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Factorized Parameter Estimation for Real-Time Gravitational Wave Inference

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arXiv 2022年

作者： Islam, Tousif Roulet, Javier Venumadhav, Tejaswi Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States Department of Mathematics University of Massachusetts DartmouthMA02747 United States Department of Physics University of Massachusetts DartmouthMA02747 United States Kavli Institute for Theoretical Physics University of California at Santa Barbara Santa BarbaraCA93106 United States TAPIR Walter Burke Institute for Theoretical Physics California Institute of Technology PasadenaCA91125 United States Department of Physics University of California at Santa Barbara Santa BarbaraCA93106 United States International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bangalore560089 India

We present a parameter estimation framework for gravitational wave (GW) signals that brings together several ideas to accelerate the inference process. First, we use the relative binning algorithm to evaluate the signal-to-noise-ratio timeseries in each detector for a given choice of intrinsic parameters. Second, we decouple the estimation of the intrinsic parameters (such as masses and spins of the components) from that of the extrinsic parameters (such as distance, orientation, and sky location) that describe a binary compact object coalescence. We achieve this by semi-analytically marginalizing the posterior distribution over extrinsic parameters without repeatedly evaluating the waveform for a fixed set of intrinsic parameters. Finally, we augment samples of intrinsic parameters with extrinsic parameters drawn from their appropriate conditional distributions. We implement the method for binaries with aligned spins, restricted to the quadrupole mode of the signal. Using simulated GW signals, we demonstrate that the method produces full eleven-dimensional posteriors that match those from standard Bayesian inference. Our framework takes only ∼ 200 s to analyze a typical binary-black-hole signal and ∼ 250 s to analyze a typical binary-neutron-star signal using one computing core. Such real-time and accurate estimation of the binary source properties will greatly aid the interpretation of triggers from gravitational wave searches, as well as the search for possible electromagnetic counterparts. We make the framework publicly available via the GW inference package cogwheel. Copyright © 2022, The Authors. All rights reserved.

关键词： Gravity waves

Optimizing neural network surrogate models: Application to black hole merger remnants

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Physical Review D 2025年第10期111卷 104029-104029页

作者： Lucy M. Thomas Katerina Chatziioannou Vijay Varma Scott E. Field TAPIR California Institute of Technology Pasadena California 91125 USA LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Department of Mathematics Center for Scientific Computing and Data Science Research University of Massachusetts Dartmouth Massachusetts 02747 USA

Surrogate models of numerical relativity simulations of merging black holes provide the most accurate tools for gravitational-wave data analysis. Neural network-based surrogates promise evaluation speedups, but their accuracy relies on (often obscure) tuning of settings such as the network architecture, hyperparameters, and the size of the training dataset. We propose a systematic optimization strategy that formalizes setting choices and motivates the amount of training data required. We apply this strategy on NRSur7dq4Remnant, an existing surrogate model for the properties of the remnant of generically precessing binary black hole mergers and construct a neural network version, which we label NRSur7dq4Remnant_NN. The systematic optimization strategy results in a new surrogate model with comparable accuracy and provides insights into the meaning and role of the various network settings and hyperparameters as well as the structure of the physical process. Moreover, NRSur7dq4Remnant_NN results in evaluation speedups of up to 8 times on a single CPU and a further improvement of 2000 times when evaluated in batches on a GPU. To determine the training-set size, we propose an iterative enrichment strategy that efficiently samples the parameter space using much smaller training sets than naive sampling. NRSur7dq4Remnant_NN requires O(104) training data, so neural-network-based surrogates are ideal for speeding up models that support such large training datasets, but at the moment cannot directly be applied to numerical relativity catalogs that are O(103) in size. The optimization strategy is available through the gwbonsai package.

