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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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Quantum Simulation for High-Energy Physics

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PRX Quantum 2023年第2期4卷 027001-027001页

作者： Christian W. Bauer Zohreh Davoudi A. Baha Balantekin Tanmoy Bhattacharya Marcela Carena Wibe A. de Jong Patrick Draper Aida El-Khadra Nate Gemelke Masanori Hanada Dmitri Kharzeev Henry Lamm Ying-Ying Li Junyu Liu Mikhail Lukin Yannick Meurice Christopher Monroe Benjamin Nachman Guido Pagano John Preskill Enrico Rinaldi Alessandro Roggero David I. Santiago Martin J. Savage Irfan Siddiqi George Siopsis David Van Zanten Nathan Wiebe Yukari Yamauchi Kübra Yeter-Aydeniz Silvia Zorzetti Physics Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Physics Maryland Center for Fundamental Physics and the NSF Institute for Robust Quantum Simulation University of Maryland College Park Maryland 20742 USA Department of Physics University of Wisconsin Madison Wisconsin 53706 USA T-2 Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Fermi National Accelerator Laboratory Batavia Illinois 60510 USA Enrico Fermi Institute University of Chicago Chicago Illinois 60637 USA Kavli Institute for Cosmological Physics University of Chicago Chicago Illinois 60637 USA Department of Physics University of Chicago Chicago Illinois 60637 USA Department of Physics and Illinois Center for the Advanced Study of the Universe and Illinois Quantum Information Science and Technology Center University of Illinois Urbana Illinois 61801 USA QuEra Computing Inc Boston Massachusetts 02135 USA Department of Mathematics University of Surrey Guildford Surrey GU2 7XH United Kingdom Center for Nuclear Theory Department of Physics and Astronomy Stony Brook University New York 11794-3800 USA Department of Physics Brookhaven National Laboratory Upton New York 11973 USA Peng Huanwu Center for Fundamental Theory Hefei Anhui 230026 China Interdisciplinary Center for Theoretical Study University of Science and Technology of China Hefei Anhui 230026 China Pritzker School of Molecular Engineering Chicago Quantum Exchange and Kadanoff Center for Theoretical Physics University of Chicago Illinois 60637 USA qBraid Co. Harper Court 5235 Chicago Illinois 60615 USA Department of Physics Harvard University Cambridge Massachusetts 02138 USA Department of Physics and Astronomy University of Iowa Iowa City Iowa 52242 USA Duke Quantum Center Duke University Durham North Carolina 27701 USA Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA Department of Physics Duke University

It is for the first time that quantum simulation for high-energy physics (HEP) is studied in the U.S. decadal particle-physics community planning, and in fact until recently, this was not considered a mainstream topic in the community. This fact speaks of a remarkable rate of growth of this subfield over the past few years, stimulated by the impressive advancements in quantum information sciences (QIS) and associated technologies over the past decade, and the significant investment in this area by the government and private sectors in the U.S. and other countries. High-energy physicists have quickly identified problems of importance to our understanding of nature at the most fundamental level, from tiniest distances to cosmological extents, that are intractable with classical computers but may benefit from quantum advantage. They have initiated, and continue to carry out, a vigorous program in theory, algorithm, and hardware co-design for simulations of relevance to the HEP mission. This Roadmap is an attempt to bring this exciting and yet challenging area of research to the spotlight, and to elaborate on what the promises, requirements, challenges, and potential solutions are over the next decade and beyond.

