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Leveraging Probe data and Machine Learning to Derive and Interpret Macroscopic Fundamental Diagrams Across U.S. cities

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Leveraging Probe Data and Machine Learning to Derive and Int...

IEEE International conference on Big data

作者： Ling Jin Xiaodan Xu Yuhan Wang Kaveh Farokhi Sadabadi Alina Lazar Duleep Rathgamage Don Zachary Needell c Anna Spurlock Mahyar Amirgholy Mona Asudegi Energy Technology Area Berkeley National Laboratory Berkeley CA Center for Advanced Transportation Technology University of Maryland College Park MD Department of Computer Science and Information Youngstown State University Youngstown OH School of Data Science and Analytics Kennesaw State University Marietta GA Energy Analysis and Environmental Impacts Division Lawrence Berkeley National Laboratory Berkeley CA Civil and Environmental Engineering Department Kennesaw State University Marietta GA Office of Transportation Policy Studies Federal Highway Administration Washington DC D.C.

Macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. Understanding network-wide traffic through MFDs can optimally allocate demand to existing networks, improving performance by maximizing network production and avoiding congestion. However, due to historical data limitations, empirically derived MFD models are sparse in the literature, especially for the U.S. cities. Leveraging a large-scale and granular census-tract-level flow and density derived from vehicle probe data, this research is the first to develop a machine learning approach to both derive MFD models and interpret their underlying difference among urban networks across the entire United States. Among the four machine learning methods tested here XGBoost is found to deliver the best performance to predict the network traffic flow for given vehicular density and location attributes. Interaction Shapley Additive explanation (SHAP) values are used to interpret the factors, such as land use, transportation infrastructure, and network topology, that influence the flow-density relationships among locations. The analysis framework developed in this work can generate datadriven MFDs and a deeper understanding of their shape dependence on network, infrastructure, and land use characteristics, which can be used by transportation authorities to derive and optimize location-specific MFDs facilitating more informed management and planning decisions at the network level.

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Metal Organic Framework cubosomes

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Angewandte chemie 2023年第11期135卷

作者： chen Li Yi Pan Tianyu Xiao Luoxing Xiang Dr. Qian Li Dr. Feng Tian Prof. Ian Manners Prof. Yiyong Mai School of Chemistry and Chemical Engineering Frontiers Science Center for Transformative Molecules Shanghai Key Laboratory of Electrical Insulation and Thermal Ageing Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China Department of Chemistry Centre for Advanced Materials and Related Technology (CAMTEC) University of Victoria 3800 Finnerty Road Victoria BC V8P 5C2 Canada Contribution: Data curation (lead) Formal analysis (lead) Investigation (lead) Writing - original draft (lead) Writing - review & editing (lead) Contribution: Data curation (equal) Formal analysis (equal) Investigation (equal) Writing - original draft (equal) Writing - review & editing (equal) Contribution: Data curation (supporting) Investigation (supporting) Writing - original draft (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) Investigation (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) Shanghai Synchrotron Radiation Facility Zhangjiang Lab Shanghai Advanced Research Institute Chinese Academy of Sciences 239 Zhangheng Road Shanghai 201204 China Contribution: Data curation (supporting) Formal analysis (supporting) Writing - original draft (supporting) Contribution: Conceptualization (supporting) Funding acquisition (supporting) Project administration (equal) Supervision (equal) Writing - original draft (supporting) Writing - review & editing (supporting) Contribution: Conceptualization (lead) Data curation (supporting) Formal analysis (lead) Funding acquisition (lead) Investigation (supporting) Methodology (lead) Project administration (lead) Supervision (lead) Writing - original draft (lead) Writing - review & editing (lead)

We demonstrate a general strategy for the synthesis of ordered bicontinuous-structured metal organic frameworks (MOFs) by using polymer cubosomes (Pcs) with a double primitive structure ( Im m symmetry) as the template. The filling of MOF precursors in the open channel of Pcs, followed by their coordination and removal of the template, generates MOF cubosomes with a single primitive topology ( Pm m ) and average mesopore diameters of 60–65 nm. Mechanism study reveals that the formation of ZIF-8 cubosomes undergoes a new MOF growth process, which involves the formation of individual MOF seeds in the template, their growth and eventual fusion into the cubosomes. Their growth kinetics follows the Avrami equation with an Avrami exponent of n =3 and a growth rate of k =1.33×10 −4 , indicating their fast 3D heterogeneous growth mode. Serving as a bioreactor, the ZIF-8 cubosomes show high loading of trypsin enzyme, leading to a high catalytic activity in the proteolysis of bovine serum albumin.

