The Experts below are selected from a list of 19509 Experts worldwide ranked by ideXlab platform
Moutarde H. - One of the best experts on this subject based on the ideXlab platform.
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Sparsity Based Recovery of Galactic Binaries Gravitational Waves
'American Physical Society (APS)', 2020Co-Authors: Blelly A., Bobin J., Moutarde H.Abstract:The detection of galactic binaries as sources of gravitational waves promises an unprecedented wealth of information about these systems, but also raises several challenges in signal processing. In particular the large number of expected sources and the risk of misidentification call for the development of robust methods. We describe here an original non-parametric reconstruction of the imprint of galactic binaries in measurements affected by instrumental noise typical of the space-based gravitational wave observatory LISA. We assess the impact of various approaches based on sparse signal modelling and focus on adaptive Structured Block sparsity. We carefully show that a sparse representation gives a reliable access to the physical content of the interferometric measurement. In particular we check the successful fast extraction of the gravitational wave signal on a simple yet realistic example involving verification galactic binaries recently proposed in LISA data challenges.Comment: 21 pages, 16 figures. Code available at https://github.com/GW-IRFU/gw-irf
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Sparsity-based recovery of Galactic-binary gravitational waves
'American Physical Society (APS)', 2020Co-Authors: Blelly A., Bobin J., Moutarde H.Abstract:International audienceThe detection of Galactic binaries as sources of gravitational waves promises an unprecedented wealth of information about these systems but also raises several challenges in signal processing. In particular the large number of expected sources and the risk of misdetection call for the development of robust methods. We describe here an original nonparametric recovery of the imprint of Galactic binaries in measurements affected by instrumental noise typical of the space-based gravitational wave observatory LISA. This method, based on a denoising procedure, aims at separating from noise the sum of all signals coming from Galactic binaries. We assess the impact of various approaches based on sparse signal modeling and focus on adaptive Structured Block sparsity. We carefully show that a sparse representation of the interferometric measurement gives a reliable access to the total signal coming from Galactic binaries. In particular we check the successful fast recovery of the gravitational wave signal on a simple yet realistic example involving verification Galactic binaries recently proposed in LISA data challenges
Van Dooren Paul - One of the best experts on this subject based on the ideXlab platform.
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Structured backward error analysis of linearized Structured polynomial eigenvalue problems
'American Mathematical Society (AMS)', 2019Co-Authors: Martínez Dopico, Froilán César, Perez Javier, Van Dooren PaulAbstract:We start by introducing a new class of Structured matrix polynomials, namely, the class of M-A-Structured matrix polynomials, to provide a common framework for many classes of Structured matrix polynomials that are important in applications: the classes of (skew-)symmetric, (anti-) palindromic, and alternating matrix polynomials. Then, we introduce the families of M-A-Structured strong Block minimal bases pencils and of M-A-Structured Block Kronecker pencils, which are particular examples of Block minimal bases pencils recently introduced by Dopico, Lawrence, Perez and Van Dooren, and show that any M-A-Structured odd-degree matrix polynomial can be strongly linearized via an M-A-Structured Block Kronecker pencil. Finally, for the classes of (skew-)symmetric, (anti-)palindromic, and alternating odd-degree matrix polynomials, the M-A-Structured framework allows us to perform a global and Structured backward stability analysis of complete Structured polynomial eigenproblems, regular or singular, solved by applying to a M-A-Structured Block Kronecker pencil a structurally backward stable algorithm that computes its complete eigenstructure, like the palindromic-QR algorithm or the Structured versions of the staircase algorithm. This analysis allows us to identify those M-A-Structured Block Kronecker pencils that yield a computed complete eigenstructure which is the exact one of a slightly perturbed Structured matrix polynomial. These pencils include (modulo permutations) the well-known Block-tridiagonal and Block-anti-tridiagonal structure-preserving linearizations. Our analysis incorporates structure to the recent (unStructured) backward error analysis performed for Block Kronecker linearizations by Dopico, Lawrence, Perez and Van Dooren, and share with it its key features, namely, it is a rigorous analysis valid for finite perturbations, i.e., it is not a first order analysis, it provides precise bounds, and it is valid simultaneously for a large class of structure-pr
Paul Van Dooren - One of the best experts on this subject based on the ideXlab platform.
