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Risk-based decision making: estimands for sequential prediction under interventions

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arXiv 2023年

作者： Luijken, Kim Morzywolek, Pawel van Amsterdam, Wouter Cinà, Giovanni Hoogland, Jeroen Keogh, Ruth Krijthe, Jesse Magliacane, Sara van Ommen, Thijs Peek, Niels Putter, Hein van Smeden, Maarten Sperrin, Matthew Wang, Junfeng Weir, Daniala Didelez, Vanessa van Geloven, Nan Department of Epidemiology Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Statistics University of Washington Seattle United States Department of Medical Informatics Amsterdam University Medical Centers Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands Department of Epidemiology and Data Science Amsterdam University Medical Centers Amsterdam Netherlands Department of Medical Statistics London School of Hygiene & Tropical Medicine Keppel Street London United Kingdom Pattern Recognition and Bio-Informatics Group EEMCS Delft University of Technology Delft Netherlands Amsterdam Machine Learning Lab University of Amsterdam Amsterdam Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology Department of Pharmaceutical Sciences Utrecht University Utrecht Netherlands Department of Biometry and Data Management Leibniz Institute for Prevention Research Epidemiology - BIPS Bremen Germany Faculty of Mathematics/Computer Science University of Bremen Bremen Germany

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions. © 2023, CC BY.

关键词： Forecasting

UFLUX v2.0: A Process-Informed Machine Learning Framework for Efficient and Explainable Modelling of Terrestrial Carbon Uptake

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IEEE Geoscience and Remote Sensing Letters 2025年

作者： Dong, Wenquan Zhu, Songyan Xu, Jian Ryan, Casey M. Chen, Man Zeng, Jingya Yu, Hao Cao, Congfeng Shi, Jiancheng School of Geosciences University of Edinburgh EdinburghEH9 3FF United Kingdom Royal Botanic Garden Edinburgh EdinburghEH3 5LR United Kingdom Faculty of Environmental and Life Sciences University of Southampton SouthamptonSO17 1BJ United Kingdom National Space Science Center Chinese Academy of Sciences Beijing100190 China LERMA/Observatoire de Paris Paris75014 France Sorbonne University Paris75005 France Department of Economics Faculty of Environment Science and Economy University of Exeter ExeterEX4 4PU United Kingdom School of Engineering Institute for Imaging Data and Communications University of Edinburgh EdinburghEH9 3JL United Kingdom Institute for Logic Language and Computation University of Amsterdam Amsterdam1098 XH Netherlands

Gross Primary Productivity (GPP), the amount of carbon plants fixed by photosynthesis, is pivotal for understanding the global carbon cycle and ecosystem functioning. Processbased models built on the knowledge of ecological processes, are susceptible to biases stemming from their assumptions and approximations. These limitations potentially result in considerable uncertainties in global GPP estimation, which may pose significant challenges to our Net Zero goals. This study presents UFLUX v2.0, a process-informed model that integrates state-of-the-art ecological knowledge and advanced machine learning technique to reduce uncertainties in GPP estimation by learning the biases between process-based models and eddy covariance (EC) measurements. In our findings, UFLUX v2.0 demonstrated a substantial improvement in model accuracy, achieving an R2 of 0.79 with a reduced RMSE of 1.60 gCm-2 d-1, compared to the process-based model's R2 of 0.51 and RMSE of 3.09 gCm-2 d-1. Our global GPP distribution analysis indicates that while UFLUX v2.0 and the process-based model achieved similar global total GPP (137.47 PgC and 132.23 Pg C, respectively), they exhibited large differences in spatial distribution, particularly in latitudinal gradients. These differences are very likely due to systematic biases in the process-based model and differing sensitivities to climate and environmental conditions. This study offers improved adaptability for GPP modelling across diverse ecosystems, and further enhances our understanding of global carbon cycles and its responses to environmental changes. © 2025 IEEE.

