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Improved analysis of GW190412 with a precessing numerical relativity surrogate waveform model

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Physical Review D 2021年第10期103卷 104027-104027页

作者： Tousif Islam Scott E. Field Carl-Johan Haster Rory Smith Department of Physics and Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth Massachusetts 02747 USA Department of Mathematics and Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth Massachusetts 02747 USA LIGO Laboratory Massachusetts Institute of Technology 185 Albany St Cambridge Massachusetts 02139 USA Department of Physics and Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology 77 Massachusetts Ave Cambridge Massachusetts 02139 USA School of Physics and Astronomy Monash University Vic 3800 Australia OzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery Clayton VIC 3800 Australia

The recent observation of GW190412, the first high-mass ratio binary black hole merger, by the LIGO-Virgo Collaboration (LVC) provides a unique opportunity to probe the impact of subdominant harmonics and precession effects encoded in a gravitational wave signal. We present refined estimates of source parameters for GW190412 using nrsur7dq4, a recently developed numerical relativity waveform surrogate model that includes all ℓ≤4 spin-weighted spherical harmonic modes as well as the full physical effects of precession. We compare our results with two different variants of phenomenological precessing binary black hole waveform models, imrphenompv3hm and imrphenomxphm, as well as to the LVC results. Our results are broadly in agreement with imrphenomxphm results and the reported LVC analysis compiled with the seobnrv4phm waveform model, but in tension with imrphenompv3hm. Using the nrsur7dq4 model, we provide a tighter constraint on the mass ratio (0.26−0.06+0.08) as compared to the LVC estimate of 0.28−0.07+0.13 (both reported as median values with 90% credible intervals). We also constrain the binary to be more face on, and find a broader posterior for the spin precession parameter. We further find that even though ℓ=4 harmonic modes have negligible signal-to-noise ratio, omission of these modes will influence the estimated posterior distribution of several source parameters including chirp mass, effective inspiral spin, luminosity distance, and inclination. We also find that commonly used model approximations, such as neglecting the asymmetric modes (which are generically excited during precession), have negligible impact on parameter recovery for moderate signal-to-noise ratio events similar to GW190412.

关键词： Gravitational waves

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

Space-time collocation method: Loop quantum hamiltonian constraints

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arXiv 2020年

作者： Yonika, A. Heryudono, A. Khanna, G. Department of Physics Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth 285 Old Westport Road North DartmouthMA02747 United States Department of Mathematics Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth 285 Old Westport Road North DartmouthMA02747 United States

A space-time collocation method (STCM) using asymptotically-constant basis functions is proposed and applied to the quantum Hamiltonian constraint for a loop-quantized treatment of the Schwarzschild interior. Canonically, these descriptions take the form of a partial-difference equation (PDE). The space-time collocation approach presents a computationally efficient, convergent, and easily parallelizable method for solving this class of equations, which is the main novelty of this study. Results of the numerical simulations will demonstrate the benefit from a parallel computing approach;and show general exibility of the framework to handle arbitrarily-sized domains. Computed solutions will be compared, when applicable, to a solution computed in the conventional method via iteratively stepping through a predefined grid of discrete values, computing the solution via a recursive relationship. Copyright © 2020, The Authors. All rights reserved.

关键词： Gravitation

A MULTIGRID REDUCTION FRAMEWORK FOR DOMAINS WITH SYMMETRIES

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arXiv 2024年

作者： Alsalti-Baldellou, Àdel Janna, Carlo Álvarez-Farré, Xavier Xavier Trias, F. Department of Information Engineering University of Padova Via Giovanni Gradenigo 6b Padova PD35131 Italy Heat and Mass Transfer Technological Center Universitat Politècnica de Catalunya BarcelonaTech Carrer de Colom 11 Terrassa08222 Spain Department of Civil Environmental and Architectural Engineering University of Padova Via Francesco Marzolo 9 Padova PD35131 Italy M3E S.r.l. Via Giambellino 7 Padova PD35129 Italy High-Performance Computing and Visualization Team SURF Science Park 140 Amsterdam1098 XG Netherlands

