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arXiv 2024年

作者： Agazie, Gabriella Anumarlapudi, Akash Archibald, Anne M. Arzoumanian, Zaven Baier, Jeremy George Baker, Paul T. Bécsy, Bence Blecha, Laura Brazier, Adam Brook, Paul R. Brown, Lucas Burke-Spolaor, Sarah Casey-Clyde, J. Andrew Charisi, Maria Chatterjee, Shami Cohen, Tyler Cordes, James M. Cornish, Neil J. Crawford, Fronefield Cromartie, H. Thankful Crowter, Kathryn DeCesar, Megan E. Demorest, Paul B. Deng, Heling Dolch, Timothy Ferrara, Elizabeth C. Fiore, William Fonseca, Emmanuel Freedman, Gabriel E. Garver-Daniels, Nate Gentile, Peter A. Glaser, Joseph Good, Deborah C. Gültekin, Kayhan Hazboun, Jeffrey S. Jennings, Ross J. Johnson, Aaron D. Jones, Megan L. Kaiser, Andrew R. Kaplan, David L. Kelley, Luke Zoltan Kerr, Matthew Key, Joey S. Laal, Nima Lam, Michael T. Lamb, William G. Larsen, Bjorn Lazio, T.W. Joseph Lewandowska, Natalia Liu, Tingting Lorimer, Duncan R. Luo, Jing Lynch, Ryan S. Ma, Chung-Pei Madison, Dustin R. McEwen, Alexander McKee, James W. McLaughlin, Maura A. McMann, Natasha Meyers, Bradley W. Meyers, Patrick M. Mingarelli, Chiara M.F. Mitridate, Andrea Natarajan, Priyamvada Ng, Cherry Nice, David J. Ocker, Stella Koch Olum, Ken D. Pennucci, Timothy T. Perera, Benetge B.P. Pol, Nihan S. Radovan, Henri A. Ransom, Scott M. Ray, Paul S. Romano, Joseph D. Runnoe, Jessie C. Sardesai, Shashwat C. Schmiedekamp, Ann Schmiedekamp, Carl Schmitz, Kai Shapiro-Albert, Brent J. Siemens, Xavier Simon, Joseph Siwek, Magdalena S. Fiscella, Sophia Sosa V. Stairs, Ingrid H. Stinebring, Daniel R. Stovall, Kevin Susobhanan, Abhimanyu Swiggum, Joseph K. Taylor, Stephen R. Turner, Jacob E. Unal, Caner Vallisneri, Michele Vigeland, Sarah J. Wahl, Haley M. Willson, London Witt, Caitlin A. Wright, David Young, Olivia Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Newcastle University NE1 7RU United Kingdom X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Department of Physics Oregon State University CorvallisOR97331 United States Department of Physics and Astronomy Widener University One University Place ChesterPA19013 United States Physics Department University of Florida GainesvilleFL32611 United States Cornell Center for Astrophysics and Planetary Science Department of Astronomy Cornell University IthacaNY14853 United States Cornell Center for Advanced Computing Cornell University IthacaNY14853 United States Institute for Gravitational Wave Astronomy School of Physics and Astronomy University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Institute of Cosmology Department of Physics and Astronomy Tufts University MedfordMA02155 United States Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Department of Physics University of Connecticut 196 Auditorium Road U-3046 StorrsCT06269-3046 United States Department of Physics and Astronomy Vanderbilt University 2301 Vanderbilt Place NashvilleTN37235 United States Department of Physics New Mexico Institute of Mining and Technology 801 Leroy Place SocorroNM87801 United States Department of Physics Montana State University BozemanMT59717 United States Department of Physics and Astronomy Franklin & Marshall College P.O. Box 3003 LancasterPA17604 United States National Research Council Research Associate National Academy of Sciences WashingtonDC20001 United States Naval Research Laboratory WashingtonDC20375 United States Departme

