Generic inspirals and mergers of binary black holes produce beamed emission of gravitational radiation that can lead to a gravitational recoil or kick of the final black hole. The kick velocity depends on the mass ratio and spins of the binary as well as on the dynamics of the binary configuration. Studies have focused so far on the most astrophysically relevant configuration of quasicircular inspirals, for which kicks as large as approximately 3300 km s;(-1) have been found. We present the first study of gravitational recoil in hyperbolic encounters. Contrary to quasicircular configurations, in which the beamed radiation tends to average during the inspiral, radiation from hyperbolic encounters is plunge dominated, resulting in an enhancement of preferential beaming. As a consequence, it is possible in highly relativistic scatterings to achieve kick velocities as large as 10 000 km s;(-1).
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http://dx.doi.org/10.1103/PhysRevLett.102.041101 | DOI Listing |
Sci Rep
December 2024
School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, India.
Dimensionality Reduction (DR) is an indispensable step to enhance classifier accuracy with data redundancy in hyperspectral images (HSI). This paper proposes a framework for DR that combines band selection (BS) and effective spatial features. The conventional clustering methods for BS typically face hard encounters when we have a less data items matched to the dimensionality of the accompanying feature space.
View Article and Find Full Text PDFJ Imaging
December 2024
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
Deep learning has shown significant value in automating radiological diagnostics but can be limited by a lack of generalizability to external datasets. Leveraging the geometric principles of non-Euclidean space, certain geometric deep learning approaches may offer an alternative means of improving model generalizability. This study investigates the potential advantages of hyperbolic convolutional neural networks (HCNNs) over traditional convolutional neural networks (CNNs) in neuroimaging tasks.
View Article and Find Full Text PDFJ Biopharm Stat
January 2025
College of Mathematics and System Science, Xinjiang University, Urumqi, China.
In clinical trials, unilateral or bilateral data can usually be encountered if a subject contributes one or both of paired organs. For the bilateral data, responses from two paired body parts are correlated. In this paper, we study various confidence intervals of common risk difference in stratified unilateral and bilateral data based on the Dallal's model.
View Article and Find Full Text PDFHeliyon
August 2023
Faculty of Technical Physics, Information Technology and Applied Mathematics, Lodz University of Technology, 90-924 Lodz, Poland.
The analysis in this communication addresses the unsteady MHD flow of tangent hyperbolic liquid through a vertical plate. The model on mass and heat transport is set up with Joule heating, heat generation, viscous dissipation, thermal radiation, chemical reaction and Soret-Dufour in the form of partial differential equations (PDEs). The PDEs are simplified into a dimensionless PDEs by utilizing a suitable quantities.
View Article and Find Full Text PDFISA Trans
December 2022
School of Automation, Harbin Engineering University, Harbin 150001, China.
This brief disposes the finite-time anti-unwinding trajectory tracking control problem of the autonomous underwater vehicle (AUV) encountering model uncertainties, ocean disturbances and actuator failures. Primarily, the kinematic model of translational and rotational motions is depicted by unit quaternion in lieu of classical Euler angle such that the AUV's dynamics could be globally and uniquely formulated. Subsequently, two finite-time control strategies are presented here to leave the state variables of AUV can converge to an adjustable region.
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