关键词： Carbon capture and utilization

POSITIVE AND NEGATIVE SQUARE ENERGIES OF GRAPHS

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arXiv 2023年

作者： Abiad, Aida de Lima, Leonardo Desai, Dheer Noal Guo, Krystal Hogben, Leslie Madrid, José Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Department of Mathematics: Analysis Logic and Discrete Mathematics Ghent University Ghent Belgium Department of Mathematics and Data Science Vrije Universiteit Brussel Brussels Belgium Departamento de Administração Geral e Aplicada Federal University of Parana Curitiba Brazil Department of Mathematical Sciences University of Delaware DE United States Department of Mathematics and Statistics University of Wyoming WY United States Korteweg-de Vries Institute University of Amsterdam Amsterdam Netherlands Department of Mathematics Iowa State University AmesIA United States American Institute of Mathematics San JoseCA United States Section de Mathematiques Universite de Geneve Geneva Switzerland

The energy of a graph G is the sum of the absolute values of the eigenvalues of the adjacency matrix of G. Let s+(G), s−(G) denote the sum of the squares of the positive and negative eigenvalues of G, respectively. It was conjectured by [Elphick, Farber, Goldberg, Wocjan, Discrete Math. (2016)] that if G is a connected graph of order n, then s+(G) ≥ n − 1 and s−(G) ≥ n − 1. In this paper, we show partial results towards this conjecture. In particular, numerous structural results that may help in proving the conjecture are derived, including the effect of various graph operations. These are then used to establish the conjecture for several graph classes, including graphs with certain fraction of positive eigenvalues and unicyclic graphs. Copyright © 2023, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

UFLUX v2.0: A Process-Informed Machine Learning Framework for Efficient and Explainable Modelling of Terrestrial Carbon Uptake

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arXiv 2024年

作者： Dong, Wenquan Zhu, Songyan Xu, Jian Ryan, Casey M. Chen, Man Zeng, Jingya Yu, Hao Cao, Congfeng Shi, Jiancheng The School of Geosciences University of Edinburgh EdinburghEH9 3FF United Kingdom Royal Botanic Garden Edinburgh EdinburghEH3 5LR United Kingdom The Faculty of Environmental and Life Sciences University of Southampton SouthamptonSO17 1BJ United Kingdom The National Space Science Center Chinese Academy of Sciences Beijing100190 China LERMA Observatoire de Paris Paris75014 France Sorbonne University Paris75005 France The Department of Economics Faculty of Environment Science and Economy University of Exeter ExeterEX4 4PU United Kingdom The School of Engineering Institute for Imaging Data and Communications University of Edinburgh EdinburghEH9 3JL United Kingdom The Institute for Logic Language and Computation University of Amsterdam Amsterdam1098 XH Netherlands

Gross Primary Productivity (GPP), the amount of carbon plants fixed by photosynthesis, is pivotal for understanding the global carbon cycle and ecosystem functioning. Process-based model built on the knowledge of ecological processes, are susceptible to biases stemming from their assumptions and approximations. These limitations potentially result in considerable uncertainties in global GPP estimation, which may pose significant challenges to our Net Zero goals. This study presents UFLUX v2.0, a process-informed model that integrates state-of-art ecological knowledge and advanced machine learning technique to reduce uncertainties in GPP estimation by learning the biases between process-based models and eddy covariance (EC) measurements. In our findings, UFLUX v2.0 demonstrated a substantial improvement in model accuracy, achieving an R2 of 0.79 with a reduced RMSE of 1.60 g C m−2 d−1, compared to the process-based model’s R2 of 0.51 and RMSE of 3.09 g C m−2 d−1. Our global GPP distribution analysis indicates that while UFLUX v2.0 and the process-based model achieved similar global total GPP (137.47 Pg C and 132.23 Pg C, respectively), they exhibited large differences in spatial distribution, particularly in latitudinal gradients. These differences are very likely due to systematic biases in the process-based model and differing sensitivities to climate and environmental conditions. This study offers improved adaptability for GPP modelling across diverse ecosystems, and further enhances our understanding of global carbon cycles and its responses to environmental changes. Copyright © 2024, The Authors. All rights reserved.

关键词： Carbon sequestration

First-order rewritability of frontier-guarded ontology-mediated queries

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arXiv 2020年

作者： Barceló, Pablo Berger, Gerald Lutz, Carsten Pieris, Andreas Millennium Institute for Foundational Research on Data & DCC University of Chile Chile Institute of Logic and Computation TU Wien Department of Mathematics and Computer Science University of Bremen Germany School of Informatics University of Edinburgh United Kingdom

We focus on ontology-mediated queries (OMQs) based on (frontier-)guarded existential rules and (unions of) conjunctive queries, and we investigate the problem of FO-rewritability, i.e., whether an OMQ can be rewritten as a first-order query. We adopt two different approaches. The first approach employs standard two-way alternating parity tree automata. Although it does not lead to a tight complexity bound, it provides a transparent solution based on widely known tools. The second approach relies on a sophisticated automata model, known as cost automata. This allows us to show that our problem is 2EXPTIME-complete. In both approaches, we provide semantic characterizations of FO-rewritability that are of independent interest. © 2020, CC-BY.