关键词： Bicontinuous Structure Block copolymer cubosome Metal Organic Framework Proteolysis

comparative immunologic profiling of mRNA and protein-conjugated vaccines: acute inflammatory responses and anti-PEG antibody production

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Integrative Biosciences 2025年第1期29卷 387-401页

作者： Gahyun Roh Jisun Lee Hyun-Mee Park Woori Kwak Hyo-Jung Park Ayoung Oh Yewon Na Dahyeon Ha Yu-Sun Lee Seo-Hyeon Bae Seonghyun Lee Subin Yoon Sowon Lee Jaehun Jung Jacob Lee Jae-Hwan Nam a Department of Medical and Biological Sciences The Catholic University of Korea Bucheon Gyeonggi-do Republic of Koreab BK Four Department of Biotechnology The Catholic University of Korea Bucheon Gyeonggi-do Republic of Korea a Department of Medical and Biological Sciences The Catholic University of Korea Bucheon Gyeonggi-do Republic of Korea c Advanced Analysis and Data Center Korea Institute of Science and Technology Seoul Republic of Korea d Department of Preventive Medicine Gachon University College of Medicine Incheon Republic of Korea e Division of Infectious Disease Department of Internal Medicine Kangnam Sacred Heart Hospital College of Medicine Hallym University Seoul Republic of Korea

ABSTRAcTMessenger ribonucleic acid (mRNA) vaccines have become a prevalent immunization method, even as the coronavirus disease 2019 (cOVID-19) pandemic recedes. However, the potential adverse effects using mRNA vaccines need to be explored in this evolving landscape. In this study, 60 participants were randomly assigned to receive either an mRNA vaccine, specifically for cOVID-19, or a conventional vaccine for meningococcal disease. Symptom records and blood samples were collected on Days 0, 3, and 7 after vaccination. Results showed that recipients of mRNA vaccines exhibited elevated levels of serum acute-phase proteins, such as haptoglobin and c-reactive protein, alongside decreased white blood cell counts compared to those receiving conventional vaccines. Proteomic analysis identified significant changes in nine proteins, including interactions involving complement component c9, haptoglobin, and alpha-1-acid glycoprotein, suggesting implications for complement activation and inflammatory responses. Furthermore, variability in anti-polyethylene glycol antibody levels was noted among mRNA vaccine recipients compared to conventional vaccine recipients. This research aims to provide useful information to help develop future vaccination strategies and shape research directions to mitigate individual adverse *** ABSTRAcTDisplay full size

关键词： mRNA cOVID-19 vaccination meningococcal vaccines c-reactive protein haptoglobin