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Surrogate model for gravitational wave signals from nonspinning, comparable-to large-mass-ratio black hole binaries built on black hole perturbation theory waveforms calibrated to numerical relativity

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Physical Review D 2022年第10期106卷 104025-104025页

作者： Tousif Islam Scott E. Field Scott A. Hughes Gaurav Khanna Vijay Varma Matthew Giesler Mark A. Scheel Lawrence E. Kidder Harald P. Pfeiffer 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 of Theoretical Physics University of California Santa Barbara Santa Barbara California 93106 USA Department of Physics and MIT Kavli Institute Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics University of Rhode Island Kingston Rhode Island 02881 USA Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Am Mühlenberg 1 Potsdam 14476 Germany Cornell Center for Astrophysics and Planetary Science Cornell University Ithaca New York 14853 USA Theoretical Astrophysics Walter Burke Institute for Theoretical Physics California Institute of Technology Pasadena California 91125 USA

We present a reduced-order surrogate model of gravitational waveforms from nonspinning binary black hole systems with comparable to large mass-ratio configurations. This surrogate model, BHPTNRSur1dq1e4, is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT) with mass ratios varying from 2.5 to 10,000. BHPTNRSur1dq1e4 extends an earlier waveform model, EMRISur1dq1e4, by using an updated transition-to-plunge model, covering longer durations up to 30,500m1 (where m1 is the mass of the primary black hole), includes several more spherical harmonic modes up to ℓ=10, and calibrates subdominant modes to numerical relativity (NR) data. In the comparable mass-ratio regime, including mass ratios as low as 2.5, the gravitational waveforms generated through ppBHPT agree surprisingly well with those from NR after this simple calibration step. We also compare our model to recent SXS and RIT NR simulations at mass ratios ranging from 15 to 32, and find the dominant quadrupolar modes agree to better than ≈10−3. We expect our model to be useful to study intermediate-mass-ratio binary systems in current and future gravitational-wave detectors.

关键词： Gravitational waves

A fully precessing higher-mode surrogate model of effective-one-body waveforms

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arXiv 2022年

作者： Gadre, Bhooshan Pürrer, Michael Field, Scott E. Ossokine, Serguei Varma, Vijay Am Mühlenberg 1 Potsdam14476 Germany Utrecht University Princetonplein 1 Utrecht3584 CC Netherlands Department of Physics East Hall University of Rhode Island KingstonRI02881 United States URI Center for Computational Research Tyler Hall University of Rhode Island KingstonRI02881 United States Department of Mathematics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Data Science Research University of Massachusetts DartmouthMA02747 United States

We present a surrogate model of SEOBNRv4PHM, a fully precessing time-domain effective-one-body (EOB) waveform model including subdominant modes. We follow an approach similar to that used to build recent numerical relativity surrogate models. Our surrogate is 5000M in duration, covers mass-ratios up to 1:20 and dimensionless spin magnitudes up to 0.8. Validating the surrogate against an independent test set we find that the median mismatch error is less than 10−3, which is typically smaller than the modeling error of SEOBNRv4PHM itself. At high total mass, a few percent of configurations can exceed mismatches of 10−2 if they are highly precessing and exceed a mass ratio of 1:4. This surrogate is nearly two orders of magnitude faster than the underlying time-domain SEOBNRv4PHM model and can be evaluated in ∼ 50 ms. Bayesian inference analyses with SEOBNRv4PHM are typically very computationally demanding and can take from weeks to months to complete. The two-order of magnitude speedup attained by our surrogate model enables practical parameter estimation analyses with this waveform family. This is crucial because Bayesian inference allows us to recover the masses and spins of binary black hole mergers given a model of the emitted gravitational waveform along with a description of the noise. © 2022, CC BY.

关键词： Relativity

Survey of gravitational wave memory in intermediate mass ratio binaries

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arXiv 2021年

作者： Islam, Tousif Field, Scott E. Khanna, Gaurav Warburton, Niels 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 Kavli Institute for Theoretical Physics University of California Santa BarbaraCA93106 United States Department of Physics University of Rhode Island KingstonRI02881 United States School of Mathematics and Statistics University College Dublin Belfield Dublin 4 Ireland

The non-linear 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 non-linear GW memory from the m 6= 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, (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. Copyright © 2021, The Authors. All rights reserved.