Divergence constraints are present in the governing equations of numerous physical phenomena, and they usually lead to a Poisson equation whose solution represents a bottleneck in many simulation codes. Algebraic Multigrid (AMG) is arguably the most powerful preconditioner for Poisson’s equation, and its effectiveness results from the complementary roles played by the smoother, responsible for damping high-frequency error components, and the coarse-grid correction, which in turn reduces low-frequency modes. This work presents several strategies to make AMG more compute-intensive by leveraging reflection, translational and rotational symmetries. AMGR, our final proposal, does not require boundary conditions to be symmetric, therefore applying to a broad range of academic and industrial configurations. It is based on a multigrid reduction framework that introduces an aggressive coarsening to the multigrid hierarchy, reducing the memory footprint, setup and application costs of the top-level smoother. While preserving AMG’s excellent convergence, AMGR allows replacing the standard sparse matrix-vector product with the more compute-intensive sparse matrix-matrix product, yielding significant accelerations. Numerical experiments on industrial CFD applications demonstrated up to 70% speed-ups when solving Poisson’s equation with AMGR instead of AMG. Additionally, strong and weak scalability analyses revealed no significant *** Codes 65F08, 65F50, 65N55, 65Y05 © 2024, CC BY-NC-ND.

关键词： Poisson equation

Simulating transient burst noise with gengli

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arXiv 2022年

作者： Lopez, Melissa Boudart, Vincent Schmidt, Stefano Caudill, Sarah Utrecht University Princetonplein 1 Utrecht3584 CC Netherlands Nikhef Science Park 105 Amsterdam1098 XG Netherlands STAR Institute Bâtiment B5 Université de Liège Sart Tilman B4000 Liège Belgium Department of Physics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States

In the field of gravitational-wave (GW) interferometers, the most severe limitation to the detection of transient signals from astrophysical sources comes from transient noise artefacts, known as glitches, that happens at a rate around 1 per minute. Because glitches reduce the amount of scientific data available, there is a need for better modelling and inclusion of glitches in large-scale studies, such as stress testing the search pipelines and increasing the confidence of detection. In this work, we employ a Generative Adversarial Network (GAN) to produce a particular class of glitches (blip) in the time domain. We share the trained network through a user-friendly open-source software package called gengli and provide practical examples of its usage. Copyright © 2022, The Authors. All rights reserved.

关键词： Gravity waves

Corrigendum to “Thermoelastic homogenization of periodic composites using an eigenstrain-based micromechanical model” [Applied Mathematical Modelling 85 (2020) 1-18

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Applied Mathematical Modelling 2021年 89卷 1999-1999页

作者： Eduardo Nobre Lages Severino Pereira Cavalcanti Marques Laboratory of Scientific Computing and Visualization Center of Technology Federal University of Alagoas Maceió 57072-900 Alagoas Brazil

ES-GP: An Ensemble Surrogate-Assisted Genetic Programming Approach to Image Classification

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IEEE Transactions on Evolutionary Computation 2025年

作者： Fan, Qinglan Zhang, Yunfeng Yao, Xunxiang Bi, Ying Xue, Bing Zhang, Mengjie Shandong University of Finance and Economics School of Computing and Artificial Intelligence Shandong Key Laboratory of Lightweight Intelligent Computing and Visualization for Digital Economy Jinan250014 China Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Luoyang471000 China Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand

Genetic Programming (GP) is a promising evolutionary machine learning technique for image classification, known for its ability to evolve flexible, effective, and interpretable models. However, the high computational cost of fitness evaluations in evolutionary learning restricts its practical applications. While Surrogate models offer efficient approximations for costly fitness evaluations, their application in GP-based image classification remains in its early stages, facing challenges such as handing flexible tree-based representations with variable lengths, designing an effective surrogate, and the limited performance of a single surrogate across various image classification tasks. To address these issues, this paper proposes an ensemble surrogate-assisted GP approach to image classification. The new approach constructs one global surrogate model to explore broad areas and three local surrogate models within specific subspaces to exploit local regions, enabling more accurate predictions of GP individuals' fitness. Moreover, a dynamic weighting strategy is developed to assign different weights to the base surrogate models in the ensemble, improving prediction accuracy. Additionally, the proposed approach refines the surrogate training set1 construction method, previously limited to single-tree GP, enabling it to accelerate both single-tree and multi-tree GP 2 for image classification. Experimental results on five datasets of varying difficulty demonstrate that the new ensemble surrogate method significantly reduces the number of expensive fitness evaluations of both single-tree and multi-tree GP-based image classification methods while achieving competitive performance. The comparisons with other state-of-the-art methods also confirm its effectiveness. © 1997-2012 IEEE.