community-developed checklists for publishing images and image analyses

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arXiv 2023年

作者： Schmied, christopher Nelson, Michael S. Avilov, Sergiy Bakker, Gert-Jan Bertocchi, cristina Bischof, Johanna Boehm, Ulrike Brocher, Jan carvalho, Mariana chiritescu, catalin christopher, Jana cimini, Beth A. conde-Sousa, Eduardo Ebner, Michael Ecker, Rupert Eliceiri, Kevin Fernandez-Rodriguez, Julia Gaudreault, Nathalie Gelman, Laurent Grunwald, David Gu, Tingting Halidi, Nadia Hammer, Mathias Hartley, Matthew Held, Marie Jug, Florian Kapoor, Varun Koksoy, Ayse Aslihan Lacoste, Judith Le Dévédec, Sylvia Le Guyader, Sylvie Liu, Penghuan Martins, Gabriel G. Mathur, Aastha Miura, Kota Llopis, Paula Montero Nitschke, Roland North, Alison Parslow, Adam c. Payne-Dwyer, Alex Plantard, Laure Ali, Rizwan Schroth-Diez, Britta Schütz, Lucas Scott, Ryan T. Seitz, Arne Selchow, Olaf Sharma, Ved P. Spitaler, Martin Srinivasan, Sathya Strambio-De-castillia, caterina Taatjes, Douglas Tischer, christian Jambor, HelenaKlara Fondazione Human Technopole Viale Rita Levi-Montalcini 1 Milano20157 Italy Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI53706 United States Max Planck Institute of Immunobiology and Epigenetics Freiburg79108 Germany Medical BioSciences department Radboud University Medical Centre Nijmegen Netherlands Laboratory for Molecular mechanics of cell adhesions Pontificia Universidad Católica de Chile Santiago Chile Osaka University Graduate School of Engineering Science Japan Euro-BioImaging ERIC Bio-Hub Meyerhofstr. 1 Heidelberg69117 Germany Carl Zeiss AG Carl-Zeiss-Straße 22 Oberkochen73447 Germany BioVoxxel Scientific Image Processing and Analysis Eugen-Roth-Strasse 8 Ludwigshafen67071 Germany Nanophotonics and BioImaging Facility at INL International Iberian Nanotechnology Laboratory 4715-330 Portugal Phi Optics Inc. 1800 S. Oak St Ste 106 ChampaignIL61820 United States Biochemistry Center Heidelberg Heidelberg University Germany Imaging Platform Broad Institute CambridgeMA02142 United States i3S Instituto de Investigação e Inovação Em Saúde and INEB Instituto de Engenharia Biomédica Universidade do Porto Porto Portugal Translational Research Institute Queensland University of Technology 37 Kent Street WoolloongabbaQLD4102 Australia School of Biomedical Sciences Faculty of Health Queensland University of Technology BrisbaneQLD4059 Australia TissueGnostics GmbH Vienna1020 Austria Department of Medical Physics and Biomedical Engineering University of Wisconsin-Madison MadisonWI53706 United States Centre for Cellular Imaging Core Facility Sahlgrenska Academy University of Gothenburg Sweden Allen Institute for Cell Science SeattleWA United States Friedrich Miescher Institute for Biomedical Research Basel Switzerland RNA Therapeutics Institute University of Massachusetts Chan Medical School WorcesterMA01605 United States University of Oklahoma NormanOK United States Advanced Light Microscopy Unit Ce

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However for scientists wishing to publish the obtained images and image analyses results, there are to date no unified guidelines. consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here we present community-developed checklists for preparing light microscopy images and image analysis for publications. These checklists offer authors, readers, and publishers key recommendations for image formatting and annotation, color selection, data availability, and for reporting image analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby heighten the quality and explanatory power of microscopy data is in publications. © 2023, cc BY-SA.

关键词： Image analysis

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INTRODUcTION

Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including china and continental Europe as a whole (warmest on record), India and Russia (second warmest), and canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°c was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°c at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°c in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°c above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Brain modelling as a service: The virtual brain on EBRAINS