The cosmic merger history of supermassive black hole binaries (SMBHBs) is expected to produce a low-frequency gravitational wave background (GWB). Here we investigate how signs of the discrete nature of this GWB can manifest in pulsar timing arrays through excursions from, and breaks in, the expected (formula presented) power-law of the GWB strain spectrum. To do this, we create a semi-analytic SMBHB population model, fit to NANOGrav’s 15 yr GWB amplitude, and with 1,000 realizations we study the populations’ characteristic strain and residual spectra. Comparing our models to the NANOGrav 15 yr spectrum, we find two interesting excursions from the power-law. The first, at 2 nHz, is below our GWB realizations with p-value significance p = 0.05 to 0.06 (≈ 1.8σ−1.9σ). The second, at 16 nHz, is above our GWB realizations with p = 0.04 to 0.15 (≈ 1.4σ−2.1σ). We explore the properties of a loud SMBHB which could cause such an excursion. Our simulations also show that the expected number of SMBHBs decreases by three orders of magnitude, from ∼ 106 to ∼ 103, between 2 nHz and 20 nHz. This causes a break in the strain spectrum as the stochasticity of the background breaks down at (formula presented), consistent with predictions pre-dating GWB measurements. The diminished GWB signal from SMBHBs at frequencies above the 26 nHz break opens a window for PTAs to detect continuous GWs from individual SMBHBs or GWs from the early universe. © 2024, CC BY.

关键词： Stochastic systems

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Bridging HPC Communities through the Julia Programming Language

arXiv

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arXiv 2022年

作者： Churavy, Valentin Godoy, William F. Bauer, Carsten Ranocha, Hendrik Schlottke-Lakemper, Michael Räss, Ludovic Blaschke, Johannes Giordano, Mosè Schnetter, Erik Omlin, Samuel Vetter, Jeffrey S. Edelman, Alan Massachussetts Institute of Technology United States Oak Ridge National Laboratory United States Paderborn Center for Parallel Computing Paderborn University Germany Department of Mathematics University of Hamburg Germany Applied and Computational Mathematics RWTH Aachen University Germany University of Stuttgart Germany ETH Zurich Switzerland Birmensdorf Switzerland National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Centre for Advanced Research Computing University College London Gower Street LondonWC1E 6BT United Kingdom Perimeter Institute 31 Caroline St. N. WaterlooONN2L 2Y5 Canada Department of Physics and Astronomy University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada Center for Computation & Technology Louisiana State University Baton RougeLA70803 United States ETH Zurich Switzerland

The Julia programming language has evolved into a modern alternative to fill existing gaps in scientific computing and data science applications. Julia leverages a unified and coordinated single-language and ecosystem paradigm and has a proven track record of achieving high performance without sacrificing user productivity. These aspects make Julia a viable alternative to high-performance computing's (HPC's) existing and increasingly costly many-body workflow composition strategy in which traditional HPC languages (e.g., Fortran, C, C++) are used for simulations, and higher-level languages (e.g., Python, R, MATLAB) are used for data analysis and interactive computing. Julia's rapid growth in language capabilities, package ecosystem, and community make it a promising universal language for HPC. This paper presents the views of a multidisciplinary group of researchers from academia, government, and industry that advocate for an HPC software development paradigm that emphasizes developer productivity, workflow portability, and low barriers for entry. We believe that the Julia programming language, its ecosystem, and its community provide modern and powerful capabilities that enable this group's objectives. Crucially, we believe that Julia can provide a feasible and less costly approach to programming scientific applications and workflows that target HPC facilities. In this work, we examine the current practice and role of Julia as a common, end-to-end programming model to address major challenges in scientific reproducibility, data-driven AI/machine learning, co-design and workflows, scalability and performance portability in heterogeneous computing, network communication, data management, and community education. As a result, the diversification of current investments to fulfill the needs of the upcoming decade is crucial as more supercomputing centers prepare for the exascale era. © 2022, CC BY.