关键词： Quantum simulation

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Lattice QCD and Particle Physics

arXiv

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arXiv 2022年

作者： Kronfeld, Andreas S. Bhattacharya, Tanmoy Blum, Thomas Christ, Norman H. DeTar, Carleton Detmold, William Edwards, Robert Hasenfratz, Anna Lin, Huey-Wen Mukherjee, Swagato Orginos, Konstantinos Brower, Richard Cirigliano, Vincenzo Davoudi, Zohreh Jóo, Bálint Jung, Chulwoo Lehner, Christoph Meinel, Stefan Neil, Ethan T. Petreczky, Peter Richards, David G. Bazavov, Alexei Catterall, Simon Dudek, Jozef J. El-Khadra, Aida X. Engelhardt, Michael Fleming, George T. Giedt, Joel Gupta, Rajan Hansen, Maxwell T. Izubuchi, Taku Karsch, Frithjof Laiho, Jack Liu, Keh-Fei Meyer, Aaron S. Rinaldi, Enrico Savage, Martin Schaich, David Shanahan, Phiala E. Sharpe, Stephen R. Sufian, Raza Syritsyn, Sergey Van de Water, Ruth S. Wagman, Michael L. Weinberg, Evan Witzel, Oliver Aubin, Christopher Boyle, Peter Chandrasekharan, Shailesh Cloët, Ian C. Constantinou, Martha Cushman, Kimmy DeGrand, Thomas Fodor, Zoltan Foreman, Sam Gottlieb, Steven Hoying, Daniel Jang, Yong-Chull Jay, William I. Jin, Xiao-Yong Kelly, Christopher Kuti, Julius Lamm, Henry Lin, Meifeng Lin, Yin Lytle, Andrew T. Mackenzie, Paul Mandula, Jeffrey Meurice, Yannick Monahan, Christopher Morningstar, Colin Osborn, James C. Park, Sungwoo Simone, James N. Strelchenko, Alexei Tomii, Masaaki Vaquero, Alejandro Vranas, Pavlos Wang, Bigeng Wilcox, Walter Yoon, Boram Zhao, Yong Theory Division Fermi National Accelerator Laboratory BataviaIL60510 United States Group T-2 Los Alamos National Laboratory Los AlamosNM87545 United States Department of Physics University of Connecticut StorrsCT06269 United States RIKEN BNL Research Center Brookhaven National Laboratory UptonNY11973 United States Department of Physics Columbia University New YorkNY10027 United States Department of Physics and Astronomy University of Utah Salt Lake CityUT84112 United States Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States The NSF Institute for Artificial Intelligence and Fundamental Interactions Theory Center Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States Department of Physics University of Colorado BoulderCO80309 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Physics Department Brookhaven National Laboratory UptonNY11973 United States Department of Physics College of William & Mary WilliamsburgVA23187 United States Department of Physics Center for Computational Science Boston University BostonMA02215 United States Institute of Nuclear Theory University of Washington SeattleWA98195 United States Department of Physics Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory Oak RidgeTN37831 United States Fakultät für Physik Universität Regensburg RegensburgD-93040 Germany Department of Physics University of Arizona TucsonAZ85721 United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Physics Syracuse University SyracuseNY13244 United States Department of Physics University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Physics New Mexic

Lattice field theory provides a mathematically rigorous definition of quantum field theory, including gauge theories. The rigor provides a platform for computation of strongly-coupled gauge theories, not only quantum chromodynamics (QCD) at long-distances but also strongly-coupled sectors that might lie beyond the Standard Model (BSM) of particle physics. Indeed, the interpretation of many experiments in particle physics, nuclear physics, and astrophysics relies, often crucially, on results from lattice QCD or BSM. In quark-flavor physics, lattice QCD is essential for grounding theoretical predictions. Together with experimental measurements, lattice QCD is used to determine many of the fundamental parameters of the Standard Model's flavor sector: five out of six quark masses and the three mixing angles and CP-violating phase of the Cabibbo-Kobayashi-Maskawa (CKM) matrix. Further Standard-Model predictions requiring lattice QCD are for processes that could reveal signatures of new physics. This area of lattice QCD has become a precision science, as has the determination of the strong coupling, αs, which is key in many areas, for example for understanding Higgs-boson decay to gluons. Lattice-QCD calculations of the hadronic contributions to the anomalous magnetic moment of the muon are another area for which precision is both mandatory and achievable. While these contributions can be estimated via a combination of certain measurements and general theoretical concepts, a direct ab initio calculation from QCD is desirable to confirm robustly the tantalizingly large discrepancy between experiment and the Standard Model. Other indirect searches for new physics are complementary to quark-flavor physics and the muon anomalous magnetic moment. Lattice QCD is an essential tool for understanding newly discovered hadrons with quark content different from the usual baryons and mesons. Lattice-QCD calculations are needed to interpret bounds on electric dipole moments, proton dec

