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Sp(2N) Yang-Mills theories on the lattice: Scale setting and topology

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Physical Review D 2022年第9期106卷 094503-094503页

作者： Ed Bennett Deog Ki Hong Jong-Wan Lee C.-J. David Lin Biagio Lucini Maurizio Piai Davide Vadacchino Swansea Academy of Advanced Computing Swansea University Fabian Way SA1 8EN Swansea Wales United Kingdom Department of Physics Pusan National University Busan 46241 Republic of Korea Institute for Extreme Physics Pusan National University Busan 46241 Republic of Korea Institute of Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu 30010 Taiwan Center for High Energy Physics Chung-Yuan Christian University Chung-Li 32023 Taiwan Centre for Theoretical and Computational Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu 30010 Taiwan Physics Division National Centre for Theoretical Sciences Taipei 10617 Taiwan Department of Mathematics Faculty of Science and Engineering Swansea University Fabian Way SA1 8EN Swansea Wales United Kingdom Department of Physics Faculty of Science and Engineering Swansea University Singleton Park SA2 8PP Swansea Wales United Kingdom School of Mathematics and Hamilton Mathematics Institute Trinity College Dublin 2 Ireland Centre for Mathematical Sciences University of Plymouth PL4 8AA Plymouth United Kingdom

We study Yang-Mills lattice theories with Sp(Nc) gauge group, with Nc=2N, for N=1,…,4. We show that if we divide the renormalized couplings appearing in the Wilson flow by the quadratic Casimir C2(F) of the Sp(Nc) group, then the resulting quantities display a good agreement among all values of Nc considered, over a finite interval in flow time. We use this scaled version of the Wilson flow as a scale-setting procedure, compute the topological susceptibility of the Sp(Nc) theories, and extrapolate the results to the continuum limit for each Nc.

关键词： Large-N expansion in field theory Lattice field theory Nonperturbative effects in field theory Quantum field theory Strong interaction Topology

Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation

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arXiv 2023年

作者： Acosta, Fernando Torales Mikuni, Vinicius Nachman, Benjamin Arratia, Miguel Karki, Bishnu Milton, Ryan Karande, Piyush Angerami, Aaron Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States Department of Physics and Astronomy University of California RiversideCA92521 United States Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Nuclear and Chemical Science Division Lawrence Livermore National Laboratory LivermoreCA94550 United States

Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared. © 2023, CC BY.

关键词： Calorimeters

High-Precision Ringdown Surrogate Model for Non-Precessing Binary Black Holes

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arXiv 2024年

作者： Zertuche, Lorena Magaña Stein, Leo C. Mitman, Keefe Field, Scott E. Varma, Vijay Boyle, Michael Deppe, Nils Kidder, Lawrence E. Moxon, Jordan Pfeiffer, Harald P. Scheel, Mark A. Nelli, Kyle C. Throwe, William Vu, Nils L. Department of Physics and Astronomy University of Mississippi UniversityMS38677 United States Theoretical Astrophysics 350-17 California Institute of Technology PasadenaCA91125 United States Department of Mathematics Center for Scientific Computing & Visualization Research University of Massachusetts DartmouthMA02747 United States Department of Physics Center for Computational Research East Hall University of Rhode Island KingstonRI02881 United States Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Laboratory for Elementary Particle Physics Cornell University IthacaNY14853 United States Department of Physics Cornell University IthacaNY14853 United States Am Mühlenberg 1 Potsdam14476 Germany

Highly precise and robust waveform models are required as improvements in detector sensitivity enable us to test general relativity with more precision than ever before. In this work, we introduce a spin-aligned surrogate ringdown model. This ringdown surrogate, NRSur3dq8_RD, is built with numerical waveforms produced using Cauchy-characteristic evolution. In addition, these waveforms are in the superrest frame of the remnant black hole allowing us to do a correct analysis of the ringdown spectrum. The novel prediction of our surrogate model is complex-valued quasinormal mode (QNM) amplitudes, with median relative errors of 10−2 − 10−3 over the parameter space. Like previous remnant surrogates, we also predict the remnant black hole’s mass and spin. The QNM mode amplitude errors translate into median errors on ringdown waveforms of ∼ 10−4. The high accuracy and QNM mode content provided by our surrogate will enable high-precision ringdown analyses such as tests of general relativity. Our ringdown model is publicly available through the python package surfinBH. Copyright © 2024, The Authors. All rights reserved.