关键词： Productivity

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Constraining ultralight vector dark matter with the Parkes Pulsar Timing Array second data release

引用

Physical Review D 2022年第8期106卷 L081101-L081101页

作者： Yu-Mei Wu Zu-Cheng Chen Qing-Guo Huang Xingjiang Zhu N. D. Ramesh Bhat Yi Feng George Hobbs Richard N. Manchester Christopher J. Russell R. M. Shannon School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou 310024 China School of Physical Sciences University of Chinese Academy of Sciences No. 19A Yuquan Road Beijing 100049 China CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China Department of Astronomy Beijing Normal University Beijing 100875 China Advanced Institute of Natural Sciences Beijing Normal University Zhuhai 519087 China International Centre for Radio Astronomy Research Curtin University Bentley Western Australia 6102 Australia Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou 311100 China Australia Telescope National Facility CSIRO Astronomy and Space Science P.O. Box 76 Epping New South Wales 1710 Australia CSIRO Scientific Computing Australian Technology Park Locked Bag 9013 Alexandria New South Wales 1435 Australia Centre for Astrophysics and Supercomputing Swinburne University of Technology Hawthorn Victoria 3122 Australia Australian Research Council Centre of Excellence in Gravitational Wave Discovery (OzGrav)

Composed of ultralight bosons, fuzzy dark matter provides an intriguing solution to challenges that the standard cold dark matter model encounters on subgalactic scales. The ultralight dark matter with mass m∼10−23 eV will induce a periodic oscillation in gravitational potentials with a frequency in the nanohertz band, leading to observable effects in the arrival times of radio pulses from pulsars. Unlike scalar dark matter, pulsar timing signals induced by the vector dark matter are dependent on the oscillation direction of the vector fields. In this work, we search for ultralight vector dark matter in the mass range of [2×10−24,2×10−22] eV through its gravitational effect in the Parkes Pulsar Timing Array (PPTA) second data release. Since no statistically significant detection is made, we place 95% upper limits on the local dark matter density as ρVF≲5 GeV/cm3 for m≲10−23 eV. As no preferred direction is found for the vector dark matter, these constraints are comparable to those given by the scalar dark matter search with an earlier 12-year dataset of PPTA.

关键词： Dark matter Gravitational waves

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Performance of the low-latency GstLAL inspiral search towards LIGO, Virgo, and KAGRA’s fourth observing run

arXiv

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arXiv 2023年

作者： Ewing, Becca Huxford, Rachael Singh, Divya Tsukada, Leo Hanna, Chad Huang, Yun-Jing Joshi, Prathamesh Li, Alvin K.Y. Magee, Ryan Messick, Cody Pace, Alex Ray, Anarya Sachdev, Surabhi Sakon, Shio Tapia, Ron Adhicary, Shomik Baral, Pratyusava Baylor, Amanda Cannon, Kipp Caudill, Sarah Chaudhary, Sushant Sharma Coughlin, Michael W. Cousins, Bryce Creighton, Jolien D.E. Essick, Reed Fong, Heather George, Richard N. Godwin, Patrick Harada, Reiko Kennington, James Kuwahara, Soichiro Meacher, Duncan Morisaki, Soichiro Mukherjee, Debnandini Niu, Wanting Posnansky, Cort Toivonen, Andrew Tsutsui, Takuya Ueno, Koh Viets, Aaron Wade, Leslie Wade, Madeline Waratkar, Gaurav Department of Physics The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Astronomy and Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States LIGO Laboratory California Institute of Technology PasadenaCA91125 United States MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Leonard E. Parker Center for Gravitation Cosmology and Astrophysics University of Wisconsin-Milwaukee MilwaukeeWI53201 United States School of Physics Georgia Institute of Technology AtlantaGA30332 United States RESCEU The University of Tokyo Tokyo113-0033 Japan Department of Physics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Data Science Research University of Massachusetts DartmouthMA02747 United States Institute of Multi-messenger Astrophysics and Cosmology Missouri University of Science and Technology Physics Building 1315 N. Pine St. RollaMO65409 United States School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Department of Physics University of Illinois UrbanaIL61801 United States Canadian Institute for Theoretical Astrophysics 60 St George St TorontoONM5S 3H8 Canada Graduate School of Science The University of Tokyo Tokyo113-0033 Japan Center for Gravitational Physics University of Texas at Austin AustinTX78712 United States LIGO Laboratory California Institute of Technology MS 100-36 PasadenaCA91125 United States Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan NASA Marshall Space Flight Center HuntsvilleAL35811 United States Center f