关键词： Calculations

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Forecasting: theory and practice

arXiv

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arXiv 2020年

作者： Petropoulos, Fotios Apiletti, Daniele Assimakopoulos, Vassilios Babai, Mohamed Zied Barrow, Devon K. Taieb, Souhaib Ben Bergmeir, Christoph Bessa, Ricardo J. Bijak, Jakub Boylan, John E. Browell, Jethro Carnevale, Claudio Castle, Jennifer L. Cirillo, Pasquale Clements, Michael P. Cordeiro, Clara Oliveira, Fernando Luiz Cyrino de Baets, Shari Dokumentov, Alexander Ellison, Joanne Fiszeder, Piotr Franses, Philip Hans Frazier, David T. Gilliland, Michael Gönül, M. Sinan Goodwin, Paul Grossi, Luigi Grushka-Cockayne, Yael Guidolin, Mariangela Guidolin, Massimo Gunter, Ulrich Guo, Xiaojia Guseo, Renato Harvey, Nigel Hendry, David F. Hollyman, Ross Januschowski, Tim Jeon, Jooyoung Jose, Victor Richmond R. Kang, Yanfei Koehler, Anne B. Kolassa, Stephan Kourentzes, Nikolaos Leva, Sonia Li, Feng Litsiou, Konstantia Makridakis, Spyros Martin, Gael M. Martinez, Andrew B. Meeran, Sheik Modis, Theodore Nikolopoulos, Konstantinos Önkal, Dilek Paccagnini, Alessia Panagiotelis, Anastasios Panapakidis, Ioannis Pavía, Jose M. Pedio, Manuela Pedregal, Diego J. Pinson, Pierre Ramos, Patrícia Rapach, David E. Reade, J. James Rostami-Tabar, Bahman Rubaszek, Michal Sermpinis, Georgios Shang, Han Lin Spiliotis, Evangelos Syntetos, Aris A. Talagala, Priyanga Dilini Talagala, Thiyanga S. Tashman, Len Thomakos, Dimitrios Thorarinsdottir, Thordis Todini, Ezio Arenas, Juan Ramón Trapero Wang, Xiaoqian Winkler, Robert L. Yusupova, Alisa Ziel, Florian School of Management University of Bath United Kingdom Politecnico di Torino Turin Italy Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece Kedge Business School France Department of Management Birmingham Business School University of Birmingham United Kingdom Belgium Faculty of Information Technology Monash University Melbourne Australia INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Social Statistics and Demography University of Southampton United Kingdom Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Department of Mechanical and Industrial Engineering University of Brescia Italy Magdalen College University of Oxford United Kingdom ZHAW School of Management and Law Zurich University of Applied Sciences Switzerland ICMA Centre Henley Business School University of Reading United Kingdom Faculdade de Ciências e Tecnologia Universidade do Algarve Portugal CEAUL Faculdade de Ciências Universidade de Lisboa Portugal Brazil Department of Business Informatics and Operations Management Faculty of Economics and Business Administration Universiteit Gent Belgium Let’s Forecast Australia Faculty of Economic Sciences and Management Nicolaus Copernicus University in Torun Poland Econometric Institute Erasmus School of Economics Rotterdam Netherlands Department of Econometrics and Business Statistics Monash University Melbourne Australia SAS United States Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Department of Statistical Sciences University of Padua Italy Darden School of Business University of Virginia United States Finance Department Bocconi University Baffi-CAREFIN Centre Milan Italy Department of Tourism and Service Management MODUL Un

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. © 2020, CC BY.