关键词： Gravity waves

Evidence of Large Recoil Velocity from a Black Hole Merger Signal

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Physical Review Letters 2022年第19期128卷 191102-191102页

作者： Vijay Varma Sylvia Biscoveanu Tousif Islam Feroz H. Shaik Carl-Johan Haster Maximiliano Isi Will M. Farr Scott E. Field Salvatore Vitale Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Am Mühlenberg 1 Potsdam 14476 Germany LIGO Laboratory Massachusetts Institute of Technology 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 Department of Mathematics University of Massachusetts Dartmouth Massachusetts 02747 USA Center for Scientific Computing and Data Science Research University of Massachusetts Dartmouth Massachusetts 02747 USA Center for Computational Astrophysics Flatiron Institute New York New York 10010 USA Department of Physics and Astronomy Stony Brook University Stony Brook New York 11794 USA

The final black hole left behind after a binary black hole merger can attain a recoil velocity, or a “kick,” reaching values up to 5000 km/s. This phenomenon has important implications for gravitational wave astronomy, black hole formation scenarios, testing general relativity, and galaxy evolution. We consider the gravitational wave signal from the binary black hole merger GW200129_065458 (henceforth referred to as GW200129), which has been shown to exhibit strong evidence of orbital precession. Using numerical relativity surrogate models, we constrain the kick velocity of GW200129 to vf∼1542−1098+747 km/s or vf≳698 km/s (one-sided limit), at 90% credibility. This marks the first identification of a large kick velocity for an individual gravitational wave event. Given the kick velocity of GW200129, we estimate that there is a less than 0.48% (7.7%) probability that the remnant black hole after the merger would be retained by globular (nuclear star) clusters. Finally, we show that kick effects are not expected to cause biases in ringdown tests of general relativity for this event, although this may change in the future with improved detectors.

关键词： Gravitational waves

Surrogate modeling of gravitational waves microlensed by spherically symmetric potentials

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Physical Review D 2025年第10期111卷 104042-104042页

作者： Uddeepta Deka Gopalkrishna Prabhu Md Arif Shaikh Shasvath J. Kapadia Vijay Varma Scott E. Field International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bangalore 560089 India Inter-University Centre for Astronomy and Astrophysics Post Bag 4 Ganeshkhind Pune 411007 India Department of Physics Vivekananda Satavarshiki Mahavidyalaya (affiliated to Vidyasagar University) Manikpara 721513 West Bengal India Department of Physics and Astronomy Seoul National University Seoul 08826 South Korea Department of Mathematics Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth Dartmouth Massachusetts 02747 USA

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 F˜(t). We show this by constructing F˜(t) for two lens models, viz. point-mass lens, and singular isothermal sphere, which notably includes logarithmic divergence behavior. We find both surrogates reproduce the original lens models accurately, with mismatches ≲5×10−4 across a range of plausible microlensed binary black hole sources observed by the Einstein Telescope. This surrogate is between 5 and 103 times faster than the underlying lensing models, and can be evaluated in about 100 ms. The accuracy and efficiency attained by our surrogate models will enable practical parameter estimation analyses of microlensed GWs.

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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

Scalable multiscale-spectral GFEM with an application to composite aero-structures

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arXiv 2022年

作者： Bénézech, Jean Seelinger, Linus Bastian, Peter Butler, Richard Dodwell, Timothy Ma, Chupeng Scheichl, Robert Centre for Integrated Materials Processes and Structures University of Bath BathBA2 7AY United Kingdom Institute for Applied Mathematics Interdisciplinary Center for Scientific Computing Heidelberg University Heidelberg Germany Institute of Data Science and AI University of Exeter Exeter United Kingdom DigiLab Exeter United Kingdom Department of Mathematical Sciences University of Bath Bath United Kingdom Institute of Scientific Research Great Bay University Dongguan China

In this paper, the first large-scale application of multiscale-spectral generalized finite element methods (MS-GFEM) to composite aero-structures is presented. The crucial novelty lies in the introduction of A-harmonicity in the local approximation spaces, which in contrast to [Babuska, Lipton, Multiscale Model. Simul. 9, 2011] is enforced more efficiently via a constraint in the local eigenproblems. This significant modification leads to excellent approximation properties, which turn out to be essential to capture accurately material strains and stresses with a low dimensional approximation space, hence maximising model order reduction. The implementation of the framework in the Distributed and Unified Numerics Environment (DUNE) software package, as well as a detailed description of all components of the method are presented and exemplified on a composite laminated beam under compressive loading. The excellent parallel scalability of the method, as well as its superior performance compared to the related, previously introduced GenEO method are demonstrated on two realistic application cases, including a C-shaped wing spar with complex geometry. Further, by allowing low-cost approximate solves for closely related models or geometries this efficient, novel technology provides the basis for future applications in optimisation or uncertainty quantification on challenging problems in composite aero-structures. Copyright © 2022, The Authors. All rights reserved.

关键词： Finite element method