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arXiv 2021年

作者： Schirner, Michael Domide, Lia Perdikis, Dionysios Triebkorn, Paul Stefanovski, Leon Pai, Roopa Prodan, Paula Valean, Bogdan Palmer, Jessica Langford, chloê Blickensdörfer, André van der Vlag, Michiel Diaz-Pier, Sandra Peyser, Alexander Klijn, Wouter Pleiter, Dirk Nahm, Anne Schmid, Oliver Woodman, Marmaduke Zehl, Lyuba Fousek, Jan Petkoski, Spase Kusch, Lionel Hashemi, Meysam Marinazzo, Daniele Mangin, Jean-François Flöel, Agnes Akintoye, Simisola Stahl, Bernd carsten cepic, Michael Johnson, Emily Deco, Gustavo McIntosh, Anthony R. Hilgetag, claus c. Morgan, Marc Schuller, Bernd Upton, Alex McMurtrie, colin Dickscheid, Timo Bjaalie, Jan G. Amunts, Katrin Mersmann, Jochen Jirsa, Viktor Ritter, Petra Berlin Institute of Health Charité - Universitätsmedizin Berlin Charitéplatz 1 Berlin10117 Germany Charité - Universitätsmedizin Berlin Freie Universität Berlin Humboldt Universität zu Berlin Department of Neurology with Experimental Neurology Charitéplatz 1 Berlin10117 Germany Bernstein Focus State Dependencies of Learning Bernstein Center for Computational Neuroscience Berlin Germany Einstein Center for Neuroscience Berlin Charitéplatz 1 Berlin10117 Germany Einstein Center Digital Future Wilhelmstraße 67 Berlin10117 Germany Codemart Str. Petofi Sandor Cluj Napoca Romania Institut de Neurosciences des Systèmes Aix Marseille Université Marseille France Institute for Advanced Simulation JARA Forschungszentrum Jülich GmbH Jülich Germany Jülich Supercomputer Centre Forschungszentrum Jülich Jülich Germany Institut für Theoretische Physik Universität Regensburg Regensburg Germany Campus Biotech Geneva Switzerland Research Centre Jülich Jülich Germany Department of Data-Analysis Faculty of Psychology and Educational Sciences Ghent University Belgium San Camillo IRCSS Venice Italy Université Paris-Saclay CEA CNRS Neurospin Baobab Gif-sur-Yvette France Department of Neurology University Medicine Greifswald Greifswald Germany Standort Greifswald Greifswald Germany Leicester De Montfort Law School De Montfort University Leicester United Kingdom Centre for Computing and Social Responsibility De Montfort University Leicester United Kingdom Department of Innovation and Digitalisation in Law Faculty of Law University of Vienna Austria Center for Brain and Cognition Computational Neuroscience Group Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain Institució Catalana de la Recerca i Estudis Avançats Barcelona Spain Department of Neuropsychology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany School of Psychological Sciences Turner Institute for Brain and Mental Health

The Virtual Brain (TVB) is now available as open-source cloud ecosystem on EBRAINS, a shared digital research platform for brain science. It offers services for constructing, simulating and analysing brain network models (BNMs) including the TVB network simulator;magnetic resonance imaging (MRI) processing pipelines to extract structural and functional connectomes;multiscale co-simulation of spiking and large-scale networks;a domain specific language for automatic high-performance code generation from user-specified models;simulation-ready BNMs of patients and healthy volunteers;Bayesian inference of epilepsy spread;data and code for mouse brain simulation;and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability and clinical translation. © 2021, cc BY.

关键词： Open source software

Stroke genetics informs drug discovery and risk prediction across ancestries (vol 611, pg 115, 2022)

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NATURE 2022年第7938期612卷 E7-E7页

