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Terrestrial gravity fluctuations are a target of scientific studies in a variety of fields within geophysics and fundamental-physics experiments involving gravity such as the observation of gravitational waves. In geophysics, these fluctuations are typically considered as signal that carries information about processes such as fault ruptures and atmospheric density perturbations. In fundamental-physics experiments, it appears as environmental noise, which needs to be avoided or mitigated. This article reviews the current state-of-the-art of modeling high-frequency terrestrial gravity fluctuations and of gravity-noise mitigation strategies. It hereby focuses on frequencies above about 50 mHz, which allows us to simplify models of atmospheric gravity perturbations (beyond Brunt–Väisälä regime) and it guarantees as well that gravitational forces on elastic media can be treated as perturbation. Extensive studies have been carried out over the past two decades to model contributions from seismic and atmospheric fields especially by the gravitational-wave community. While terrestrial gravity fluctuations above 50 mHz have not been observed conclusively yet, sensitivity of instruments for geophysical observations and of gravitational-wave detectors is improving, and we can expect first observations in the coming years. The next challenges include the design of gravity-noise mitigation systems to be implemented in current gravitational-wave detectors, and further improvement of models for future gravitational-wave detectors where terrestrial gravity noise will play a more important role. Also, many aspects of the recent proposition to use a new generation of gravity sensors to improve real-time earthquake early-warning systems still require detailed analyses.

In continuously monitored systems the standard quantum limit is given by the trade-off between shot noise and back-action noise. In gravitational-wave detectors, such as Advanced LIGO, both contributions can be simultaneously squeezed in a broad frequency band by injecting a spectrum of squeezed vacuum states with a frequency-dependent squeeze angle. This approach requires setting up an additional long baseline, low-loss filter cavity in a vacuum system at the detector’s site. Here, we show that the need for such a filter cavity can be eliminated, by exploiting Einstein–Podolsky–Rosen (EPR)-entangled signals and idler beams. By harnessing their mutual quantum correlations and the difference in the way each beam propagates in the interferometer, we can engineer the input signal beam to have the appropriate frequency-dependent conditional squeezing once the out-going idler beam is detected. Our proposal is appropriate for all future gravitational-wave detectors for achieving sensitivities beyond the standard quantum limit. Quantum metrology can enhance gravitational-wave detection through the use of squeezed states. A new proposal now suggests that with EPR entanglement one can do even better, reaching sensitivities beyond the standard quantum limit.

Transient gravity changes are expected to occur at all distances during an earthquake rupture, even before the arrival of seismic waves. Here we report on the search of such a prompt gravity signal in data recorded by a superconducting gravimeter and broadband seismometers during the 2011 Mw 9.0 Tohoku-Oki earthquake. During the earthquake rupture, a signal exceeding the background noise is observed with a statistical significance higher than 99% and an amplitude of a fraction of μGal, consistent in sign and order of magnitude with theoretical predictions from a first-order model. While prompt gravity signal detection with state-of-the-art gravimeters and seismometers is challenged by background seismic noise, its robust detection with gravity gradiometers under development could open new directions in earthquake seismology, and overcome fundamental limitations of current earthquake early-warning systems imposed by the propagation speed of seismic waves. Earthquakes have been theorised to produce gravity signals that may arrive before seismic waves, but until now they had not been detected. Montagner et al . have detected prompt gravity signals from the 2011 Tohoku-Oki earthquake thus allowing an early warning of earthquakes before seismic wave arrival.

The suspended test masses of gravitational-wave (GW) detectors require precise alignment to be able to operate the detector stably and with high sensitivity. This includes the continuous counter-acting of seismic disturbances, which, below a few Hertz, are not sufficiently reduced by the seismic isolation system. The residual angular motion of suspended test masses is further suppressed by the Angular Sensing and Control (ASC) system. However, in doing so, the angular motion can be enhanced by the ASC at higher frequencies where the seismic isolation system is very effective. This has led to sensitivity limitations between about 10 Hz and 25 Hz of the LIGO detectors in past observation runs. The observed ASC noise was larger than simple models predict, which means that more accurate detector models and new simulation tools are required. In this article, we present Lightsaber, a new time-domain simulator of the ASC in LIGO. Lightsaber is a nonlinear simulation of the optomechanical system consisting of the high-power cavity laser beam and the last two stages of suspension in LIGO including the ASC. The main noise inputs are power fluctuations of the laser beam at the input of the arm cavities, read-out noise of sensors used for the ASC, displacement noise from the suspension platforms, and noise introduced by the suspension damping loops. While the plant simulation uses local degrees of freedom of individual suspension systems, the control is applied on a global angular basis, which requires a conversion between the local and global bases for sensing and actuation. Some of the studies that can be done with this simulation concern mis-centering of the beam-spot (BS) position on the test masses, the role of laser power fluctuations for angular dynamics, and the role of the various nonlinear dynamics. The next important step following this work will be a detailed comparison between Lightsaber results and data from the control channels of the LIGO detectors.