GstLAL is a stream-based matched-filtering search pipeline aiming at the prompt discovery of gravitational waves from compact binary coalescences such as the mergers of black holes and neutron stars. Over the past three observation runs by the LIGO, Virgo, and KAGRA (LVK) collaboration, the GstLAL search pipeline has participated in several tens of gravitational wave discoveries. The fourth observing run (O4) is set to begin in May 2023 and is expected to see the discovery of many new and interesting gravitational wave signals which will inform our understanding of astrophysics and cosmology. We describe the current configuration of the GstLAL low-latency search and show its readiness for the upcoming observation run by presenting its performance on a mock data challenge. The mock data challenge includes 40 days of LIGO Hanford, LIGO Livingston, and Virgo strain data along with an injection campaign in order to fully characterize the performance of the search. We find an improved performance in terms of detection rate and significance estimation as compared to that observed in the O3 online analysis. The improvements are attributed to several incremental advances in the likelihood ratio ranking statistic computation and the method of background estimation. © 2023, CC BY.

关键词： Gravity waves

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Bayesian inference for gravitational waves from binary neutron star mergers in third-generation observatories

arXiv

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arXiv 2021年

作者： Smith, Rory Borhanian, Ssohrab Sathyaprakash, Bangalore Vivanco, FranciscoHernandez Field, Scott E. Lasky, Paul Mandel, Ilya Morisaki, Soichiro Ottaway, David Slagmolen, Bram Thrane, Eric Töyrä, Daniel Vitale, Salvatore School of Physics and Astronomy Monash University VIC3800 Australia OzGrav The ARC Centre of Excellence for Gravitational Wave Discovery ClaytonVIC3800 Australia Institute for Gravitation and the Cosmos Department of Physics Pennsylvania State University University ParkPA16802 United States Department of Astronomy and Astrophysics Penn State University University ParkPA16802 United States School of Physics and Astronomy Cardiff University CardiffCF24 3AA United Kingdom Department of Mathematics Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States Department of Physics University of Wisconsin-Milwaukee MilwaukeeWI53201 United States OzGrav University of Adelaide AdelaideSA5005 Australia OzGrav ANU Centre for Gravitational Astrophysics Research Schools of Physics and Astronomy and Astrophysics The Australian National University ACT2601 Australia LIGO Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States

Third-generation (3G) gravitational-wave detectors will observe thousands of coalescing neutron star binaries with unprecedented fidelity. Extracting the highest precision science from these signals is expected to be challenging owing to both high signal-to-noise ratios and long-duration signals. We demonstrate that current Bayesian inference paradigms can be extended to the analysis of binary neutron star signals without breaking the computational bank. We construct reduced order models for ∼ 90 minute long gravitational-wave signals, covering the observing band (5−2048 Hz), speeding up inference by a factor of ∼ 1.3 × 104 compared to the calculation times without reduced order models. The reduced order models incorporate key physics including the effects of tidal deformability, amplitude modulation due to the Earth's rotation, and spin-induced orbital precession. We show how reduced order modeling can accelerate inference on data containing multiple, overlapping gravitational-wave signals, and determine the speedup as a function of the number of overlapping signals. Thus, we conclude that Bayesian inference is computationally tractable for the long-lived, overlapping, high signal-to-noise-ratio events present in 3G observatories. Copyright © 2021, The Authors. All rights reserved.

关键词： Gravitational effects

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LET'S PLAY POLO: DIRECT OPTIMIZATION OF THE PROBABILITY OF LESION ORIGIN IN PROTON TREATMENT PLANNING FOR LOW-GRADE GLIOMA PATIENTS

International Journal of Particle Therapy

引用

International Journal of Particle Therapy 2024年 12卷

作者： Tim Ortkamp Habiba Sallem Semi Harrabi Martin Frank Oliver Jäkel Julia Bauer Niklas Wahl Scientific Computing Center (scc) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Helmholtz Information and Data Science School for Health (HIDSS4Health) Karlsruhe Germany Medical Physics In Radiation Oncology (e040) German Cancer Research Center (DKFZ) Heidelberg Germany Department Of Radiation Oncology University Hospital Heidelberg Heidelberg Germany Heidelberg Institute For Radiation Oncology (hiro) National Center for Radiation Research in Oncology (NCRO) Heidelberg Germany Heidelberg Ion Beam Therapy Center (HIT) Heidelberg Germany