关键词： Relativity

Approximation of Fully Optimized HD-tDCS Stimulus Patterns with Fewer Current Sources Using Branch and Bound Algorithm

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Brain Stimulation 2017年第1期10卷 e12-e12页

作者： Seyhmus Guler Moritz Dannhauer Burak Erem Rob Macleod Don Tucker Sergei Turovets Phan Luu Deniz Erdogmus Dana H. Brooks Department of Electrical and Computer Engineering Northeastern University Boston MA USA Center for Integrative Biomedical Computing University of Utah Salt Lake UT USA Scientific Computing and Imaging Institute University of Utah Salt Lake UT USA Computational Radiology Laboratory Boston Children's Hospital Boston MA USA Electrical Geodesics Inc. (EGI) Eugene OR USA

Corrigendum to “High order positivity-preserving discontinuous Galerkin schemes for radiative transfer equations on triangular meshes” [J. Comput. Phys. 397 (2019) 108811]

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Journal of computational Physics 2020年 406卷

作者： Min Zhang Juan Cheng Jianxian Qiu School of Mathematical Sciences Xiamen University Xiamen Fujian 361005 PR China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing 100088 PR China Center for Applied Physics and Technology Peking University Beijing 100871 PR China School of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computing Xiamen University Xiamen Fujian 361005 PR China

Randomized compiling for scalable quantum computing on a noisy superconducting quantum processor

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arXiv 2020年

作者： Hashim, Akel Naik, Ravi K. Morvan, Alexis Ville, Jean-Loup Mitchell, Bradley Kreikebaum, John Mark Davis, Marc Smith, Ethan Iancu, Costin O'Brien, Kevin P. Hincks, Ian Wallman, Joel J. Emerson, Joseph Siddiqi, Irfan Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley BerkeleyCA94720 United States Graduate Group in Applied Science and Technology University of California at Berkeley BerkeleyCA94720 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Quantum Benchmark Inc. KitchenerONN2H 5G5 Canada Institute for Quantum Computing and Department of Applied Mathematics University of Waterloo WaterlooONN2L 3G1 Canada

The successful implementation of algorithms on quantum processors relies on the accurate control of quantum bits (qubits) to perform logic gate operations. In this era of noisy intermediate-scale quantum (NISQ) computing, systematic miscalibrations, drift, and crosstalk in the control of qubits can lead to a coherent form of error which has no classical analog. Coherent errors severely limit the performance of quantum algorithms in an unpredictable manner, and mitigating their impact is necessary for realizing reliable quantum computations. Moreover, the average error rates measured by randomized benchmarking and related protocols are not sensitive to the full impact of coherent errors, and therefore do not reliably predict the global performance of quantum algorithms, leaving us unprepared to validate the accuracy of future large-scale quantum computations. Randomized compiling is a protocol designed to overcome these performance limitations by converting coherent errors into stochastic noise, dramatically reducing unpredictable errors in quantum algorithms and enabling accurate predictions of algorithmic performance from error rates measured via cycle benchmarking. In this work, we demonstrate significant performance gains under randomized compiling for the four-qubit quantum Fourier transform algorithm and for random circuits of variable depth on a superconducting quantum processor. Additionally, we accurately predict algorithm performance using experimentally-measured error rates. Our results demonstrate that randomized compiling can be utilized to leverage and predict the capabilities of modern-day noisy quantum processors, paving the way forward for scalable quantum computing. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

Analysis of GWTC-3 with fully precessing numerical relativity surrogate models

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arXiv 2023年

作者： Islam, Tousif Vajpeyi, Avi Shaik, Feroz H. Haster, Carl-Johan Varma, Vijay Field, Scott E. Lange, Jacob O’Shaughnessy, Richard Smith, Rory Department of Mathematics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Data Science Research University of Massachusetts DartmouthMA02747 United States Department of Statistics The University of Auckland New Zealand School of Physics and Astronomy Monash University VIC3800 Australia OzGrav The ARC Centre of Excellence for Gravitational Wave Discovery ClaytonVIC3800 Australia Department of Physics and Astronomy University of Nevada Las Vegas 4505 South Maryland Parkway Las VegasNV89154 United States Nevada Center for Astrophysics University of Nevada Las VegasNV89154 United States 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 Max Planck Institute for Gravitational Physics Albert Einstein Institute PotsdamD-14476 Germany Center for Computational Relativity and Gravitation Rochester Institute of Technology RochesterNY14623 United States University of Texas AustinTX78712 United States