关键词： Forecasting

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11th German Conference on Chemoinformatics (GCC 2015) Abstracts

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JOURNAL OF CHEMINFORMATICS 2016年第1-Sup期8卷 1-27页

作者： [Anonymous] GDCh-CIC Division Associated Board Member Beilstein-Institut zur Förderung der Chemischen Wissenschaften Trakehner Str. 7–9 60487 Frankfurt Germany Division Medicinal Chemistry Amsterdam Institute for Molecules Medicines and Systems (AIMMS) VU University Amsterdam The Netherlands Centre for Bioinformatics Uni Hamburg Bundesstr. 43 20146 Hamburg Germany Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany GlaxoSmithKline Stevenage SG1 2NY UK Discngine Paris 75011 France Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany Bundeskriminalamt Wiesbaden Central Analytics II 65173 Wiesbaden Germany Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA Center for Drug Research Ludwig-Maximilians University Munich Munich Germany Inorganic Chemistry and Center for Nanointegration University of Duisburg-Essen Essen Germany CAM-D Technologies Essen Germany Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany Leiden University Leiden Netherlands Culgi BV Berlin Germany Physikalische Chemie III TU Dortmund 44227 Dortmund Germany Centre for Molecular Informatics Department of Chemistry University of Cambridge Lensfield Road Cambridge CB2 1EW United Kingdom Unilever Centre for Molecular Informatics Department of Chemistry Lensfield Road Cambridge CB2 1EW UK Centre for Molecular Science Informatics Department of Chemistry University of Cambridge Cambridge CB2 1EW UK Department of Chemistry Bar-Ilan University Ramat-Gan 5290002 Israel Department of Pharmaceutical Chemistry University of Marburg Marburg Germany Pharmaceutical and Medicinal Chemistry Institute of Pharmacy Freie Universität Berlin Königin-Luise Straße 2+4 14195 Berlin Germany Computer-Aided Drug Design Institute of Pharmacy Freie Universität Berlin 14195 Berlin Germany Center for Bioinformatics University of Hamburg 20146 Hamburg Germany Department of P

Information about several papers discussed at the 11th German Conference on Chemoinformatics (GCC) held November 8-10, 2015 sponsored by the Chemistry-Information-Computer (CIC) division of the German Chemical Society is presented. Topics include accurate description of protein-ligand binding and its functional activity, combination of chemoinformatics and bioinformatics using three dimensional structure of amino acids, and Human Genome Project leading to advancement in drug discovery.

关键词： CHEMINFORMATICS PREDICTION models MONTE Carlo method

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Author Correction: The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

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Nature computational science 2023年第7期3卷 658页

作者： Gengjie Jia Yu Li Xue Zhong Kanix Wang Milton Pividori Rabab Alomairy Aniello Esposito Hatem Ltaief Chikashi Terao Masato Akiyama Koichi Matsuda David E Keyes Hae Kyung Im Takashi Gojobori Yoichiro Kamatani Michiaki Kubo Nancy J Cox James Evans Xin Gao Andrey Rzhetsky Shenzhen Branch Guangdong Laboratory of Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China. jiagengjie@***. Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia. Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong People's Republic of China. Department of Medicine and Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN US. Department of Medicine Institute of Genomics and Systems Biology Committee on Genomics Genetics and Systems Biology University of Chicago Chicago IL US. Department of Operations Business Analytics and Information Systems University of Cincinnati Cincinnati OH US. Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA US. Extreme Computing Research Center Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia. HPE HPC/AI EMEA Research Laboratory Bristol UK. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Clinical Research Center Shizuoka General Hospital Shizuoka Japan. Department of Applied Genetics The School of Pharmaceutical Sciences University of Shizuoka Shizuoka Japan. Department of Ophthalmology Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan. Biological and Environmenta

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Corrigendum to "Synergetic effect of bismuth vanadate over copolymerized carbon nitride composites for highly efficient photocatalytic H2 and O2 generation" [J. Colloid Interface Sci. 627 (2022) 621-629]