作者： Mishra, Aniket Malik, Rainer Hachiya, Tsuyoshi Jurgenson, Tuuli Namba, Shinichi Posner, Daniel c. Kamanu, Frederick K. Koido, Masaru Le Grand, Quentin Shi, Mingyang He, Yunye Georgakis, Marios K. caro, Ilana Krebs, Kristi Liaw, Yi-ching Vaura, Felix c. Lin, Kuang Winsvold, Bendik Slagsvold Srinivasasainagendra, Vinodh Parodi, Livia Bae, Hee-Joon chauhan, Ganesh chong, Michael R. Tomppo, Liisa Akinyemi, Rufus Roshchupkin, Gennady V. Habib, Naomi Jee, Yon Ho Thomassen, Jesper Qvist Abedi, Vida carcel-Marquez, Jara Nygaard, Marianne Leonard, Hampton L. Yang, chaojie Yonova-Doing, Ekaterina Knol, Maria J. Lewis, Adam J. Judy, Renae L. Ago, Tetsuro Amouyel, Philippe Armstrong, Nicole D. Bakker, Mark K. Bartz, Traci M. Bennett, David A. Bis, Joshua c. Bordes, constance Borte, Sigrid cain, Anael Ridker, Paul M. cho, Kelly chen, Zhengming cruchaga, carlos cole, John W. de Jager, Phil L. de cid, Rafael Endres, Matthias Ferreira, Leslie E. Geerlings, Mirjam I. Gasca, Natalie c. Gudnason, Vilmundur Hata, Jun He, Jing Heath, Alicia K. Ho, Yuk-Lam Havulinna, Aki S. Hopewell, Jemma c. Hyacinth, I. Hyacinth Inouye, Michael Jacob, Mina A. Jeon, christina E. Jern, christina Kamouchi, Masahiro Keene, Keith L. Kitazono, Takanari Kittner, Steven J. Konuma, Takahiro Kumar, Amit Lacaze, Paul Launer, Lenore J. Lee, Keon-Joo Lepik, Kaido Li, Jiang Li, Liming Manichaikul, Ani Markus, Hugh S. Marston, Nicholas A. Meitinger, Thomas Mitchell, Braxton D. Montellano, Felipe A. Morisaki, Takayuki Mosley, Thomas H. Nalls, Mike A. Nordestgaard, Borge G. O'Donnell, Martin J. Okada, Yukinori Onland-Moret, N. charlotte Ovbiagele, Bruce Peters, Annette Psaty, Bruce M. Rich, Stephen S. Rosand, Jonathan Sabatine, Marc S. Sacco, Ralph L. Saleheen, Danish Sandset, Else charlotte Salomaa, Veikko Sargurupremraj, Muralidharan Sasaki, Makoto Satizabal, claudia L. Schmidt, carsten O. Shimizu, Atsushi Smith, Nicholas L. Sloane, Kelly L. Sutoh, Yoichi Sun, Yan V. Tanno, Kozo Tiedt, Steffen Tatlisumak, Turgut Torres-Aguila, Nuria P. Tiwari, Hemant K. Tregouet, Bordeaux Population Health Research Center University of Bordeaux Inserm UMR 1219 Bordeaux France Department of Public Health Bordeaux University Hospital Bordeaux France Department of Neurology Memory Clinic Bordeaux University Hospital Bordeaux France Department of Neurology Institute for Neurodegenerative Diseases CHU de Bordeaux Bordeaux France Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Munich Germany Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of Harvard and the Massachusetts Institute of Technology Cambridge MA USA Munich Cluster for Systems Neurology Munich Germany German Center for Neurodegenerative Diseases (DZNE) Munich Germany Iwate Tohoku Medical Megabank Organization Iwate Medical University Iwate Japan Estonian Genome Centre Institute of Genomics University of Tartu Tartu Estonia Institute of Mathematics and Statistics University of Tartu Tartu Estonia Department of Computational Biology University of Lausanne Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland University Center for Primary Care and Public Health Lausanne Switzerland Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan Department of Genome Informatics Graduate School of Medicine The University of Tokyo Tokyo Japan Laboratory for Systems Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan Integrated Frontier Research for Medical Science Division Institute for Open and Transdisciplinary Research Initiatives Osaka University Suita Japan Center for Infectious Disease Education and Research (CiDER) Osaka University Suita Japan Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA TIMI S

