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Interplay between numerical relativity and perturbation theory: Finite size effects

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Physical Review D 2023年第4期108卷 044012-044012页

作者： Tousif Islam Gaurav Khanna Department of Physics University of Massachusetts Dartmouth Massachusetts 02747 USA Department of Mathematics University of Massachusetts Dartmouth Massachusetts 02747 USA Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth Massachusetts 02747 USA Department of Physics and Center for Computational Research University of Rhode Island Kingston Rhode Island 02881 USA

We investigate the interplay between numerical relativity (NR) and point-particle black hole perturbation theory (ppBHPT) in the comparable mass ratio regime. In the ppBHPT framework, the secondary black hole is treated as a point particle, neglecting its finite size. Our study focuses on addressing the missing finite size effect in the ppBHPT framework and proposing a method for incorporating the size of the secondary into the perturbation theory framework. We demonstrate that by considering the secondary as a finite size object, the BHPT waveforms closely match NR waveforms. Additionally, we revisit the α−β scaling technique, which was previously introduced by Islam et al. [Phys. Rev. D 106, 104025 (2022)], as a means to effectively match ppBHPT waveforms to NR waveforms. We further analyze the scaling procedure and decompose it into different components, attributing them to various effects, including the corrections arising from the finite size of the secondary black hole.

关键词： General relativity Gravitational waves

Optimizing post-Newtonian parameters and fixing the BMS frame for numerical-relativity waveform hybridizations

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arXiv 2024年

作者： Sun, Dongze Boyle, Michael Mitman, Keefe Scheel, Mark A. Stein, Leo C. Teukolsky, Saul A. Varma, Vijay Theoretical Astrophysics 350-17 California Institute of Technology 1200 E California Boulevard PasadenaCA91125 United States Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Department of Physics and Astronomy University of Mississippi UniversityMS38677 United States Department of Mathematics Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States PotsdamD-14476 Germany

Numerical relativity (NR) simulations of binary black holes provide precise waveforms, but are typically too computationally expensive to produce waveforms with enough orbits to cover the whole frequency band of gravitational-wave observatories. Accordingly, it is important to be able to hybridize NR waveforms with analytic, post-Newtonian (PN) waveforms, which are accurate during the early inspiral phase. We show that to build such hybrids, it is crucial to both fix the Bondi-Metzner-Sachs (BMS) frame of the NR waveforms to match that of PN theory, and optimize over the PN parameters to mitigate the error caused by the discrepancy between NR and PN parameters. We test such a hybridization procedure including all spin-weighted spherical harmonic modes with |m| ≤ for ≤ 8, using 29 NR waveforms with mass ratios q ≤ 10 and spin magnitudes |χ1|, |χ2| ≤ 0.8. We find that for spin-aligned systems, the PN and NR waveforms agree very well. The difference is limited by the small nonzero orbital eccentricity of the NR waveforms, or equivalently by the lack of eccentric terms in the PN waveforms. To maintain full accuracy of the simulations, the matching window for spin-aligned systems should be at least 5 orbits long and end at least 15 orbits before merger. For precessing systems, the errors are larger than for spin-aligned cases. The errors are likely limited by the absence of precession-related spin-spin PN terms. Using 105 M long NR waveforms, we find that there is no optimal choice of the matching window within this time span, because the hybridization result for precessing cases is always better if using earlier or longer matching windows. We provide the mean orbital frequency of the smallest acceptable matching window as a function of the target error between the PN and NR waveforms and the black hole spins. Copyright © 2024, The Authors. All rights reserved.