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Journal of colloid and interface science 2022年第PT A期628卷 366-367页

作者： Asif Hayat Muhammad Sohail T A Taha Sunil Kumar Baburao Mane Abdullah G Al-Sehemi Ahmed A Al-Ghamdi W I Nawawi Arkom Palamanit Mohammed A Amin Ahmed M Fallatah Zeeshan Ajmal Hamid Ali Wasim Ullah Khan Muhammad Wajid Shah Javid Khan S Wageh State Key Laboratory of Photocatalysis on Energy and Environment College of Chemistry Fuzhou University Fuzhou 350116 China. Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of China Huzhou 313001 China. Physics Department College of Science Jouf University P.O. Box: 2014 Sakaka Saudi Arabia Physics and Engineering Mathematics Department Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt. Department of Chemistry Khaja Bandanawaz University Kalaburagi Karnataka 585104 India. Research Center for Advanced Materials Science (RCAMS) King Khalid University P.O. Box 9004 Abha 61413 Saudi Arabia Department of Chemistry College of Science King Khalid University P.O. Box 9004 Abha 61413 Saudi Arabia. Department of Physics Faculty of Science King Abdulaziz University Jeddah 21589 Saudi Arabia. Faculty of Applied Sciences Universiti Teknologi MARA Cawangan Perlis Arau Perlis 02600 Malaysia. Energy Technology Program Department of Specialized Engineering Faculty of Engineering Prince of Songkla University 15 Karnjanavanich Rd. Hat Yai Songkhla 90110 Thailand. Department of Chemistry College of Science Taif University P.O. Box 11099 Taif 21944 Saudi Arabia. School of Chemistry and Chemical Engineering Northwestern Polytechnical University Xian 710072 China. Multiscale Computational Materials Facility Key Laboratory of Eco-Materials Advanced Technology College of Materials Science and Engineering Fuzhou University Fuzhou 350100 China. State Key Laboratory of Optoelectronic Materials and Technologies School of Materials Science and Engineering Sun Yat-sen University Guangzhou 510275 China. Electronic address: wasimullah89@***. School Of Chemistry Sun Yat-sen University Guangzhou 510275 China. Electronic address: m.wajidshah@***. College of Materials Science and Engineering Hunan University Changsha Hunan 410082 China. Electronic address: javidchemist@yahoo.com.

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The constraining power of the Marked Power Spectrum: an analytical study

arXiv

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arXiv 2024年

作者： Marinucci, Marco Jung, Gabriel Liguori, Michele Ravenni, Andrea Spezzati, Francesco Andrews, Adam Baldi, Marco Coulton, William R. Karagiannis, Dionysios Villaescusa-Navarro, Francisco Wandelt, Benjamin D. Dipartimento di Fisica e Astronomia "G. Galilei" Universit‘a degli Studi di Padova via Marzolo 8 PadovaI-35131 Italy INFN Sezione di Padova via Marzolo 8 PadovaI-35131 Italy Université Paris-Saclay CNRS Institut d’Astrophysique Spatiale Orsay91405 France INAF OAS Bologna via Piero Gobetti 101 BolognaI-40129 Italy INFN Sezione di Bologna via Irnerio 46 BolognaI-40126 Italy Dipartimento di Fisica e Astronomia Alma Mater Studiorum University of Bologna Via Piero Gobetti 93/2 BO Bologna40129 Italy Kavli Institute for Cosmology Cambridge Madingley Road CambridgeCB3 0HA United Kingdom DAMTP Centre for Mathematical Sciences University of Cambridge Wilberforce Road CambridgeCB3 OWA United Kingdom Dipartimento di Fisica e Scienze della Terra Università degli Studi di Ferrara via Giuseppe Saragat 1 Ferrara44122 Italy INFN Sezione di Ferrara via Giuseppe Saragat 1 Ferrara44122 Italy Department of Physics & Astronomy University of the Western Cape Cape Town7535 South Africa Center for Computational Astrophysics 160 5th Avenue New YorkNY10010 United States Department of Physics and Astronomy Johns Hopkins University 3400 North Charles Street BaltimoreMD21218 United States Department of Applied Mathematics and Statistics Johns Hopkins University 3400 North Charles Street BaltimoreMD21218 United States UMR 7095 98 bis bd Arago ParisF-75014 France