cOSMOS2020: A panchromatic view of the universe to z ∼ 10 from two complementary catalogs

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arXiv 2021年

作者： Weaver, John R. Kauffmann, O.B. Ilbert, O. Mccracken, H.J. Moneti, A. Toft, S. Brammer, G. Shuntov, M. Davidzon, I. Hsieh, B.c. Laigle, c. Anastasiou, A. Jespersen, c.K. Vinther, J. capak, P. casey, c.M. McPartland, c.J.R. Milvang-Jensen, B. Mobasher, B. Sanders, D.B. Zalesky, L. Arnouts, S. Aussel, H. Dunlop, J.S. Faisst, A. Franx, M. Furtak, L.J. Fynbo, J.P.U. Gould, K.M.L. Greve, T.R. Gwyn, S. Kartaltepe, J.S. Kashino, D. Koekemoer, A.M. Kokorev, V. Le Fèvre, O. Lilly, S. Masters, D. Magdis, G. Mehta, V. Peng, Y. Riechers, D.A. Salvato, M. Sawicki, M. Scarlata, c. Scoville, N. Shirley, R. Sneppen, A. Smolčić, V. Steinhardt, c. Stern, D. Tanaka, M. Taniguchi, Y. Teplitz, H.I. Vaccari, M. Wang, W.-H. Zamorani, G. Niels Bohr Institute University of Copenhagen Jagtvej 128 Copenhagen2200 Denmark Aix Marseille Univ CNRS LAM Laboratoire d’Astrophysique de Marseille Marseille France Institut d’Astrophysique de Paris UMR 7095 CNRS Sorbonne Université 98 bis boulevard Arago Paris75014 France Institute of Astrophysics & Astronomy Academia Sinica Taipei10617 Taiwan The University of Texas at Austin 2515 Speedway Blvd Stop C1400 AustinTX78712 United States Institute for Astronomy University of Hawaii 2680 Woodlawn Drive HonoluluHI96822 United States Department of Physics and Astronomy University of California Riverside 900 University Avenue RiversideCA92521 United States AIM UMR 7158 CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité Gif-sur-YvetteF-91191 France Institute for Astronomy University of Edinburgh Royal Observatory EdinburghEH9 3HJ United Kingdom Infrared Processing and Analysis Center California Institute of Technology 1200 E. California Blvd PasadenaCA91125 United States Leiden Observatory Leiden University P.O.Box 9513 LeidenNL-2300 AA Netherlands DTU-Space Technical University of Denmark Elektrovej 327 Kgs. LyngbyDK2800 Denmark Department of Physics and Astronomy University College London Gower Place LondonWC1E 6BT United Kingdom Herzberg Astronomy and Astrophysics National Research Council of Canada 5071 West Saanich Rd. VictoriaBCV9E 2E7 Canada School of Physics and Astronomy Institute of Technology 84 Lomb Memorial Drive RochesterNY14623 United States Department of Physics ETH Zürich Wolfgang-Pauli-Strasse 27 Zurich8093 Switzerland Institute for Advanced Research Nagoya University Furocho Chikusa Aichi Nagoya464-8602 Japan Space Telescope Science Institute 3700 San Martin Dr. BaltimoreMD21218 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States Institute for Astronomy Astrophysics Space Applications and Remo