关键词： Relativity

Remnant black hole properties from numerical-relativity-informed perturbation theory and implications for waveform modelling

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arXiv 2023年

作者： Islam, Tousif Field, Scott E. Khanna, Gaurav Department of Physics University of Massachusetts DartmouthMA02747 United States Department of Mathematics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States Department of Physics University of Rhode Island KingstonRI02881 United States

During binary black hole (BBH) mergers, energy and momenta are carried away from the binary system as gravitational radiation. Access to the radiated energy and momenta allows us to predict the properties of the remnant black hole. We develop a Python package gw_remnant to compute the remnant mass, remnant spin, peak luminosity, and the final kick imparted on the remnant black hole from the gravitational radiation. Using this package, we compute the remnant properties of the final black hole in case of non-spinning BBH mergers with mass ratios ranging from q = 2.5 to q = 1000 using waveform modes generated from BHPTNRSur1dq1e4, a recently developed numerical-relativity-informed surrogate model based on black-hole perturbation theory framework. We validate our results against the remnant properties estimated from numerical relativity (NR) surrogate models in the comparable mass ratio regime and against recently available high-mass-ratio NR simulations at q = [15, 32, 64]. We find that our remnant property estimates computed from fluxes at future null infinity closely match the estimates obtained from the NR surrogate model of apparent horizon data. Using Gaussian process regression fitting methods, we train a surrogate model, BHPTNR_Remnant, for the properties of the remnant black hole arising from BBH mergers with mass ratios from q = 2.5 to q = 1000. Finally, we discuss potential improvements in the BHPTNRSur1dq1e4 waveform model when including remnant information. We make both the gw_remnant and BHPTNR_Remnant packages publicly available. Copyright © 2023, The Authors. All rights reserved.

关键词： Black holes

Memetic Optimization with Cryptographic Encryption for Secure Medical Data Transmission in IoT-Based Distributed Systems

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Computers, Materials & Continua 2021年第2期66卷 1577-1594页

作者： Srinath Doss Jothi Paranthaman Suseendran Gopalakrishnan Akila Duraisamy Souvik Pal Balaganesh Duraisamy Chung Le Van Dac-Nhuong Le Faculty of Computing Botho UniversityGaboroneBotswana Department of Computer Science Gaborone University College of Law and Professional StudiesGaboroneBotswana Vels Institute of Science Technology&Advanced StudiesChennai600117India Global Institute of Management and Technology West Bengal741102India Faculty of Computer Science and Multimedia Lincoln University CollegeKelantan15050Malaysia Center for Visualization&Simulation Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Haiphong UniversityHaiphong180000Vietnam

In the healthcare system,the Internet of Things(IoT)based distributed systems play a vital role in transferring the medical-related documents and information among the organizations to reduce the replication in medical *** datum is sensitive,and hence security is a must in transforming the sensational *** this paper,an Evolutionary Algorithm,namely the Memetic Algorithm is used for encrypting the text *** encrypted information is then inserted into the medical images using Discrete Wavelet Transform 1 level and 2 *** reverse method of the Memetic Algorithm is implemented when extracting a hidden message from the encoded *** show its precision,equivalent to five RGB images and five Grayscale images are used to test the proposed *** results of the proposed algorithm were analyzed using statistical methods,and the proposed algorithm showed the importance of data transfer in healthcare systems in a stable *** the future,to embed the privacy-preserving of medical data,it can be extended with blockchain technology.

关键词： Internet of Things medical images cryptography memetic algorithm steganography

White dwarf mass-radius relation in theories beyond general relativity

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arXiv 2023年

作者： Alam, Khursid Islam, Tousif Department of Physical Sciences Indian Institute of Science Education and Research Kolkata WB Mohanpur741 246 India Department of Physics University of Massachusetts DartmouthMA02747 United States Department of Mathematics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States

We explore the internal structures of the white dwarfs in two different modified theories of gravity: (i) scalar-tensor-vector gravity and (ii) beyond Horndeski theories of G3 type. The modification of the gravitational force inside the white dwarf results in the modification of the mass and radius of the white dwarf. We use observational data from various astrophysical probes including Gaia to test the validity of these two classes of modified theories of gravity. We update the constraints on the parameters controlling the deviation from general relativity (and Newtonian gravity in the weak field limit) as: 0.007 ≤ α for the scalar-tensor-vector gravity and −0.08 ≤ γ ≤ 0.007 for the beyond Horndeski theories of G3 type. Finally, we demonstrate the selection effect of the astrophysical data on the tests of the nature of gravity using white dwarf mass-radius relations specially in cases where the number of data-points are not many. Copyright © 2023, The Authors. All rights reserved.

