Information Engineering (DIIE) Guglielmo Marconi University Via Plinio 44 00193 Rome Italy Department of Statistics Computer Science Applications G. Parenti University of Florence Viale Morgagni 59 50134 Florence Italy Department of Mechanical and Civil Engineering University of Cassino and Lazio Meridionale Via G. Di Biasio 43 03043 Cassino Italy Department of Information Engineering University of Florence Via di Santa Marta 3 50139 Florence Italy Department of Industrial Engineering University of Florence Via di Santa Marta 3 50139 Florence Italy

In this paper we present the Interuniversity Research Center STEERING, formed in June 2017. The Research Center has been founded by three Italian Universities through five departments. It represents the connection between Statistics and engineering. The five departments promoting it are the following: department of Innovation and Information engineering (Guglielmo Marconi University, Rome); department of Statistics computer science applications, department of Information engineering, department of Industrial engineering (University of Florence); department of Mechanical and Civil engineering (University of Cassino and Lazio Meridionale). The potentiality of the Research Center and some of its aims are explained through three empirical case studies.

关键词： robust process optimization experimental design split-plot quality reliability

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Author Correction: Anak Krakatau triggers volcanic freezer in the upper troposphere

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Scientific reports 2020年第1期10卷 7279页

作者： A T Prata A Folch A J Prata R Biondi H Brenot C Cimarelli S Corradini J Lapierre A Costa Barcelona Supercomputing Center Computer Applications in Science and Engineering Barcelona Spain. andrew.prata@bsc.es. Barcelona Supercomputing Center Computer Applications in Science and Engineering Barcelona Spain. AIRES Pty. Ltd. Mt Eliza Victoria Australia. Visiting Professor School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia. Università degli Studi di Padova Dipartimento di Geoscienze Padua Italy. Royal Belgian Institute for Space Aeronomy Brussels Belgium. Department of Earth and Environmental Sciences Ludwig-Maximilians-Universität München Germany. Istituto Nazionale di Geofisica e Vulcanologia Osservatorio Nazionale Terremoti Rome Italy. Earth Networks Inc. Germantown MD United States. Istituto Nazionale di Geofisica e Vulcanologia Sezione di Bologna Bologna Italy.

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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GoT-WAVE: Temporal network alignment using graphlet-orbit transitions

arXiv

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arXiv 2018年

作者： Aparício, David Ribeiro, Pedro Silva, Fernando Milenković, Tijana CRACS INESC-TEC Faculdade de Ciências Universidade do Porto R. Campo Alegre 1021 Porto4169-007 Portugal Department of Computer Science and Engineering Interdisciplinary Center for Network Science and Applications ECK Institute for Global Health University of Notre Dame Notre DameIN46556 United States

Global pairwise network alignment (GPNA) aims to find a one-to-one node mapping between two networks that identifies conserved network regions. GPNA algorithms optimize node conservation (NC) and edge conservation (EC). NC quantifies topological similarity between nodes. Graphlet-based degree vectors (GDVs) are a state-of-the-art topological NC measure. Dynamic GDVs (DGDVs) were used as a dynamic NC measure within the first-ever algorithms for GPNA of temporal networks: DynaMAGNA++ and DynaWAVE. The latter is superior for larger networks. We recently developed a different graphlet-based measure of temporal node similarity, graphlet-orbit transitions (GoTs). Here, we use GoTs instead of DGDVs as a new dynamic NC measure within DynaWAVE, resulting in a new approach, GoT-WAVE. On synthetic networks, GoT-WAVE improves DynaWAVE's accuracy by 25% and speed by 64%. On real networks, when optimizing only dynamic NC, each method is superior ≈50% of the time. While DynaWAVE benefits more from also optimizing dynamic EC, only GoT-WAVE can support directed edges. Hence, GoT-WAVE is a promising new temporal GPNA algorithm, which efficiently optimizes dynamic NC. Future work on better incorporating dynamic EC may yield further improvements. Copyright © 2018, The Authors. All rights reserved.