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Structured backward error analysis of linearized Structured polynomial eigenvalue problems
arXiv: Numerical Analysis, 2016Co-Authors: Froilan M Dopico, Javier Perez, Paul Van DoorenAbstract:We introduce a new class of Structured matrix polynomials, namely, the class of M_A-Structured matrix polynomials, to provide a common framework for many classes of Structured matrix polynomials that are important in applications: the classes of (skew-)symmetric, (anti-)palindromic, and alternating matrix polynomials. Then, we introduce the families of M_A-Structured strong Block minimal bases pencils and of M_A-Structured Block Kronecker pencils,, and show that any M_A-Structured odd-degree matrix polynomial can be strongly linearized via an M_A-Structured Block Kronecker pencil. Finally, for the classes of (skew-)symmetric, (anti-)palindromic, and alternating odd-degree matrix polynomials, the M_A-Structured framework allows us to perform a global and Structured backward stability analysis of complete Structured polynomial eigenproblems, regular or singular, solved by applying to an M_A-Structured Block Kronecker pencil a structurally backward stable algorithm that computes its complete eigenstructure, like the palindromic-QR algorithm or the Structured versions of the staircase algorithm. This analysis allows us to identify those M_A-Structured Block Kronecker pencils that yield a computed complete eigenstructure which is the exact one of a slightly perturbed Structured matrix polynomial.These pencils include (modulo permutations) the well-known Block-tridiagonal and Block-antitridiagonal structure-preserving linearizations. Our analysis incorporates structure to the recent (unStructured) backward error analysis performed for Block Kronecker linearizations by Dopico, Lawrence, P\'erez and Van Dooren, and share with it its key features, namely, it is a rigorous analysis valid for finite perturbations, i.e., it is not a first order analysis, it provides precise bounds, and it is valid simultaneously for a large class of structure-preserving strong linearizations.
Blelly A. - One of the best experts on this subject based on the ideXlab platform.
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Sparsity Based Recovery of Galactic Binaries Gravitational Waves
'American Physical Society (APS)', 2020Co-Authors: Blelly A., Bobin J., Moutarde H.Abstract:The detection of galactic binaries as sources of gravitational waves promises an unprecedented wealth of information about these systems, but also raises several challenges in signal processing. In particular the large number of expected sources and the risk of misidentification call for the development of robust methods. We describe here an original non-parametric reconstruction of the imprint of galactic binaries in measurements affected by instrumental noise typical of the space-based gravitational wave observatory LISA. We assess the impact of various approaches based on sparse signal modelling and focus on adaptive Structured Block sparsity. We carefully show that a sparse representation gives a reliable access to the physical content of the interferometric measurement. In particular we check the successful fast extraction of the gravitational wave signal on a simple yet realistic example involving verification galactic binaries recently proposed in LISA data challenges.Comment: 21 pages, 16 figures. Code available at https://github.com/GW-IRFU/gw-irf
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Sparsity-based recovery of Galactic-binary gravitational waves
'American Physical Society (APS)', 2020Co-Authors: Blelly A., Bobin J., Moutarde H.Abstract:International audienceThe detection of Galactic binaries as sources of gravitational waves promises an unprecedented wealth of information about these systems but also raises several challenges in signal processing. In particular the large number of expected sources and the risk of misdetection call for the development of robust methods. We describe here an original nonparametric recovery of the imprint of Galactic binaries in measurements affected by instrumental noise typical of the space-based gravitational wave observatory LISA. This method, based on a denoising procedure, aims at separating from noise the sum of all signals coming from Galactic binaries. We assess the impact of various approaches based on sparse signal modeling and focus on adaptive Structured Block sparsity. We carefully show that a sparse representation of the interferometric measurement gives a reliable access to the total signal coming from Galactic binaries. In particular we check the successful fast recovery of the gravitational wave signal on a simple yet realistic example involving verification Galactic binaries recently proposed in LISA data challenges
Martínez Dopico, Froilán César - One of the best experts on this subject based on the ideXlab platform.
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Structured backward error analysis of linearized Structured polynomial eigenvalue problems
'American Mathematical Society (AMS)', 2019Co-Authors: Martínez Dopico, Froilán César, Perez Javier, Van Dooren PaulAbstract:We start by introducing a new class of Structured matrix polynomials, namely, the class of M-A-Structured matrix polynomials, to provide a common framework for many classes of Structured matrix polynomials that are important in applications: the classes of (skew-)symmetric, (anti-) palindromic, and alternating matrix polynomials. Then, we introduce the families of M-A-Structured strong Block minimal bases pencils and of M-A-Structured Block Kronecker pencils, which are particular examples of Block minimal bases pencils recently introduced by Dopico, Lawrence, Perez and Van Dooren, and show that any M-A-Structured odd-degree matrix polynomial can be strongly linearized via an M-A-Structured Block Kronecker pencil. Finally, for the classes of (skew-)symmetric, (anti-)palindromic, and alternating odd-degree matrix polynomials, the M-A-Structured framework allows us to perform a global and Structured backward stability analysis of complete Structured polynomial eigenproblems, regular or singular, solved by applying to a M-A-Structured Block Kronecker pencil a structurally backward stable algorithm that computes its complete eigenstructure, like the palindromic-QR algorithm or the Structured versions of the staircase algorithm. This analysis allows us to identify those M-A-Structured Block Kronecker pencils that yield a computed complete eigenstructure which is the exact one of a slightly perturbed Structured matrix polynomial. These pencils include (modulo permutations) the well-known Block-tridiagonal and Block-anti-tridiagonal structure-preserving linearizations. Our analysis incorporates structure to the recent (unStructured) backward error analysis performed for Block Kronecker linearizations by Dopico, Lawrence, Perez and Van Dooren, and share with it its key features, namely, it is a rigorous analysis valid for finite perturbations, i.e., it is not a first order analysis, it provides precise bounds, and it is valid simultaneously for a large class of structure-pr
