Precessing Binary Black Holes as Better Dark Sirens

Gravitational waves (GWs) from binary black hole mergers provide unique opportunities for cosmological inference such as standard sirens. However, the accurate determination of the luminosity distance of the event is limited by the correlation between the distance and the angle between the binary's orbital angular momentum and the observer's line of sight. In the letter, we investigate the effect of precession on the distance estimation of binary black hole events for the third-generation (3G) GW detectors. We find that the precession can enhance the precision of distance inference by one order of magnitude compared to the scenario where precession is absent. The constraint on the host galaxies can be improved due to the improved distance measurement, therefore the Hubble constant can be measured with higher precision and accuracy. These findings underscore the noteworthy impact of precession on the precision of distance estimation for 3G ground-based GW detectors, which can serve as highly accurate probes of the Universe.Comment: 6 pages, 6 figure

The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals (EMRIs). This endeavor presents a significant challenge due to the complex and lengthy EMRI signals, further compounded by their inherently faint nature. In this letter, we introduce a 2-layer Convolutional Neural Network (CNN) approach to detect EMRI signals for space-borne detectors, achieving a true positive rate (TPR) of 96.9 % at a 1 % false positive rate (FPR) for signal-to-noise ratio (SNR) from 50 to 100. Especially, the key intrinsic parameters of EMRIs such as mass and spin of the supermassive black hole (SMBH) and the initial eccentricity of the orbit can be inferred directly by employing a VGG network. The mass and spin of the SMBH can be determined at 99 % and 92 % respectively. This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter estimation. Our model also has a low dependency on the accuracy of the waveform model. This study underscores the potential of deep learning methods in EMRI data analysis, enabling the rapid detection of EMRI signals and efficient parameter estimation .Comment: 6 pages, 5 figure

Detecting extreme-mass-ratio inspirals for space-borne detectors with deep learning

One of the primary objectives for space-borne gravitational wave detectors is the detection of extreme-mass-ratio inspirals (EMRIs). This undertaking poses a substantial challenge because of the complex and long EMRI signals, further complicated by their inherently faint signal. In this research, we introduce a 2-layer Convolutional Neural Network (CNN) approach to detect EMRI signals for space-borne detectors. Our method employs the Q-transform for data preprocessing, effectively preserving EMRI signal characteristics while minimizing data size. By harnessing the robust capabilities of CNNs, we can reliably distinguish EMRI signals from noise, particularly when the signal-to-noise~(SNR) ratio reaches 50, a benchmark considered a ``golden'' EMRI. At the meantime, we incorporate time-delay interferometry (TDI) to ensure practical utility. We assess our model's performance using a 0.5-year dataset, achieving a true positive rate~(TPR) of 94.2\% at a 1\% false positive rate~(FPR) across various signal-to-noise ratio form 50-100, with 91\% TPR and 1\% FPR at an SNR of 50. This study underscores the promise of incorporating deep learning methods to advance EMRI data analysis, potentially leading to rapid EMRI signal detection.Comment: 12 pages, 8 figures, 2 table

Experimental Study of the Effect of Splitter Blades on the Performance Characteristics of Francis Turbines

With the improvement in energy structures, the safe and stable operation of hydropower units is becoming the most important issue for electric grids. To expand the stable operating range of a 200 m head Francis turbine, splitter blades were designed to increase the cavitation ability and lower the high-amplitude pressure fluctuations. Experimental studies were carried out to analyze the effect of the splitter blades on the turbine performance characteristics (efficiency, cavitation, and pressure fluctuation), and the results obtained were compared with those for normal blades. The results reveal that the splitter blades can increase the efficiency by approximately 2%, and they can reduce the pressure fluctuation in the vaneless space, under high-head operating conditions. The flow observation results reveal that the splitter blades can restrain the cavitation at the suction side of the blades, and thereby expand the stable operating range. Analyses of the pressure fluctuation show that the splitter blades can change the blade passing frequency and sharply lower its amplitude. This study may provide a reference for all Francis turbine designs, which makes it significant for the stable and effective operation of hydropower units

Tracing astrophysical black hole seeds and primordial black holes with LISA-Taiji network

In this work, we discuss the improvement that the joint network of LISA and Taiji could provide on exploring two kinds of black hole formation mechanisms. For astrophysical origin, we consider light seed and heavy seed scenarios, and generate populations accordingly. We find that the joint network has the potential to observe growing light seeds in the range 15 20 and detection rates of LISA and the joint network. The joint network expands LISA's horizon towards lower mass end, where the event rate is high, so we have better chance observing primordial black holes with the joint network. We also estimate the parameters using Fisher matrices of LISA and the joint network, and find that the joint network significantly improves the estimation

miR-143-null Is against Diet-Induced Obesity by Promoting BAT Thermogenesis and Inhibiting WAT Adipogenesis

Excessive energy intake is the main cause of obesity, and stimulation of brown adipose tissue (BAT) thermogenesis has emerged as an attractive tool for anti-obesity. Although miR-143 has been reported to promote white adipocyte differentiation, its role in BAT remains unclear. In our study, we found that during HFD-induced obesity, the expression of miR-143 in BAT was significantly reduced, and the expression of miR-143 in WAT first increased and then decreased. Knockout (KO) of miR-143 with CRISPR/Cas9 did not affect the energy metabolism of normal diet fed mice and brown adipocyte differentiation but inhibited the differentiation of white adipocytes. Importantly, during high fat diet-induced obesity, miR-143KO significantly reduced body weight, and improved energy expenditure, insulin sensitivity, and glucose tolerance. Further exploration showed that miR-143KO reduced the weight of adipose tissue, promoted mitochondrial number and functions, induced thermogenesis and lipolysis of BAT, increased lipolysis, and inhibited lipogenesis of white adipose tissue (WAT). Our study considerably improves our collective understanding of the function of miR-143 in adipose tissue and its potential significance in anti-obesity and provides a new avenue for the management of obesity through the inhibition of miR-143 in BAT and WAT