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Improved Fitness Dependent Optimizer for Solving Economic Load Dispatch Problem

arXiv

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arXiv 2022年

作者： Tahir, Barzan Hussein Rashid, Tarik A. Rauf, Hafiz Tayyab Bacanin, Nebojsa Chhabra, Amit Vimal, S. Yaseen, Zaher Mundher Department of Computer Science and Engineering University of Kurdistan Helwer Erbil Iraq Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Singidunum University Danijelova 32 Belgrade11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia USQ's Advanced Data Analytics Research Group School of Mathematics Physics and Computing University of Southern Queensland QLD4350 Australia New era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq

Economic Load Dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to Economic Load Dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness Dependent Optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness Dependent Optimizer (FDO) examines the search spaces based on the searching approach of Particle Swarm Optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have carried out Fitness Dependent Optimizer to solve the Economic Load Dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of Fitness Dependent Optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for Fitness Dependent Optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multi-dimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. Empirical results obtained using the enhanced variant of the Fitness Dependent Optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional Fitness Dependent Optimizer. The experimental study obtained 7.94E-12, the lowest transmission loss using the enhanced Fitness Dependent Optimizer. Correspondingly, various standard estimations are used to prove the stability

关键词： Decision making

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Template bank for compact binary mergers in the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA

arXiv

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arXiv 2022年

作者： Sakon, Shio Tsukada, Leo Fong, Heather Kennington, James Niu, Wanting Hanna, Chad Adhicary, Shomik Baral, Pratyusava Baylor, Amanda Cannon, Kipp Caudill, Sarah Cousins, Bryce Creighton, Jolien D.E. Ewing, Becca George, Richard N. Godwin, Patrick Harada, Reiko Huang, Yun-Jing Huxford, Rachael Joshi, Prathamesh Kuwahara, Soichiro Li, Alvin K.Y. Magee, Ryan Meacher, Duncan Messick, Cody Morisaki, Soichiro Mukherjee, Debnandini Pace, Alex Posnansky, Cort Ray, Anarya Sachdev, Surabhi Singh, Divya Tapia, Ron Tsutsui, Takuya Ueno, Koh Viets, Aaron Wade, Leslie Wade, Madeline Wang, Jonathan Department of Physics The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Physics and Astronomy University of British Columbia VancouverBCV6T 1Z4 Canada RESCEU The University of Tokyo Tokyo113-0033 Japan Graduate School of Science The University of Tokyo Tokyo113-0033 Japan Department of Astronomy and Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Leonard E. Parker Center for Gravitation Cosmology and Astrophysics University of Wisconsin-Milwaukee MilwaukeeWI53201 United States Department of Physics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Data Science Research University of Massachusetts DartmouthMA02747 United States Department of Physics University of Illinois UrbanaIL61801 United States Center for Gravitational Physics University of Texas at Austin AustinTX78712 United States LIGO Laboratory California Institute of Technology PasadenaCA91125 United States MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan NASA Marshall Space Flight Center HuntsvilleAL35811 United States Center for Space Plasma and Aeronomic Research University of Alabama in Huntsville HuntsvilleAL35899 United States School of Physics Georgia Institute of Technology AtlantaGA30332 United States Concordia University Wisconsin MequonWI53097 United States Department of Physics Hayes Hall Kenyon College GambierOH43022 United States Department of Physics University of Michigan Ann ArborMI48109 United States