The cosmic Evolution Survey (cOSMOS) has become a cornerstone of extragalactic astronomy. Since the last public catalog in 2015, a wealth of new imaging and spectroscopic data has been collected in the cOSMOS field. This paper describes the collection, processing, and analysis of this new imaging data to produce a new reference photometric redshift catalog. Source detection and multi-wavelength photometry is performed for 1.7 million sources across the 2 deg2 of the cOSMOS field, ∼966 000 of which are measured with all available broad-band data using both traditional aperture photometric methods and a new profile-fitting photometric extraction tool, The Farmer, which we have developed. A detailed comparison of the two resulting photometric catalogs is presented. Photometric redshifts are computed for all sources in each catalog utilizing two independent photometric redshift codes. Finally, a comparison is made between the performance of the photometric methodologies and of the redshift codes to demonstrate an exceptional degree of self-consistency in the resulting photometric redshifts. The i © 2021, cc BY.

关键词： Photometry

Euclid Quick data Release (Q1) combined Euclid and Spitzer galaxy density catalogues at z>1.3 and detection of significant Euclid passive galaxy overdensities in Spitzer overdense regions

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arXiv 2025年

作者： Mai, N. Mei, S. cleland, c. chary, R. Bartlett, J.G. castignani, G. Dannerbauer, H. De Lucia, G. Fontanot, F. Scott, D. Andreon, S. Bhargava, S. Dole, H. Dussere, T. Stanford, S.A. Tran, V.P. Weaver, J.R. Duc, P.-A. Risso, I. Aghanim, N. Altieri, B. Amara, A. Auricchio, N. Aussel, H. Baccigalupi, c. Baldi, M. Balestra, A. Bardelli, S. Battaglia, P. Biviano, A. Bonchi, A. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. caillat, A. camera, S. cañas-Herrera, G. capobianco, V. carbone, c. carretero, J. casas, S. castellano, M. cavuoti, S. chambers, K.c. cimatti, A. colodro-conde, c. congedo, G. conselice, c.J. conversi, L. copin, Y. courbin, F. courtois, H.M. Da Silva, A. Degaudenzi, H. Di Giorgio, A.M. Douspis, M. Dubath, F. Duncan, c.A.J. Dupac, X. Dusini, S. Ealet, A. Escoffier, S. Farina, M. Farinelli, R. Faustini, F. Ferriol, S. Finelli, F. Fosalba, P. Fotopoulou, S. Frailis, M. Franceschi, E. Fumana, M. Galeotta, S. George, K. Gillis, B. Giocoli, c. Gracia-carpio, J. Granett, B.R. Grazian, A. Grupp, F. Haugan, S.V.H. Holmes, W. Hormuth, F. Hornstrup, A. Hudelot, P. Jahnke, K. Jhabvala, M. Keihänen, E. Kermiche, S. Kiessling, A. Kubik, B. Kümmel, M. Kunz, M. Kurki-Suonio, H. Le Boulc’h, Q. Le Brun, A.M.c. Le Mignant, D. Ligori, S. Lilje, P.B. Lindholm, V. Lloro, I. Mainetti, G. Maino, D. Maiorano, E. Mansutti, O. Marggraf, O. Martinelli, M. Martinet, N. Marulli, F. Massey, R. Maurogordato, S. Medinaceli, E. Mellier, Y. Meneghetti, M. Merlin, E. Meylan, G. Mora, A. Moresco, M. Moscardini, L. Nakajima, R. Neissner, c. Niemi, S.-M. Nightingale, J.W. Padilla, c. Paltani, S. Pasian, F. Pedersen, K. Percival, W.J. Pettorino, V. Pires, S. Polenta, G. Poncet, M. Popa, L.A. Pozzetti, L. Raison, F. Renzi, A. Rhodes, J. Riccio, G. Romelli, E. Roncarelli, M. Saglia, R. Sakr, Z. Sapone, D. Sartoris, B. Schewtschenko, J.A. Schneider, P. Scodeggio, M. Secroun, A. Seidel, G. Seiffert, M. Serrano, S. Université Paris Cité CNRS Astroparticule et Cosmologie Paris75013 France CNRS-UCB International Research Laboratory Centre Pierre Binetruy IRL2007 CPB-IN2P3 Berkeley United States Infrared Processing and Analysis Center California Institute of Technology PasadenaCA91125 United States University of California Los AngelesCA90095-1562 United States INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy Tenerife La Laguna38200 Spain INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy Department of Physics and Astronomy University of British Columbia VancouverBCV6T 1Z1 Canada INAF-Osservatorio Astronomico di Brera Via Brera 28 Milano20122 Italy Université Côte d’Azur Observatoire de la Côte d’Azur CNRS Laboratoire Lagrange Bd de l’Observatoire CS 34229 Nice06304 cedex 4 France Université Paris-Saclay CNRS Institut d’astrophysique spatiale Orsay91405 France Department of Physics and Astronomy University of California DavisCA95616 United States Department of Astronomy University of Massachusetts AmherstMA01003 United States Université de Strasbourg CNRS Observatoire astronomique de Strasbourg UMR 7550 Strasbourg67000 France INFN-Sezione di Genova Via Dodecaneso 33 Genova16146 Italy ESAC/ESA Camino Bajo del Castillo s/n. Urb. Villafranca del Castillo Madrid Villanueva de la Cañada28692 Spain School of Mathematics and Physics University of Surrey Guild-ford SurreyGU2 7XH United Kingdom Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-Yvette91191 France INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy SISSA International School for Advanced Studies Via Bonomea 265 TS Trieste34136 Italy Dipartimento di Fisica e Astronomia Università di Bologna Via Gobetti 93/2 Bologna40129 Italy INFN-Sezione di Bologna Viale Berti

The Euclid spacecraft will detect tens of thousands of clusters and protoclusters at z > 1.3 over the course of its mission. With a total coverage of 63.1 deg2, the Euclid Quick data Release 1 (Q1) is large enough to detect tens of clusters and hundreds of protoclusters at these early epochs. The Q1 photometric redshift catalogue enables us to detect clusters out to z 1.5;however, infrared imaging from the Spitzer Space Telescope extends this limit to higher redshifts by using high local projected densities of Spitzer-selected galaxies as signposts for cluster and protocluster candidates. We use Spitzer imaging of the Euclid Deep Fields (EDFs) to derive densities for a sample of Spitzer-selected galaxies at redshifts z > 1.3, building Spitzer IRAc1 and IRAc2 photometric catalogues that are 95% complete at a magnitude limit of IRAc2 =22.2, 22.6, and 22.8 for the Euclid Deep Field South, Euclid Deep Field Fornax, and Euclid Deep Field North, respectively. We apply two complementary methods to calculate galaxy densities: (1) aperture and surface density;and (2) the Nth-nearest-neighbour method. When considering a sample selected at a magnitude limit of IRAc2 1.3, and our catalogues will allow us to extend cluster and protocluster detections to z > 1.3 in the EDFs. © 2025, cc BY.

关键词： Active Galactic Nuclei