The marked power spectrum - a two-point correlation function of a transformed density field - has emerged as a promising tool for extracting cosmological information from the large-scale structure of the Universe. In this work, we present the first comprehensive analytical study of the marked power spectrum’s sensitivity to primordial non-Gaussianity (PNG) of the non-local type. We extend previous effective field theory frameworks to incorporate PNG, developing a complete theoretical model that we validate against the Quijote simulation suite. Through a systematic Fisher analysis, we compare the constraining power of the marked power spectrum against traditional approaches combining the power spectrum and bispectrum (P+B). We explore different choices of mark parameters to evaluate their impact on parameter constraints, particularly focusing on equilateral and orthogonal PNG as well as neutrino masses. Our analysis shows that while marking up underdense regions yields optimal constraints in the low shot-noise regime, the marked power spectrum’s performance for discrete tracers with BOSS-like number densities does not surpass that of P+B analysis at mildly non-linear scales (k 0.25 h/Mpc). However, the marked approach offers several practical advantages, including simpler estimation procedures and potentially more manageable systematic effects. Our theoretical framework reveals how the marked power spectrum incorporates higher-order correlation information through terms resembling tree-level bispectra and power spectrum convolutions. This work establishes a robust foundation for applying marked statistics to future large-volume surveys. © 2024, CC BY.

关键词： Shot noise

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Search for Continuous Gravitational Waves from Known Pulsars in the First Part of the Fourth LIGO-Virgo-KAGRA Observing Run

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Astrophysical Journal 2025年第2期983卷 99-99页