关键词： Ontology

Bordering of Symmetric Matrices and an Application to the Minimum Number of Distinct Eigenvalues for the Join of Graphs

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arXiv 2023年

作者： Abiad, Aida Fallat, Shaun M. Kempton, Mark Levene, Rupert H. Oblak, Polona Šmigoc, Helena Tait, Michael Meulen, Kevin Vander Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Department of Mathematics: Analysis Logic and Discrete Mathematics Ghent University Ghent Belgium Department of Mathematics and Data Science Vrije Universiteit Brussel Brussels Belgium Department of Mathematics and Statistics University of Regina ReginaSKS4S 0A2 Canada Department of Mathematics Brigham Young University ProvoUT84602 United States School of Mathematics and Statistics University College Dublin BelfieldDublin 4 Ireland University of Ljubljana Faculty of Computer and Information Science Faculty of Mathematics and Physics Ljubljana Slovenia Institute of Mathematics Physics and Mechanics Ljubljana Slovenia Department of Mathematics & Statistics Villanova University VillanovaPA19085 United States Department of Mathematics Redeemer University ONL9K 1J4 Canada

An important facet of the inverse eigenvalue problem for graphs is to determine the minimum number of distinct eigenvalues of a particular graph. We resolve this question for the join of a connected graph with a path. We then focus on bordering a matrix and attempt to control the change in the number of distinct eigenvalues induced by this operation. By applying bordering techniques to the join of graphs, we obtain numerous results on the nature of the minimum number of distinct eigenvalues as vertices are joined to a fixed *** Codes 05C50, 15A18 © 2023, CC BY.

关键词： Matrix algebra

SPT clusters with DES and HST weak lensing. II. Cosmological constraints from the abundance of massive halos

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Physical Review D 2024年第8期110卷 083510页

作者： S. Bocquet S. Grandis L. E. Bleem M. Klein J. J. Mohr T. Schrabback T. M. C. Abbott P. A. R. Ade M. Aguena A. Alarcon S. Allam S. W. Allen O. Alves A. Amon A. J. Anderson J. Annis B. Ansarinejad J. E. Austermann S. Avila D. Bacon M. Bayliss J. A. Beall K. Bechtol M. R. Becker A. N. Bender B. A. Benson G. M. Bernstein S. Bhargava F. Bianchini M. Brodwin D. Brooks L. Bryant A. Campos R. E. A. Canning J. E. Carlstrom A. Carnero Rosell M. Carrasco Kind J. Carretero F. J. Castander R. Cawthon C. L. Chang C. Chang P. Chaubal R. Chen H. C. Chiang A. Choi T-L. Chou R. Citron C. Corbett Moran J. Cordero M. Costanzi T. M. Crawford A. T. Crites L. N. da Costa M. E. S. Pereira C. Davis T. M. Davis J. DeRose S. Desai T. de Haan H. T. Diehl M. A. Dobbs S. Dodelson C. Doux A. Drlica-Wagner K. Eckert J. Elvin-Poole S. Everett W. Everett I. Ferrero A. Ferté A. M. Flores J. Frieman J. Gallicchio J. García-Bellido M. Gatti E. M. George G. Giannini M. D. Gladders D. Gruen R. A. Gruendl N. Gupta G. Gutierrez N. W. Halverson I. Harrison W. G. Hartley K. Herner S. R. Hinton G. P. Holder D. L. Hollowood W. L. Holzapfel K. Honscheid J. D. Hrubes N. Huang J. Hubmayr E. M. Huff D. Huterer K. D. Irwin D. J. James M. Jarvis G. Khullar K. Kim L. Knox R. Kraft E. Krause K. Kuehn N. Kuropatkin F. Kéruzoré O. Lahav A. T. Lee P.-F. Leget D. Li H. Lin A. Lowitz N. MacCrann G. Mahler A. Mantz J. L. Marshall J. McCullough M. McDonald J. J. McMahon J. Mena-Fernández F. Menanteau S. S. Meyer R. Miquel J. Montgomery J. Myles T. Natoli A. Navarro-Alsina J. P. Nibarger G. I. Noble V. Novosad R. L. C. Ogando Y. Omori S. Padin S. Pandey P. Paschos S. Patil A. Pieres A. A. Plazas Malagón A. Porredon J. Prat C. Pryke M. Raveri C. L. Reichardt J. Roberson R. P. Rollins C. Romero A. Roodman J. E. Ruhl E. S. Rykoff B. R. Saliwanchik L. Salvati C. Sánchez E. Sanchez D. Sanchez Cid A. Saro K. K. Schaffer L. F. Secco I. Sevilla-Noarbe K. Sharon E. Sheldon T. Shin C. Sievers G. Smecher M. Smith T. Somboonpanyakul M. Sommer B. Stalder A. A. Stark J. Stephen V. Straz University Observatory Faculty of Physics Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstraße 25/8 6020 Innsbruck Austria 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 Max Planck Institute for Extraterrestrial Physics Gießenbachstraße 1 85748 Garching Germany Argelander-Institut für Astronomie Auf dem Hügel 71 53121 Bonn Germany Cerro Tololo Inter-American Observatory NSF’s National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile School of Physics and Astronomy Cardiff University Cardiff CF24 3YB United Kingdom Laboratório Interinstitucional de e-Astronomia—LIneA Rua Gal. José Cristino 77 Rio de Janeiro Rio de Janeiro—20921-400 Brazil Fermi National Accelerator Laboratory P. O. Box 500 Batavia Illinois 60510 USA Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall Stanford California 94305 USA Department of Physics Stanford University 382 Via Pueblo Mall 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 Institute of Astronomy University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom School of Physics University of Melbourne Parkville Victoria 3010 Australia NIST Quantum Devices Group 325 Broadway Mailcode 817.03 Boulder Colorado 80305 USA Department of Physics University of Colorado Boulder Colorado 80309 USA Institut de Física d’Altes Energies (IFAE) The Barcelona Institute of Science and Technology Campus UAB 08193 Bellaterra (Barcelona) Spain Institute of Cosmology and Gravitation University of Portsmouth Portsmouth