The third Gravitational-Wave Transient Catalog (GWTC-3) contains 90 binary coalescence candidates detected by the LIGO-Virgo-KAGRA Collaboration (LVK). We provide a re-analysis of binary black hole (BBH) events using a recently developed numerical relativity (NR) waveform surrogate model, NRSur7dq4, that includes all ≤ 4 spin-weighted spherical harmonic modes as well as the complete physical effects of precession. Properties of the remnant black holes’ (BH’s) mass, spin vector, and kick vector are found using an associated remnant surrogate model NRSur7dq4Remnant. Both NRSur7dq4 and NRSur7dq4Remnant models have errors comparable to numerical relativity simulations and allow for high-accuracy parameter estimates. We restrict our analysis to 47 BBH events that fall within the regime of validity of NRSur7dq4 (mass ratios greater than 1/6 and total masses greater than 60M☉). While for most of these events our results match the LVK analyses that were obtained using the semi-analytical models such as IMRPhenomXPHM and SEOBNRv4PHM, we find that for more than 20% of events the NRSur7dq4 model recovers noticeably different measurements of black hole properties like the masses and spins, as well as extrinsic properties like the binary inclination and distance. For instance, GW150914_095045 exhibits noticeable differences in spin precession and spin magnitude measurements. Other notable findings include one event (GW191109_010717) that constrains the effective spin χeff to be negative at a 99.3% credible level and two events (GW191109_010717 and GW200129_065458) with well-constrained kick velocities. Furthermore, compared to the models used in the LVK analyses, NRSur7dq4 recovers a larger signal-to-noise ratio and/or Bayes factors for several events. While these findings have important astrophysical implications, some of the more interesting events are affected by transient detector noise, which can be challenging to remove from short-duration signals. Copyright © 2023, The Au

关键词： Black holes

Corrigendum to: A convergent adaptive finite element method for elliptic Dirichlet boundary control problems

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IMA Journal of Numerical Analysis 2020年第1期40卷 800-800页

作者： Gong, Wei Liu, Wenbin Tan, Zhiyu Yan, Ningning National Center for Mathematics and Interdisciplinary Sciences Chinese Academy of Sciences Beijing China The State Key Laboratory of Scientific and Engineering Computing Chinese Academy of Sciences Beijing China Institute of Computational Mathematics Chinese Academy of Sciences Beijing China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Kent Business School University of Kent Kent UK Department of Mathematics Hong Kong Baptist University Kowloon Tong Hong Kong SAR NCMIS Chinese Academy of Sciences Beijing China LSEC Chinese Academy of Sciences Beijing China Institute of Systems Science Chinese Academy of Sciences Beijing China

SWEMniCS: a software toolbox for modeling coastal ocean circulation, storm surges, inland, and compound flooding

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npj Natural Hazards

npj Natural Hazards 2024年第1期1卷 1-17页

作者： Clint Dawson Benjamin Pachev Jennifer Proft Eirik Valseth Mark Loveland Oden Institute for Computational Engineering and Sciences The University of Texas at Austin Austin TX USA The Department of Data Science The Norwegian University of Life Science Ås Norway Department of Scientific Computing and Numerical Analysis Simula Research Laboratory Oslo Norway Information and Technology Laboratory Engineering Research and Development Center US Army Corps of Engineers Vicksburg MS USA

Flooding from storm surges, rainfall-runoff, and their interaction into compounding events are major natural hazards in coastal regions. To assess risks of damages to life and properties alike, numerical models are needed to guide emergency responses and future assessments. Numerical models, such as ADCIRC have over many decades shown their usefulness in such assessments. However, these models have a high threshold in terms of new user engagement as development and compilation is not trivial for users trained in compiled programming languages. Here, we develop a new open-source finite element solver for the numerical simulation of flooding. The numerical solution of the underlying PDEs is developed using the finite element framework FEniCSx. The goal is a framework where new methods can be rapidly tested before time-consuming development into codes like ADCIRC. We validate the framework on several test cases, including large-scale computations in the Gulf of Mexico for Hurricane Ike (2008).

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