作者： Abac, A.G. Abbott, R. Abouelfettouh, I. Acernese, F. Ackley, K. Adhicary, S. Adhikari, N. Adhikari, R.X. Adkins, V.K. Agarwal, D. Agathos, M. Aghaei Abchouyeh, M. Aguiar, O.D. Aguilar, I. Aiello, L. Ain, A. Ajith, P. Akutsu, T. Albanesi, S. Alfaidi, R.A. Al-Jodah, A. Alléné, C. Allocca, A. Al-Shammari, S. Altin, P.A. Alvarez-Lopez, S. Amato, A. Amez-Droz, L. Amorosi, A. Amra, C. Ananyeva, A. Anderson, S.B. Anderson, W.G. Andia, M. Ando, M. Andrade, T. Andres, N. Andrés-Carcasona, M. Andrić, T. Anglin, J. Ansoldi, S. Antelis, J.M. Antier, S. Aoumi, M. Appavuravther, E.Z. Appert, S. Apple, S.K. Arai, K. Araya, A. Araya, M.C. Areeda, J.S. Argianas, L. Aritomi, N. Armato, F. Arnaud, N. Arogeti, M. Aronson, S.M. Ashton, G. Aso, Y. Assiduo, M. Assis de Souza Melo, S. Aston, S.M. Astone, P. Attadio, F. Aubin, F. AultONeal, K. Avallone, G. Babak, S. Badaracco, F. Badger, C. Bae, S. Bagnasco, S. Bagui, E. Baier, J.G. Baiotti, L. Bajpai, R. Baka, T. Ball, M. Ballardin, G. Ballmer, S.W. Banagiri, S. Banerjee, B. Bankar, D. Baral, P. Barayoga, J.C. Barish, B.C. Barker, D. Barneo, P. Barone, F. Barr, B. Barsotti, L. Barsuglia, M. Barta, D. Bartoletti, A.M. Barton, M.A. Bartos, I. Basak, S. Basalaev, A. Bassiri, R. Basti, A. Bates, D.E. Bawaj, M. Baxi, P. Bayley, J.C. Baylor, A.C. Baynard, P.A. Bazzan, M. Bedakihale, V.M. Beirnaert, F. Bejger, M. Belardinelli, D. Bell, A.S. Benedetto, V. Benoit, W. Bentley, J.D. Ben Yaala, M. Bera, S. Berbel, M. Bergamin, F. Berger, B.K. Bernuzzi, S. Beroiz, M. Bersanetti, D. Bertolini, A. Betzwieser, J. Beveridge, D. Bevins, N. Bhandare, R. Bhardwaj, U. Bhatt, R. Bhattacharjee, D. Bhaumik, S. Bhowmick, S. Bianchi, A. Bilenko, I.A. Billingsley, G. Binetti, A. Bini, S. Birnholtz, O. Biscoveanu, S. Bisht, A. Bitossi, M. Bizouard, M.-A. Blackburn, J.K. Blagg, L.A. Blair, C.D. Blair, D.G. Bobba, F. Bode, N. Boileau, G. Boldrini, M. Bolingbroke, G.N. Bolliand, A. Bonavena, L.D. Bondarescu, R. Bondu, F. Bonilla, E. Bonilla, M.S. Bonino, A. Bonnand, R. Booker, P. Borchers, A. Boschi, V. Bose, S. Boss Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Potsdam D-14476 Germany LIGO Laboratory California Institute of Technology Pasadena 91125 CA United States LIGO Hanford Observatory Richland 99352 WA United States Dipartimento di Farmacia Università di Salerno Fisciano Salerno I-84084 Italy INFN Sezione di Napoli Napoli I-80126 Italy University of Warwick Coventry CV 47AL United Kingdom The Pennsylvania State University University Park 16802 PA United States University of Wisconsin-Milwaukee Milwaukee 53201 WI United States Louisiana State University Baton Rouge 70803 LA United States Université Catholique de Louvain Louvain-la-Neuve B-1348 Belgium Inter-University Centre for Astronomy and Astrophysics Pune 411007 India Queen Mary University of London London E1 4NS United Kingdom Department of Physics and Astronomy Sejong University 209 Neungdong-ro Gwangjin-gu Seoul 143-747 South Korea Instituto Nacional de Pesquisas Espaciais São José dos Campos São Paulo 12227-010 Brazil Stanford University Stanford 94305 CA United States Università di Roma Tor Vergata Roma I-00133 Italy INFN Sezione di Roma Tor Vergata Roma I-00133 Italy Cardiff University Cardiff CF24 3AA United Kingdom Universiteit Antwerpen Antwerpen 2000 Belgium International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan INFN Sezione di Torino Torino I-10125 Italy Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena Jena D-07743 Germany Dipartimento di Fisica Università degli Studi di Torino Torino I-10125 Italy SUPA University of Glasgow Glasgow G12 8QQ United Kingdom OzGrav University of Western Australia Crawley 6009 WA

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of general relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO-Virgo-KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering single-harmonic and dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is 6.4 × 10^-27 for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is 8.8 × 10^-9 for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets, we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of nonstandard polarizations as predicted by the Brans-Dicke theory. © 2025. The Author(s). Published by the American Astronomical Society.

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GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

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Nature genetics 2023年第9期55卷 1471-1482页