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Financial prediction using back propagation neural networks with opposition based learning

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Journal of Physics: Conference Series 2018年第1期1142卷

作者： K Kalaiselvi K Velusamy C Gomathi Department of Computer Science and Applications The Oxford College of Science Bangalore Karnataka India Department of Computer Science Sri Vasavi College Erode Tamilnadu India Department of Computer Applications Vidyaa Vikas College of Engineering & Technology Tiruchengodu Tamilnadu India

Stock price prediction has recently brought together significant attention among the researchers. In the past decades, the stock price prediction has contributed a primary role in the stock market. The shareholder and investigators should give concentration a reliable method to predict stock price/index. An accurate prediction is an important to enlarge an effective market trading strategies. A back propagation neural network is widely used well known multi-layer supervised feed forward neural network algorithm since its simplicity and high problem-solving ability. In the traditional back propagation neural network, weight updating done by gradient decent based learning algorithm which is falling into local minima and learning rate is slow. Hence, keep away from above mentioned drawbacks; the opposition based learning (OBL) algorithm is used for weight adjustment in a back propagation neural network. The empirical result shows that the proposed prediction model demonstrates a superior performance in financial time series forecasting. For evaluating the performance of the proposed model, the empirical research is applied to well known stock market data sets such as S & P BSE Sensex and Nifty 50.

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Main belt asteroids taxonomical information from Dark Energy Survey data

arXiv

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arXiv 2023年

作者： Carruba, Valerio Camargo, Júlio I.B. Aljbaae, Safwan Ferreira, F.S. Lin, E. Figueiredo-Peixoto, V. Banda-Huarca, M.V. Pieres, A. Boufleur, R.C. da Costa, L.N. Abbott, T.M.C. Aguena, M. Allam, Sahar S. Alves, O. Bernardinelli, P.H. Bertin, E. Brooks, D. Carnero Rosell, A. Carretero, J. Pereira, M.E.S. Davis, T.M. De Vicente, J. Desai, S. Doel, P. Ferrero, I. Friedel, D. Frieman, J. García-Bellido, J. Gatti, M. Giannini, G. Gruen, D. Gruendl, R.A. Herner, K. Hinton, S.R. Hollowood, D.L. James, D.J. Kent, S. Kuehn, K. Lahav, O. Marshall, J.L. Mena-Fernández, J. Miquel, R. Palmese, A. Plazas Malagón, A.A. Rodríguez-Monroy, M. Sanchez, E. Santiago, B. Schubnell, M. Smith, M. Suchyta, E. Swanson, M.E.C. Tarle, G. Walker, A.R. Weaverdyck, N. Wiseman, P. School of Natural Sciences and Engineering Brazil Laboratório Interinstitucional de e-Astronomia - LIneA Brazil Observatório Nacional Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil Division of Space Mechanics and Control C.P. 515 SP São José dos Campos12227-310 Brazil Department of Physics University of Michigan Ann ArborMI48109 United States IGeo - Universidade Federal do Rio de Janeiro Av. Athos da Silveira Ramos 274 RJ Rio de Janeiro21941-916 Brazil Cerro Tololo Inter-American Observatory NSF’s National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Astronomy Department University of Washington Box 351580 SeattleWA98195 United States CNRS UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France Sorbonne Universités UPMC Univ Paris 06 UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Instituto de Astrofisica de Canarias Tenerife La LagunaE-38205 Spain Universidad de La Laguna Dpto. AstrofÃsica Tenerife La LagunaE-38206 Spain The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain Hamburger Sternwarte Universität Hamburg Gojenbergsweg 112 Hamburg21029 Germany School of Mathematics and Physics University of Queensland BrisbaneQLD4072 Australia Madrid Spain Department of Physics IIT Hyderabad Telangana Kandi502285 India Institute of Theoretical Astrophysics University of Oslo. P.O. Box 1029 Blindern OsloNO-0315 Norway Center for Astrophysical Surveys National Center for Supercomputing Applications 1205 West Clark St. UrbanaIL61801 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Instituto de Fisica Teorica UAM/CSIC Universidad Autonoma de Madrid Madrid28049 Spain Department

While proper orbital elements are currently available for more than 1 million asteroids, taxonomical information is still lagging behind. Surveys like SDSS-MOC4 provided preliminary information for more than 100,000 objects, but many asteroids still lack even a basic taxonomy. In this study, we use Dark Energy Survey (DES) data to provide new information on asteroid physical properties. By cross-correlating the new DES database with other databases, we investigate how asteroid taxonomy is reflected in DES data. While the resolution of DES data is not sufficient to distinguish between different asteroid taxonomies within the complexes, except for V-type objects, it can provide information on whether an asteroid belongs to the C- or S-complex. Here, machine learning methods optimized through the use of genetic algorithms were used to predict the labels of more than 68 000 asteroids with no prior taxonomic information. Using a high-quality, limited set of asteroids with data on gri slopes and i − z colors, we detected 409 new possible V-type asteroids. Their orbital distribution is highly consistent with that of other known V-type objects. © 2023, CC BY.

关键词： Taxonomies

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