Literature DB >> 29694109

Prospects for Detecting Gravitational Waves at 5 Hz with Ground-Based Detectors.

Hang Yu1, Denis Martynov1, Salvatore Vitale1, Matthew Evans1, David Shoemaker1, Bryan Barr2, Giles Hammond2, Stefan Hild2, James Hough2, Sabina Huttner2, Sheila Rowan2, Borja Sorazu2, Ludovico Carbone3, Andreas Freise3, Conor Mow-Lowry3, Katherine L Dooley4, Paul Fulda5, Hartmut Grote6, Daniel Sigg7.   

Abstract

We propose an upgrade to Advanced LIGO (aLIGO), named LIGO-LF, that focuses on improving the sensitivity in the 5-30 Hz low-frequency band, and we explore the upgrade's astrophysical applications. We present a comprehensive study of the detector's technical noises and show that with technologies currently under development, such as interferometrically sensed seismometers and balanced-homodyne readout, LIGO-LF can reach the fundamental limits set by quantum and thermal noises down to 5 Hz. These technologies are also directly applicable to the future generation of detectors. We go on to consider this upgrade's implications for the astrophysical output of an aLIGO-like detector. A single LIGO-LF can detect mergers of stellar-mass black holes (BHs) out to a redshift of z≃6 and would be sensitive to intermediate-mass black holes up to 2000  M_{⊙}. The detection rate of merging BHs will increase by a factor of 18 compared to aLIGO. Additionally, for a given source the chirp mass and total mass can be constrained 2 times better than aLIGO and the effective spin 3-5 times better than aLIGO. Furthermore, LIGO-LF enables the localization of coalescing binary neutron stars with an uncertainty solid angle 10 times smaller than that of aLIGO at 30 Hz and 4 times smaller when the entire signal is used. LIGO-LF also significantly enhances the probability of detecting other astrophysical phenomena including the tidal excitation of neutron star r modes and the gravitational memory effects.

Year:  2018        PMID: 29694109     DOI: 10.1103/PhysRevLett.120.141102

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  1 in total

1.  Nonlinear Noise Cleaning in Gravitational-Wave Detectors With Convolutional Neural Networks.

Authors:  Hang Yu; Rana X Adhikari
Journal:  Front Artif Intell       Date:  2022-03-17
  1 in total

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