作者： Remi Stevelink Dorothee Kasteleijn-Nolst Trenite Bobby P. C. Koeleman Dick Lindhout Anja C. M. Sonsma Ciarán Campbell Gianpiero L. Cavalleri Norman Delanty Hany El-Naggar Austin Lacey Peter Widdess-Walsh Colin P. Doherty Siwei Chen Claire Churchhouse Mark J. Daly Yen-Chen A. Feng Daniel P. Howrigan Mitja I. Kurki Benjamin M. Neale Aarno Palotie Wei Zhou Namrata Gupta Henrike O. Heyne Bassel Abou-Khalil Kevin F. Haas R. S. Taneja Oluyomi M. Adesoji Michael Nothnagel Peter Nürnberg Thomas Sander Zaid Afawi Ilan Blatt Amos D. Korczyn Elisabetta Amadori Ganna Balagura Antonella Riva Marcello Scala Paolo Scudieri Pasquale Striano Federico Zara Michele Iacomino Francesca Madia Alison Anderson Patrick Kwan Terence J. O’Brien Slavé Petrovski K. Meng Tan Marian Todaro Joseph Anderson Danielle M. Andrade Quratulain Zulfiqar Ali Grazia Annesi Pauls Auce Andreja Avbersek Simona Balestrini Vera Braatz Claudia B. Catarino Krishna Chinthapalli Jennifer Jamnadas-Khoda Dalia Kasperavičiūte Costin Leu Josemir W. Sander Sanjay M. Sisodiya Sara Zagaglia Melanie Bahlo Karen L. Oliver Mark D. Baker Beata Fonferko-Shadrach William O. Pickrell Carmen Barba Claudia Bianchini Viola Doccini Renzo Guerrini Davide Mei Martino Montomoli Elena Parrini Annalisa Vetro Karen Barboza Fabrice Bartolomei Thomas Bast Bernhard J. Steinhoff Larry Baum Stacey S. Cherny Pak Sham Tobias Baumgartner Christian E. Elger Kerstin Hallmann Wolfram S. Kunz Rainer Surges Randi von Wrede Gábor Zsurka Betül Baykan Nerses Bebek Yeşim Kesim Albert J. Becker Susanne Schoch Felicitas Becker Caitlin A. Bennett Samuel F. Berkovic Stephanie L. Leech Brigid M. Regan Ingrid E. Scheffer Bianca Berghuis Ahmad Beydoun Wassim Nasreddine Francesca Bisulli Lidia Di Vito Lorenzo Ferri Tania Giangregorio Laura Licchetta Raffaella Minardi Barbara Mostacci Carlotta Stipa Paolo Tinuper Lorenzo Muccioli Federica Pondrelli Dheeraj R. Bobbili Emadeldin Hassanin Roland Krause Zied Landoulsi Patrick May Ingo Borggraefe Christian Bosselmann Christian Hengsbach Josua Kegele Stephen Lauxmann Department of Genetics University Medical Center Utrecht Utrecht The Netherlands Stichting Epilepsie Instellingen Nederland (SEIN) Heemstede The Netherlands School of Pharmacy and Biomolecular Sciences The Royal College of Surgeons in Ireland Dublin Ireland The FutureNeuro Research Centre Dublin Ireland Department of Neurology Beaumont Hospital Dublin Ireland Department of Neurology St. James’s Hospital Dublin Ireland Analytic and Translational Genetics Unit Department of Medicine Massachusetts General Hospital and Harvard Medical School Boston MA USA Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of Harvard and M.I.T Cambridge MA USA Institute for Molecular Medicine Finland (FIMM) University of Helsinki Helsinki Finland Psychiatric & Neurodevelopmental Genetics Unit Department of Psychiatry Massachusetts General Hospital and Harvard Medical School Boston MA USA Division of Biostatistics Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan Hasso Plattner Institute Digital Health Center University of Potsdam Potsdam Germany Hasso Plattner Institute Mount Sinai School of Medicine New York City NY USA Department of Neurology Vanderbilt University Medical Center Nashville TN USA Cologne Center for Genomics (CCG) University of Cologne Faculty of Medicine and University Hospital Cologne Cologne Germany University Hospital Cologne Cologne Germany Department of Neurology Charité Universitaetsmedizin Berlin Campus Virchow-Clinic Berlin Germany Tel-Aviv University Sackler Faculty of Medicine Ramat Aviv Israel Department of Neurology Sheba Medical Center Ramat Gan Israel Department of Neurosciences Rehabilitation Ophthalmology Genetics Maternal and Child Health University of Genova Genova Italy IRCCS Istituto Giannina Gaslini Genova Italy Department of Medicine University of Melbour

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment.

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