We present cosmological constraints from the abundance of galaxy clusters selected via the thermal Sunyaev-Zel’dovich (SZ) effect in South Pole Telescope (SPT) data with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). The cluster sample is constructed from the combined SPT-SZ, SPTpol ECS, and SPTpol 500d surveys, and comprises 1,005 confirmed clusters in the redshift range 0.25–1.78 over a total sky area of 5200 deg2. We use DES Year 3 weak-lensing data for 688 clusters with redshifts z<0.95 and HST weak-lensing data for 39 clusters with 0.6dataset, we place a 95% upper limit on the sum of neutrino masses ∑mν<0.18 eV. When additionally allowing the dark energy equation of state parameter w to vary, we obtain w=−1.45±0.31 from our cluster-based analysis. In combination with Planck data, we measure w=−1.34−0.15+0.22, or a 2.2σ difference with a cosmological constant. We use the cluster abundance to measure σ8 in five redshift bins between 0.25 and 1.8, and we find the results to be consistent with structure growth as predicted by the ΛCDM model fit to Planck primary CMB data.

关键词： Cosmological parameters Dark energy Large scale structure of the Universe Galaxy clusters

STAGER checklist: Standardized testing and assessment guidelines for evaluating generative artificial intelligence reliability

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iMetaOmics

iMetaOmics 2024年第1期1卷

作者： Jinghong Chen Lingxuan Zhu Weiming Mou Anqi Lin Dongqiang Zeng Chang Qi Zaoqu Liu Aimin Jiang Bufu Tang Wenjie Shi Ulf D. Kahlert Jianguo Zhou Shipeng Guo Xiaofan Lu Xu Sun Trunghieu Ngo Zhongji Pu Baolei Jia Che Ok Jeon Yongbin He Haiyang Wu Shuqin Gu Wisit Cheungpasitporn Haojie Huang Weipu Mao Shixiang Wang Xin Chen Loïc Cabannes Gerald Sng Gui Ren Iain S. Whitaker Stephen Ali Quan Cheng Kai Miao Shuofeng Yuan Peng Luo Department of Oncology Zhujiang Hospital Southern Medical University Guangzhou China The Second School of Clinical Medicine Southern Medical University Guangzhou China Department of Urology Shanghai General Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Oncology Nanfang Hospital Southern Medical University Guangzhou China Institute of Logic and Computation TU Wien Wien Austria Institute of Basic Medical Sciences Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China Department of Urology Changhai Hospital Naval Medical University (Second Military Medical University) Shanghai China Department of Radiation Oncology Zhongshan Hospital Fudan University Shanghai China Molecular and Experimental Surgery University Clinic for General- Visceral- Vascular- and Trans-Plantation Surgery Medical Faculty University Hospital Magdeburg Otto-von Guericke University Magdeburg Germany Department of Oncology The Second Affiliated Hospital of Zunyi Medical University Zunyi China Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Erlangen Germany Comprehensive Cancer Center Erlangen-EMN Erlangen Germany GZDLab Chongqing China Department of Cancer and Functional Genomics Institute of Genetics and Molecular and Cellular Biology CNRS/INSERM/UNISTRA Illkirch France Linguistique Informatique UFR-Linguistique Université Paris Cité Paris France Xianghu Laboratory Hangzhou China Department of Life Science Chung-Ang University Seoul Korea School of Sport Medicine and Rehabilitation Beijing Sport University Beijing China Department of Microbiology and Immunology School of Medicine Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel Hill North Carolina USA Department of Graduate School Tianjin Medical University Tianjin China Department of Clinical College of Neurology Neurosurgery and Neurorehabilitation Tianjin Medical Universi