关键词： White dwarfs

Surrogate modeling of gravitational waves microlensed by spherically symmetric potentials

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arXiv 2025年

作者： Deka, Uddeepta Prabhu, Gopalkrishna Shaikh, Md Arif Kapadia, Shasvath J. Varma, Vijay Field, Scott E. International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bangalore560089 India Inter-University Centre for Astronomy and Astrophysics Post Bag 4 Ganeshkhind Pune411007 India Department of Physics Vivekananda Satavarshiki Mahavidyalaya Vidyasagar University West Bengal Manikpara721513 India Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Department of Mathematics Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States

The anticipated observation of the gravitational microlensing of gravitational waves (GWs) promises to shed light on a host of astrophysical and cosmological questions. However, extracting the parameters of the lens from the modulated GWs requires accurate modeling of the lensing amplification factor, accounting for wave-optics effects. Analytic solutions to the lens equation have not been found to date, except for a handful of simplistic lens models. While numerical solutions to this equation have been developed, the time and computational resources required to evaluate the amplification factor numerically make large-scale parameter estimation of the lens (and source) parameters prohibitive. On the other hand, surrogate modeling of GWs has proven to be a powerful tool to accurately, and rapidly, produce GW templates at arbitrary points in parameter space, interpolating from a finite set of available waveforms at discrete parameter values. In this work, we demonstrate that surrogate modeling can also effectively be applied to the evaluation of the time-domain microlensing amplification factor eF(t), for arbitrary lens configurations. We show this by constructing eF(t) for two lens models, viz. point-mass lens, and singular isothermal sphere. We benchmark the evaluation times, and accuracy, of the surrogate microlensed templates, and show that indeed surrogate modeling is a powerful tool to make large-scale parameter estimation in the context of microlensed GWs practicable. © 2025, CC BY.

关键词： Time domain analysis

ChartKG: A Knowledge-Graph-Based Representation for Chart Images

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arXiv 2024年

作者： Zhou, Zhiguang Wang, Haoxuan Zhao, Zhengqing Zheng, Fengling Wang, Yongheng Chen, Wei Wang, Yong School of Media and Design Hangzhou Dianzi University CO Hangzhou310018 China School of Media and Design Hangzhou Dianzi University China Big Data Visualization and Human Computer Collaborative Intelligent Laboratory Hangzhou Dianzi University China Research Center of Big Data Intelligence Zhejiang Lab Hangzhou China State Key Lab of CAD&CG Zhejiang University China College of Computing and Data Science Nanyang Technological University Singapore

Chart images, such as bar charts, pie charts, and line charts, are explosively produced due to the wide usage of data visualizations. Accordingly, knowledge mining from chart images is becoming increasingly important, which can benefit downstream tasks like chart retrieval and knowledge graph completion. However, existing methods for chart knowledge mining mainly focus on converting chart images into raw data and often ignore their visual encodings and semantic meanings, which can result in information loss for many downstream tasks. In this paper, we propose ChartKG, a novel knowledge graph (KG) based representation for chart images, which can model the visual elements in a chart image and semantic relations among them including visual encodings and visual insights in a unified ***, we develop a general framework to convert chart images to the proposed KG-based representation. It integrates a series of image processing techniques to identify visual elements and relations, e.g., CNNs to classify charts, yolov5 and optical character recognition to parse charts, and rule-based methods to construct graphs. We present four cases to illustrate how our knowledge-graph-based representation can model the detailed visual elements and semantic relations in charts, and further demonstrate how our approach can benefit downstream applications such as semantic-aware chart retrieval and chart question answering. We also conduct quantitative evaluations to assess the two fundamental building blocks of our chart-to-KG framework, i.e., object recognition and optical character recognition. The results provide support for the usefulness and effectiveness of ChartKG. © 2024, CC BY.