In automatic milking systems (AMSs), the detection of clinical mastitis (CM) and the subsequent separation of abnormal milk should be reliably performed by commercial AMSs. Therefore, the objectives of this cross-sectional study were (1) to determine the sensitivity (SN) and specificity (SP) of CM detection of AMS by the four most common manufacturers in Bavarian dairy farms, and (2) to identify routinely collected cow data (AMS and monthly test day data of the regional Dairy Herd Improvement Association (DHIA)) that could improve the SN and SP of clinical mastitis detection. Bavarian dairy farms with AMS from the manufacturers DeLaval, GEA Farm Technologies, Lely, and Lemmer-Fullwood were recruited with the aim of sampling at least 40 cows with clinical mastitis per AMS manufacturer in addition to clinically healthy ones. During a single farm visit, cow-level milking information was first electronically extracted from each AMS and then all lactating cows examined for their udder health status in the barn. Clinical mastitis was defined as at least the presence of visibly abnormal milk. In addition, available DHIA test results from the previous six months were collected. None of the manufacturers provided a definition for clinical mastitis (i.e., visually abnormal milk), therefore, the SN and SP of AMS warning lists for udder health were assessed for each manufacturer individually, based on the clinical evaluation results. Generalized linear mixed models (GLMMs) with herd as random effect were used to determine the potential influence of routinely recorded parameters on SN and SP. A total of 7411 cows on 114 farms were assessed; of these, 7096 cows could be matched to AMS data and were included in the analysis. The prevalence of clinical mastitis was 3.4% (239 cows). When considering the 95% confidence interval (95% CI), all but one manufacturer achieved the minimum SN limit of >80%: DeLaval (SN: 61.4% (95% CI: 49.0%–72.8%)), GEA (75.9% (62.4%–86.5%)), Lely (78.2% (67.4%–86.8%)), and Lemmer-Fullwood (67.6% (50.2%–82.0%)). However, none of the evaluated AMSs achieved the minimum SP limit of 99%: DeLaval (SP: 89.3% (95% CI: 87.7%–90.7%)), GEA (79.2% (77.1%–81.2%)), Lely (86.2% (84.6%–87.7%)), and Lemmer-Fullwood (92.2% (90.8%–93.5%)). All AMS manufacturers’ robots showed an association of SP with cow classification based on somatic cell count (SCC) measurement from the last two DHIA test results: cows that were above the threshold of 100,000 cells/mL for subclinical mastitis on both test days had lower chances of being classified as healthy by the AMS compared to cows that were below the threshold. In conclusion, the detection of clinical mastitis cases was satisfactory across AMS manufacturers. However, the low SP will lead to unnecessarily discarded milk and increased workload to assess potentially false-positive mastitis cases. Based on the results of our study, farmers must evaluate all available data (test day data, AMS data, and daily assessment of their cows in the barn) to make decisions about individual cows and to ultimately ensure animal welfare, food quality, and the economic viability of their farm.

As herd sizes have increased in the last decades, computerized monitoring solutions, which provide fast, objective and accurate evaluations of the herd status, gain more and more importance. This study analyzes the feasibility of a Time-of-Flight-camera-based system for gathering body traits in dairy cows for use under cow barn conditions. Recording, determination of body condition score on a 5 point scale by visual and manual inspection, and measuring the backfat thickness with ultrasound took place from July 2011 to May 2012 at the dairy research farm Karkendamm of the Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel (Germany) and between August 2010 and July 2012 at the Institute for Agricultural Engineering and Animal Husbandry of Bavarian State Research Center for Agriculture in Grub (Germany). The two breeds Holstein Friesian cows (Karkendamm) and Fleckvieh (Grub) were considered in this study. Software for recording, image sorting and evaluation, determining the body parts needed, and extracting traits from the images was written and assembled to an automated system. Sorting the images and finding ischeal tuberosities, base of the tail, and dishes of the rump, backbone, and hips had error rates of 0.2%, 1.5%, 0.1%, and 2.6%, respectively. 13 traits were extracted and compared to backfat thickness and body condition score as well as between breeds. All traits depend significantly on the animal and showed very large effect sizes. Coefficients of determination restricted to individual animals were reaching up to 0.89. The precision in measuring the traits and gathering backfat thickness was comparable. Results indicated that the application of Time-Of-Flight in determination of body traits is feasible.

