Coherent quantum effects through dispersive bosonic media

The coherent evolution of two atomic qubits mediated by a set of bosonic field modes is investigated. By assuming a specific encoding of the quantum states in the internal levels of the two atoms we show that entangling quantum gates can be realised, with high fidelity, even when a large number of mediating modes is involved. The effect of losses and imperfections on the gates' operation is also considered in detail.Comment: 7 pages, 10 figure

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

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

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

Variable stars detection in the field of open cluster NGC 188

This work presents the charge-coupled device (CCD) photometric survey of the old open cluster NGC 188. Time-series V-band photometric observations were conducted for ten nights in January 2017 using the Nanshan One-meter Wide-field Telescope (NOWT) to search for variable stars in the field of the cluster field. A total of 25 variable stars, including one new variable star, were detected in the target field. Among the detected variables, 16 are cluster member stars, and the others are identified as field stars. The periods, radial velocities, effective temperatures, and classifications of the detected variables are discussed in this work. Most of the stars' effective temperatures are between 4200 K and 6600 K, indicating their spectral types are G or K. The newly discovered variable is probably a W UMa system. In this study, a known cluster variable star (V21 = V0769 Cep) is classified as an EA-type variable star based on the presence of an 0.5 magnitude eclipse in its light curve

Distributed coherent manipulation of qutrits by virtual excitation processes

We propose a scheme for the deterministic coherent manipulation of two atomic qutrits, trapped in separate cavities coupled through a short optical fibre or optical resonator. We study such a system in the regime of dispersive atom-field interactions, where the dynamics of atoms, cavities and fibre operates through virtual population of both the atomic excited states and photonic states in the cavities and fibre. We show that the resulting effective dynamics allows for the creation of robust qutrit entanglement, and thoroughly investigate the influence of imperfections and dissipation, due to atomic spontaneous emission and photon leakage, on the entanglement of the two qutrits state.Comment: 15 pages, 4 figure

Assessment of the key aroma compounds in rose-based products

AbstractIn this study, headspace solid phase microextraction–gas chromatography-mass spectrometry and GC-olfactometry were used to analyze the key aroma compounds in three types of rose-based products, including low-temperature extracts (LTEs), high-temperature extracts (HTEs), and rose drinks (RDs). In combination with the Guadagni theory, it was confirmed that the key aroma components of LTE were β-phenyl ethyl alcohol, citronellol, geraniol, and eugenol. The main aroma compounds in HTE were β-phenyl ethyl alcohol, citronellol, geraniol, eugenol, linalool, and rose oxide. The four key aroma compounds in RDs were β-phenyl ethyl alcohol, eugenol, geraniol, and linalool

Water-Soluble Flexible Organic Frameworks That Include and Deliver Proteins.

Four water-soluble hydrazone-based three-dimensional (3D) flexible organic frameworks FOF-1-4 have been synthesized from a semirigid tetracationic tetraaldehyde and four flexible dihydrazides. 1H NMR spectroscopy indicated the quantitative formation of FOF-1-4 in D2O, while dynamic light scattering experiments revealed that, depending on the concentration, these porous frameworks display hydrodynamic diameters ranging from 50 to 120 nm. The porosity of the frameworks is confirmed by ethanol vapor adsorption experiments of the solid samples as well as the high loading capacity for a 2.3 nm porphyrin guest in water. The new water-soluble frameworks exhibit low cytotoxicity and form inherent pores with diameters of 5.3 or 6.7 nm, allowing rapid inclusion of proteins such as bovine serum albumin and green and orange fluorescent proteins, and efficient delivery of the proteins into normal and cancer cells. Flow cytometric analysis reveals percentages of the delivered cells up to 99.8%