Matched-filtering gravitational wave search pipelines identify gravitational wave signals by computing correlations, i.e., signal-to-noise ratios, between gravitational wave detector data and gravitational wave template waveforms. Intrinsic parameters, the component masses and spins, of the gravitational wave waveforms are often stored in "template banks", and the construction of a densely populated template bank is essential for some gravitational wave search pipelines. This paper presents a template bank that is currently being used by the GstLAL-based compact binary search pipeline in the fourth observing run of the LIGO, Virgo, and KAGRA collaboration, and was generated with a new binary tree approach of placing templates, manifold. The template bank contains 1.8×106 sets of template parameters covering plausible neutron star and black hole systems up to a total mass of 400 Mo with component masses between 1-200 Mo and mass ratios between 1 and 20 under the assumption that each component object’s angular momentum is aligned with the orbital angular momentum. We validate the template bank generated with our new method, manifold, by comparing it with a template bank generated with the previously used stochastic template placement method. We show that both template banks have similar effectualness. The GstLAL search pipeline performs singular value decomposition (SVD) on the template banks to reduce the number of filters used. We describe a new grouping of waveforms that improves the computational efficiency of SVD by nearly 5 times as compared to previously reported SVD sorting schemes. Copyright © 2022, The Authors. All rights reserved.

关键词： Singular value decomposition

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Perspectives on adaptive dynamical systems

arXiv

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arXiv 2023年

作者： Sawicki, Jakub Berner, Rico Loos, Sarah A.M. Anvari, Mehrnaz Bader, Rolf Barfuss, Wolfram Botta, Nicola Brede, Nuria Franović, Igor Gauthier, Daniel J. Goldt, Sebastian Hajizadeh, Aida Hövel, Philipp Karin, Omer Lorenz-Spreen, Philipp Miehl, Christoph Mölter, Jan Olmi, Simona Schöll, Eckehard Seif, Alireza Tass, Peter A. Volpe, Giovanni Yanchuk, Serhiy Kurths, Jürgen Potsdam Institute for Climate Impact Research Telegrafenberg Potsdam14473 Germany Fachhochschule Nordwestschweiz FHNW Leonhardsstrasse 6 Basel4009 Switzerland Department of Physics Humboldt-Universität zu Berlin Newtonstraße 15 Berlin12489 Germany DAMTP University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt-Augustin53757 Germany Institute of Systematic Musicology University of Hamburg Germany Transdisciplinary Research Area: Sustainable Futures University of Bonn Bonn53113 Germany University of Bonn Bonn53113 Germany Department of Computer Science and Engineering Chalmers University of Technology Göteborg412 96 Sweden Department of Computer Science University of Potsdam An der Bahn 2 Potsdam14476 Germany Scientific Computing Laboratory Center for the Study of Complex Systems Institute of Physics Belgrade University of Belgrade Pregrevica 118 Belgrade11080 Serbia The Ohio State University Department of Physics and Electrical and Computer Engineering 191 West Woodruff Ave ColumbusOH43210 United States ResCon Technologies LLC 1275 Kinnear Rd. Suite 239 ColumbusOH43212 United States Trieste Italy Research Group Comparative Neuroscience Leibniz Institute for Neurobiology Magdeburg Germany Theoretical Physics Saarland University Saarbrücken66123 Germany Center for Biophysics Saarland University Saarbrücken66123 Germany Department of Mathematics Imperial College London United Kingdom Center for Adaptive Rationality Max Planck Institute for Human Development Lentzeallee 94 Berlin14195 Germany Max Planck Institute for Brain Research Max-von-Laue-Straße 4 Frankfurt am Main60438 Germany Technical University of Munich School of Life Sciences Alte Akademie 8 Freising85354 Germany Department of Mathematics School of Computation Information and Technology Technical University of Munich Boltzmannstraße 3 Garching bei München8574

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems like the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges, and give perspectives on future research directions, looking to inspire interdisciplinary approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamical systems