With increasing herd sizes, camera based monitoring solutions rise in importance. 3D cameras, for example Time-Of-Flight (TOF) cameras, measure depth information. These additional information (3D data) could be beneficial for monitoring in dairy production. In previous studies regarding TOF technology, only standing cows were recorded to avoid motion artifacts. Therefore, necessary conditions for a TOF camera application in dairy cows are examined in this study. For this purpose, two cow models with plaster and fur surface, respectively, were recorded at four controlled velocities to quantify the effects of movement, fur color, and fur. Comparison criteria concerning image usability, pixel-wise deviation, and precision in coordinate determination were defined. Fur and fur color showed large effects ( η 2 =0.235 and η 2 =0.472, respectively), which became even more considerable when the models were moving. The velocity of recorded animals must therefore be controlled when using TOF cameras. As another main result, body parts which lie in the middle of the cow model’s back can be determined neglecting the effect of velocity or fur. With this in mind, further studies may obtain sound results using TOF technology in dairy production.

In the dense regions of star clusters, close encounters with black holes (BHs) can occur giving rise to a new class of gravitational-wave (GW) signals. Binary–single encounters between three BHs are expected to dominate the rate of signals from unbound systems in the frequency band of terrestrial GW detectors. The encounter can describe a quasi-hyperbolic trajectory, which was the focus of a recent study. In some cases, the encounter can take a more complex form including one or two BH mergers as a result of the encounter, repeating cycles of close proximity between the BHs, and the exchange of a BH that is part of the binary. The variety of types of encounters leads to a variety of GW signals emerging from these encounters. Using the ARWV N-body code, we performed 42 numerical simulations, to explore various outcomes of binary–single interaction, and we characterize the diverse GW signatures produced during these encounters. Additionally, we evaluated the detectability of these GW signals by injecting them into the simulated noise of the Einstein Telescope and exploring different methods to detect the signals. Our findings shed light on the complexities of these interactions and their potential implications for GW astronomy.

The Einstein Telescope (ET) is a proposed third-generation gravitational-wave (GW) underground observatory. It will have greatly increased sensitivity compared to current GW detectors, and it is designed to extend the observation band down to a few Hz. At these frequencies, a major limitation of the ET sensitivity is predicted to be due to gravitational fluctuations produced by the environment, most importantly by the seismic field, which give rise to the so-called Newtonian noise (NN). Accurate models of ET NN are crucial to assess the compatibility of an ET candidate site with the ET sensitivity target also considering a possible reduction in NN by noise cancellation. With NN models becoming increasingly complex as they include details of geology and topography, it is crucial to have tools to make robust assessments of their accuracy. For this purpose, we derive a lower bound on seismic NN spectra, which is weakly dependent on geology and properties of the seismic field. As a first application, we use the lower limit to compare it with NN estimates recently calculated for the Sardinia and Euregio Meuse–Rhine (EMR) candidate sites. We find the utility of the method, which shows an inconsistency with the predictions for the EMR site, which indicates that ET NN models require further improvement.

A new era of lunar exploration has begun bringing immense opportunities for science as well. It has been proposed to deploy a new generation of observatories on the lunar surface for deep studies of our Universe. This includes radio antennas, which would be protected on the far side of the Moon from terrestrial radio interference, and gravitational-wave (GW) detectors, which would profit from the extremely low level of seismic disturbances on the Moon. In recent years, novel concepts have been proposed for lunar GW detectors based on long-baseline laser interferometry or on compact sensors measuring the lunar surface vibrations caused by GWs. In this article, we review the concepts and science opportunities for such instruments on the Moon. In addition to promising breakthrough discoveries in astrophysics and cosmology, lunar GW detectors would also be formidable probes of the lunar internal structure and improve our understanding of the lunar geophysical environment.