关键词： Knowledge graph

Mutually-Constrained Cross-Sectional and Longitudinal Non-Negative Matrix Factorization: Application to Modeling Brain Aging Trajectories

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Mutually-Constrained Cross-Sectional and Longitudinal Non-Ne...

IEEE International Symposium on Biomedical Imaging

作者： Ioanna Skampardoni Junhao Wen Erus Guray Haochang Shou Konstantina Nikita Christos Davatzikos Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Laboratory of AI and Biomedical Science (LABS) Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University of Southern California Los Angeles CA USA Department of Biostatistics Epidemiology & Informatics Penn Statistics in Imaging and Visualization Center University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Brain aging is a multifaceted and highly heterogeneous process accompanied by several pathologies. Here, we propose a method for dissecting the heterogeneity of neuropathologic processes occurring with aging using machine learning and leveraging information from cross-sectional and longitudinal data. Specifically, we hypothesize that the heterogeneity observed in brain aging can be captured by a set of patterns consistent with longitudinal trajectories of brain change, the latter directly capturing evolving neuropathologic processes on an individual basis. Applying the method to structural magnetic resonance imaging data from the BLSA study, we derived five distinct, reproducible, and clinically informative components of neuroanatomical brain change, highlighting the method’s potential as a tool for precision medicine.

关键词： Pathology Magnetic resonance imaging Precision medicine Sociology Aging Hazards Trajectory

Survey of gravitational wave memory in intermediate mass ratio binaries

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Physical Review D 2023年第2期108卷 024046-024046页

作者： Tousif Islam Scott E. Field Gaurav Khanna Niels Warburton Department of Physics University of Massachusetts Dartmouth Massachusetts 02747 USA Department of Mathematics University of Massachusetts Dartmouth Massachusetts 02747 USA Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth Massachusetts 02747 USA Kavli Institute for Theoretical Physics University of California Santa Barbara California 93106 USA Department of Physics University of Rhode Island Kingston Rhode Island 02881 USA School of Mathematics and Statistics University College Dublin Belfield Dublin 4 Ireland

The nonlinear gravitational wave (GW) memory effect is a distinct prediction in general relativity. While the effect has been well studied for comparable mass binaries, it has mostly been overlooked for intermediate mass ratio inspirals (IMRIs). We offer a comprehensive analysis of the phenomenology and detectability of memory effects, including contributions from subdominant harmonic modes, in heavy IMRIs consisting of a stellar mass black hole and an intermediate mass black hole. When formed through hierarchical mergers, for example when a GW190521-like remnant captures a stellar mass black hole, IMRI systems have a large total mass, large spin on the primary, and possibly residual eccentricity; features that potentially raise the prospect for memory detection. We compute both the displacement and spin nonlinear GW memory from the m≠0 gravitational waveforms computed within a black hole perturbation theory framework that is partially calibrated to numerical relativity waveforms. We probe the dependence of memory effects on mass ratio, spin, and eccentricity and consider the detectability of a memory signal from IMRIs using current and future GW detectors. We find that (i) while eccentricity introduces additional features in both displacement and spin memory, it does not appreciatively change the prospects of detectability, (ii) including higher modes into the memory computation can increase signal-to-noise (SNR) values by about 7% in some cases, (iii) the SNR from displacement memory dramatically increases as the spin approaches large, positive values, and (iv) spin memory from heavy IMRIs would, however, be difficult to detect with future generation detectors even from highly spinning systems. Our results suggest that hierarchical binary black hole mergers may be a promising source for detecting memory and could favorably impact memory forecasts.

关键词： Gravitational waves