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Register a new userPassive Newtonian noise suppression for gravitational-wave observatories based on shaping of the local topography
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In this article we propose a new method for reducing Newtonian noise in laser-interferometric gravitational-wave detectors located on the Earths surface. We show that by excavating meter-scale recesses in the ground around the main test masses of a gravitational wave detector it is possible to reduce the coupling of Rayleigh wave driven seismic disturbances to test mass displacement. A discussion of the optimal recess shape is given and we use finite element simulations to derive the scaling of the Newtonian noise suppression with the parameters of the recess as well as the frequency of the seismic excitation. Considering an interferometer similar to an Advance LIGO configuration, our simulations indicate a frequency dependent Newtonian noise suppression factor of 2 to 4 in the relevant frequency range for a recesses of 4m depth and a width and length of 11m and 5m, respectively. Though a retrofit to existing interferometers seems not impossible, the application of our concept to future infrastructures seems to provide a better benefit/cost ratio and therefore a higher feasibility.
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We present an analysis of Brownian force noise from residual gas damping of reference test masses as a fundamental sensitivity limit in small force experiments. The resulting acceleration noise increases significantly when the distance of the test mass to the surrounding experimental apparatus is smaller than the dimension of the test mass itself. For the Advanced LIGO interferometric gravitational wave observatory, where the relevant test mass is a suspended 340 mm diameter cylindrical end mirror, the force noise power is increased by roughly a factor 40 by the presence of a similarly shaped reaction mass at a nominal separation of 5 mm. The force noise, of order 20 fNrthz for $2 times 10^{-6}$ Pa of residual H$_2$ gas, rivals quantum optical fluctuations as the dominant noise source between 10 and 30 Hz. We present here a numerical and analytical analysis for the gas damping force noise for Advanced LIGO, backed up by experimental evidence from several recent measurements. Finally, we discuss the impact of residual gas damping on the gravitational wave sensitivity and possible mitigation strategies.
Terrestrial gravity noise, also known as Newtonian noise, produced by ambient seismic and infrasound fields will pose one of the main sensitivity limitations in low-frequency, ground-based, gravitational-wave (GW) detectors. It was estimated that this noise foreground needs to be suppressed by about 3 -- 5 orders of magnitude in the frequency band 10,mHz to 1,Hz, which will be extremely challenging. In this article, we present a new approach that greatly facilitates cancellation of gravity noise in full-tensor GW detectors. The method uses optimal combinations of tensor channels and environmental sensors such as seismometers and microphones to reduce gravity noise. It makes explicit use of the direction of propagation of a GW, and can therefore either be implemented in directional searches for GWs or in observations of known sources. We show that suppression of the Newtonian-noise foreground is greatly facilitated using the extra strain channels in full-tensor GW detectors. Only a modest number of auxiliary, high-sensitivity environmental sensors are required to achieve noise suppression by a few orders of magnitude.
For ground-based gravitational wave (GW) detectors, lightning strokes in the atmosphere are sources of environmental noise. Some GW detectors are built or planned in underground facilities, and knowledge of how lightning strokes affect them is of interest. In this paper, the lightning detection system in KAGRA is introduced, and the properties of the magnetic field measured inside and outside the KAGRA tunnel are shown. One lightning-induced event in the GW channel of the KAGRA main interferometer is also shown. Finally, possible applications of lightning events for the GW experiments are discussed.
(abridged) The signal-to-noise ratio (SNR) is used in gravitational-wave observations as the basic figure of merit for detection confidence and, together with the Fisher matrix, for the amount of physical information that can be extracted from a detected signal. SNRs are usually computed from a sensitivity curve, which describes the gravitational-wave amplitude needed by a monochromatic source of given frequency to achieve a threshold SNR. For interferometric space-based detectors similar to LISA, which are sensitive to long-lived signals and have constantly changing position and orientation, exact SNRs need to be computed on a source-by-source basis. For convenience, most authors prefer to work with sky-averaged sensitivities, accepting inaccurate SNRs for individual sources and giving up control over the statistical distribution of SNRs for source populations. In this paper, we describe a straightforward end-to-end recipe to compute the non-sky-averaged sensitivity of interferometric space-based detectors of any geometry: in effect, we derive error bars for the sky-averaged sensitivity curve, which provide a stringent statistical interpretation for previously unqualified statements about sky-averaged SNRs. As a worked-out example, we consider isotropic and Galactic-disk populations of monochromatic sources, as observed with the classic LISA configuration. We confirm that the (standard) inverse-rms average sensitivity for the isotropic population remains the same whether or not the LISA orbits are included in the computation. However, detector motion tightens the distribution of sensitivities, so for 50% of sources the sensitivity is within 30% of its average. For the Galactic-disk population, the average and the distribution of the sensitivity for a moving detector turn out to be similar to the isotropic case.
We construct a Bayesian inference deep learning machine for parameter estimation of gravitational wave events of binaries of black hole coalescence. The structure of our deep Bayseian machine adopts the conditional variational autoencoder scheme by conditioning both the gravitational wave strains and the variations of amplitude spectral density of the detector noise. We show that our deep Bayesian machine is capable of yielding the posteriors compatible with the ones from the nest sampling method, and of fighting against the noise outliers. We also apply our deep Bayesian machine to the LIGO/Virgo O3 events, and find that conditioning detector noise to fight against its drifting is relevant for the events with medium signal-to-noise ratios.
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