Generative artificial intelligence (AI) holds immense potential for medical applications, but the lack of a comprehensive evaluation framework and methodological deficiencies in existing studies hinder its effective implementation. Standardized assessment guidelines are crucial for ensuring reliable and consistent evaluation of generative AI in healthcare. Our objective is to develop robust, standardized guidelines tailored for evaluating generative AI performance in medical contexts. Through a rigorous literature review utilizing the Web of sciences, Cochrane Library, PubMed, and Google Scholar, we focused on research testing generative AI capabilities in medicine. Our multidisciplinary team of experts conducted discussion sessions to develop a comprehensive 32-item checklist. This checklist encompasses critical evaluation aspects of generative AI in medical applications, addressing key dimensions such as question collection, querying methodologies, and assessment techniques. The checklist and its broader assessment framework provide a holistic evaluation of AI systems, delineating a clear pathway from question gathering to result assessment. It guides researchers through potential challenges and pitfalls, enhancing research quality and reporting and aiding the evolution of generative AI in medicine and life sciences. Our framework furnishes a standardized, systematic approach for testing generative AI's applicability in medicine. For a concise checklist, please refer to Table S or visit *** . This work formulates the standardized testing and assessment guidelines for evaluating generative artificial intelligence (AI) reliability (STAGER) checklist, a 32-item framework offering standardized assessment guidelines tailored for evaluating generative AI systems in medical and life science contexts. It consists of key aspects, including question collection, querying approaches, and assessment techniques. It enhances research quality and facilitates advances in

关键词： generative AI medical and life science contexts reliability standardized assessment guidelines

Overview of the Medium and High Frequency Telescopes of the LiteBIRD satellite mission