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The NANOGrav 15-year Data Set: Search for Anisotropy in the Gravitational-Wave Background

arXiv

引用

arXiv 2023年

作者： Agazie, Gabriella Anumarlapudi, Akash Archibald, Anne M. Arzoumanian, Zaven Baker, Paul T. Bécsy, Bence Blecha, Laura Brazier, Adam Brook, Paul R. Burke-Spolaor, Sarah Andrew Casey-Clyde, J. Charisi, Maria Chatterjee, Shami Cohen, Tyler Cordes, James M. Cornish, Neil J. Crawford, Fronefield Thankful Cromartie, H. Crowter, Kathryn DeCesar, Megan E. Demorest, Paul B. Dolch, Timothy Drachler, Brendan Ferrara, Elizabeth C. Fiore, William Fonseca, Emmanuel Freedman, Gabriel E. Gardiner, Emiko Garver-Daniels, Nate Gentile, Peter A. Glaser, Joseph Good, Deborah C. Gültekin, Kayhan Hazboun, Jeffrey S. Jennings, Ross J. Johnson, Aaron D. Jones, Megan L. Kaiser, Andrew R. Kaplan, David L. Kelley, Luke Zoltan Kerr, Matthew Key, Joey S. Laal, Nima Lam, Michael T. Lamb, William G. Joseph W. Lazio, T. Lewandowska, Natalia Liu, Tingting Lorimer, Duncan R. Luo, Jing Lynch, Ryan S. Ma, Chung-Pei Madison, Dustin R. McEwen, Alexander McKee, James W. McLaughlin, Maura A. McMann, Natasha Meyers, Bradley W. Mingarelli, Chiara M.F. Mitridate, Andrea Ng, Cherry Nice, David J. Ocker, Stella Koch Olum, Ken D. Pennucci, Timothy T. Perera, Benetge B.P. Pol, Nihan S. Radovan, Henri A. Ransom, Scott M. Ray, Paul S. Romano, Joseph D. Sardesai, Shashwat C. Schmiedekamp, Ann Schmiedekamp, Carl Schmitz, Kai Schult, Levi Shapiro-Albert, Brent J. Siemens, Xavier Simon, Joseph Siwek, Magdalena S. Stairs, Ingrid H. Stinebring, Daniel R. Stovall, Kevin Susobhanan, Abhimanyu Swiggum, Joseph K. Taylor, Stephen R. Turner, Jacob E. Unal, Caner Vallisneri, Michele Vigeland, Sarah J. Wahl, Haley M. Witt, Caitlin A. Young, Olivia Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Newcastle University NE1 7RU United Kingdom X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Department of Physics and Astronomy Widener University One University Place ChesterPA19013 United States Department of Physics Oregon State University CorvallisOR97331 United States Physics Department University of Florida GainesvilleFL32611 United States Cornell Center for Astrophysics and Planetary Science Department of Astronomy Cornell University IthacaNY14853 United States Cornell Center for Advanced Computing Cornell University IthacaNY14853 United States Institute for Gravitational Wave Astronomy School of Physics and Astronomy University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Department of Physics University of Connecticut 196 Auditorium Road U-3046 StorrsCT06269-3046 United States Department of Physics and Astronomy Vanderbilt University 2301 Vanderbilt Place NashvilleTN37235 United States Department of Physics New Mexico Institute of Mining and Technology 801 Leroy Place SocorroNM87801 United States Department of Physics Montana State University BozemanMT59717 United States Department of Physics and Astronomy Franklin & Marshall College P.O. Box 3003 LancasterPA17604 United States Department of Physics and Astronomy University of British Columbia 6224 Agricultural Road VancouverBCV6T 1Z1 Canada George Mason University the Naval Research Laboratory WashingtonDC20375 United States National Radio Astronomy Observatory 1003 Lopezville Rd. SocorroNM87801 Un

The North American Nanohertz Observatory for Gravitational Waves (NANOGrav) has reported evidence for the presence of an isotropic nanohertz gravitational wave background (GWB) in its 15 yr dataset. However, if the GWB is produced by a population of inspiraling supermassive black hole binary (SMBHB) systems, then the background is predicted to be anisotropic, depending on the distribution of these systems in the local Universe and the statistical properties of the SMBHB population. In this work, we search for anisotropy in the GWB using multiple methods and bases to describe the distribution of the GWB power on the sky. We do not find significant evidence of anisotropy, and place a Bayesian 95% upper limit on the level of broadband anisotropy such that (Cl>0/Cl=0) © 2023, CC BY.

关键词： Gravity waves

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