关键词： Genetic algorithms

Applying higher-modes consistency test on GW190814 : lessons on no-hair theorem, nature of the secondary compact object and waveform modeling

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arXiv 2021年

作者： Islam, Tousif Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States Department of Physics University of Massachusetts DartmouthMA02747 United States Department of Mathematics University of Massachusetts DartmouthMA02747 United States Kavli Institute for Theoretical Physics University of California Santa BarbaraCA93106 United States

As one of the consequences of the black-hole "no-hair" theorem in general relativity (GR), the multipolar structure of the radiation (i.e. different spherical harmonic modes) from a merging quasi-circular binary black hole (BBH) is fully determined by the intrinsic parameters (i.e. the masses and spins of the companion black holes). In Refs. [1, 2], we have formulated an efficient test named ‘higher-modes consistency test’ to check for the consistency of the observed gravitational-wave (GW) signal with the expected multipolar structure of radiation from BBHs in GR. Detection of the high-mass-ratio merger of GW190814 enables the observation of spherical harmonic modes beyond the dominant (`, m) = (2, ±2) mode;thereby providing a unique opportunity to perform the ‘higher-modes consistency test’. Using different state-of-art waveform models (IMRPhenomXPHM, IMRPhenomXHM, IMRPhenomHM and SEONNRv4HM ROM), we show that GW190814 strongly favors the "no-hair" hypothesis in GR over a hypothesis that assumes generic deviation from the multipolar structure of the radiation by a Bayes factor of logeB ∼ 8. We further investigate any potential systematic errors arising as a result of different waveform modeling choices as well as due to neglecting many higher order modes. We find that, if the waveform model includes only (`, m) = (3, ±3) mode in its higher harmonics, the same event may reject the ‘no-hair’ hypothesis in GR when the detector sensitivity improves by a factor of 12.5. Our analysis, therefore, provides motivation to include as many higher order modes as possible in future waveform models. Copyright © 2021, The Authors. All rights reserved.

关键词： Merging

Impact of subdominant modes on the interpretation of gravitational-wave signals from heavy binary black hole systems

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Physical Review D 2020年第12期101卷 124054-124054页

作者： Feroz H. Shaik Jacob Lange Scott E. Field Richard O’Shaughnessy Vijay Varma Lawrence E. Kidder Harald P. Pfeiffer Daniel Wysocki Department of Physics Department of Mathematics and the Center for Scientific Computing & Visualization Research University of Massachusetts Dartmouth Dartmouth Massachusetts 02747 USA Center for Computational Relativity and Gravitation Rochester Institute of Technology Rochester New York 14623 USA Department of Mathematics and the Center for Scientific Computing & Visualization Research University of Massachusetts Dartmouth Dartmouth Massachusetts 02747 USA Theoretical Astrophysics California Institute of Technology Pasadena California 91125 USA Cornell Center for Astrophysics and Planetary Science Cornell University Ithaca New York 14853 USA Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Am Mühlenberg 1 14476 Potsdam Germany

Over the past year, a handful of new gravitational wave models have been developed to include multiple harmonic modes thereby enabling for the first time fully Bayesian inference studies including higher modes to be performed. Using one recently developed numerical relativity surrogate model, NRHybSur3dq8, we investigate the importance of higher modes on parameter inference of coalescing massive binary black holes. We focus on examples relevant to the current three-detector network of observatories, with a detector-frame mass set to 120 M⊙ and with signal amplitude values that are consistent with plausible candidates for the next few observing runs. We show that for such systems the higher mode content will be important for interpreting coalescing binary black holes, reducing systematic bias, and computing properties of the remnant object. Even for comparable-mass binaries and at low signal amplitude, the omission of higher modes can influence posterior probability distributions. We discuss the impact of our results on source population inference and self-consistency tests of general relativity. Our work can be used to better understand asymmetric binary black hole merger events, such as GW190412. Higher modes are critical for such systems, and their omission usually produces substantial parameter biases.

关键词： Gravitational waves