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arXiv 2021年

作者： Montier, L. Mot, B. de Bernardis, P. Maffei, B. Pisano, G. Columbro, F. Gudmundsson, J.E. Henrot-Versillé, S. Lamagna, L. Montgomery, J. Prouvé, T. Russell, M. Savini, G. Stever, S. Thompson, K.L. Tsujimoto, M. Tucker, C. Westbrook, B. Ade, P.A.R. Adler, A. Allys, E. Arnold, K. Auguste, D. Aumont, J. Aurlien, R. Austermann, J. Baccigalupi, C. Banday, A.J. Banerji, R. Barreiro, R.B. Basak, S. Beall, J. Beck, D. Beckman, S. Bermejo, J. Bersanelli, M. Bonis, J. Borrill, J. Boulanger, F. Bounissou, S. Brilenkov, M. Brown, M. Bucher, M. Calabrese, E. Campeti, P. Carones, A. Casas, F.J. Challinor, A. Chan, V. Cheung, K. Chinone, Y. Cliche, J.F. Colombo, L. Cubas, J. Cukierman, A. Curtis, D. D’Alessandro, G. Dachlythra, N. de Petris, M. Dickinson, C. Diego-Palazuelos, P. Dobbs, M. Dotani, T. Duband, L. Duff, S. Duval, J.M. Ebisawa, K. Elleflot, T. Eriksen, H.K. Errard, J. Essinger-Hileman, T. Finelli, F. Flauger, R. Franceschet, C. Fuskeland, U. Galloway, M. Ganga, K. Gao, J.R. Genova-Santos, R. Gerbino, M. Gervasi, M. Ghigna, T. Gjerløw, E. Gradziel, M.L. Grain, J. Grupp, F. Gruppuso, A. de Haan, T. Halverson, N.W. Hargrave, P. Hasebe, T. Hasegawa, M. Hattori, M. Hazumi, M. Herman, D. Herranz, D. Hill, C.A. Hilton, G. Hirota, Y. Hivon, E. Hlozek, R.A. Hoshino, Y. de la Hoz, E. Hubmayr, J. Ichiki, K. Iida, T. Imada, H. Ishimura, K. Ishino, H. Jaehnig, G. Kaga, T. Kashima, S. Katayama, N. Kato, A. Kawasaki, T. Keskitalo, R. Kisner, T. Kobayashi, Y. Kogiso, N. Kogut, A. Kohri, K. Komatsu, E. Komatsu, K. Konishi, K. Krachmalnicoff, N. Kreykenbohm, I. Kuo, C.L. Kushino, A. Lanen, J.V. Lattanzi, M. Lee, A.T. Leloup, C. Levrier, F. Linder, E. Louis, T. Luzzi, G. Maciaszek, T. Maino, D. Maki, M. Mandelli, S. Martinez-Gonzalez, E. Masi, S. Matsumura, T. Mennella, A. Migliaccio, M. Minami, Y. Mitsuda, K. Morgante, G. Murata, Y. Murphy, J.A. Nagai, M. Nagano, Y. Nagasaki, T. Nagata, R. Nakamura, S. Namikawa, T. IRAP Université de Toulouse CNRS CNES UPS Toulouse France Dipartimento di Fisica Università La Sapienza INFN Roma P. le A. Moro 2 Roma Italy CNRS UMR 8617 Université Paris-Sud 11 Bâtiment 121 Orsay91405 France Cardiff University School of Physics and Astronomy CardiffCF10 3XQ United Kingdom Stockholm University Sweden Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay91405 France McGill University Physics Department MontrealQCH3A 0G4 Canada Univ. Grenoble Alpes CEA IRIG-DSBT Grenoble38000 France University of California San Diego Department of Physics San DiegoCA92093-0424 United States United Kingdom Okayama University Department of Physics Okayama700-8530 Japan UTIAS University of Tokyo KashiwaChiba277-8583 Japan Menlo ParkCA94025 United States Stanford University Department of Physics CA94305-4060 United States Sagamihara Kanagawa252-5210 Japan University of California Berkeley Department of Physics BerkeleyCA94720 United States Stockholm University Sweden Laboratoire de Physique de l’École Normale Supérieure ENS Université PSL CNRS Sorbonne Université Université de Paris Paris75005 France University of Oslo Institute of Theoretical Astrophysics OsloNO-0315 Norway BoulderCO80305 United States Via Bonomea 265 Trieste34136 Italy Avenida los Castros SN Santander39005 Spain School of Physics Indian Institute of Science Education and Research Thiruvananthapuram Maruthamala PO Vithura ThiruvananthapuramKerala695551 India Plaza Cardenal Cisneros 3 Madrid28040 Spain Dipartimento di Fisica Università degli Studi di Milano INAF-IASF Milano Sezione INFN Milano Italy Computational Cosmology Center BerkeleyCA94720 United States University of California Berkeley Space Science Laboratory BerkeleyCA94720 United States University of Manchester ManchesterM13 9PL United Kingdom - University Paris Diderot CNRS/IN2P3 CEA/Irfu Obs de Paris Sorbonne Paris Cité France Dipartimento di Fisica Univ

LiteBIRD is a JAXA-led Strategic Large-Class mission designed to search for the existence of the primordial gravitational waves produced during the inflationary phase of the Universe, through the measurements of their imprint onto the polarization of the cosmic microwave background (CMB). These measurements, requiring unprecedented sensitivity, will be performed over the full sky, at large angular scales, and over 15 frequency bands from 34 GHz to 448 GHz. The LiteBIRD instruments consist of three telescopes, namely the Low-, Medium- and High-Frequency Telescope (respectively LFT, MFT and HFT). We present in this paper an overview of the design of the Medium-Frequency Telescope (89–224 GHz) and the High-Frequency Telescope (166–448 GHz), the so-called MHFT, under European responsibility, which are two cryogenic refractive telescopes cooled down to 5 K. They include a continuous rotating half-wave plate as the first optical element, two high-density polyethylene (HDPE) lenses and more than three thousand transition-edge sensor (TES) detectors cooled to 100 mK. We provide an overview of the concept design and the remaining specific challenges that we have to face in order to achieve the scientific goals of LiteBIRD. © 2021, CC BY.

关键词： Cosmology