Application of the space-based optical interferometer towards measuring cosmological distances of quasars.

Measuring the quasar distance through joint analysis of spectroastrometry (SA) and reverberation mapping (RM) observations is a new method for driving the development of cosmology. In this paper, we carry out detailed simulation and analysis to study the effect of four basic observational parameters (baseline length, exposure time, equivalent diameter and spectral resolution) on the data quality of differential phase curves (DPCs), furthermore on the accuracy of distance measurement. In our simulation, we adopt an axis symmetrical disc model of broad line region (BLR) to generate differential phase signals. We find that the differential phases and their Poisson errors could be amplified by extending the baseline, while the influence of OPD errors can be reduced during fitting the BLR model. Longer exposure time or larger equivalent diameter helps reduce the absolute Poisson error. Therefore, the relative error of DPCs could be reduce by increasing any of the above three parameters, then the the accuracy of distance measurement could be improved. In contrast, the uncertainty of $D_{\rm{A}}$ could be improved with higher spectral resolution, although the relative error of DPCs would be amplified. We show how the uncertainty of distance measurement varies with the relative error of DPCs. It is found that the relative error of DPCs $<$ 20$\%$ is a limit for accurate distance measurement. As any of the basic observational parameters become larger, the relative error of DPCs have a lower limit (roughly 5$\%$) and the uncertainty of distance measurement can be better than 2$\%$.

Phase Retrieval Based on Deep Learning in Grating Interferometer

Grating interferometry is a promising technique to obtain differential phase contrast images with illumination source of low intrinsic transverse coherence. However, retrieving the phase contrast image from the differential phase contrast image is difficult due to the accumulated noise and artifacts from the differential phase contrast image (DPCI) reconstruction. In this paper, we implemented a deep learning-based phase retrieval method to suppress these artifacts. Conventional deep learning based denoising requires noisy-clean image pair, but it is not feasible to obtain sufficient number of clean images for grating interferometry. In this paper, we apply a recently developed neural network called Noise2Noise (N2N) that uses noise-noise image pairs for training. We obtained many differential phase contrast images through combination of phase stepping images, and these were used as noise input/target pairs for N2N training. The application of the N2N network to simulated and measured DPCI showed that the phase contrast images were retrieved with strongly suppressed phase retrieval artifacts. These results can be used in grating interferometer applications which uses phase stepping method.

A proposed model for Mantle upwelling in the Syrian coastal: نموذج مقترح لتقبب المعطف في الساحل السوري

The aim of the research is to develop a conception of the proposed model for Mantle upwelling (diapering) in the coastal region, as the results of this research showed the occurrence of Mantle upwelling regression under the coastal region during the Pliocene period, and this led to the occurrence of basaltic deposits in the Syrian coast during the Pliocene, where we note the center of the vaulting was under Qardaha and Safita, and the Mantle upwelling reached a depth of 35 km within the continental crust, where basalt rocks were formed as a result of partial melting of the upper mantle, and it is upwelled with low melting and differential degrees. Basalt rocks in the initial differential phase of the original basaltic silage.

Isotropic quantitative differential phase contrast imaging techniques: a review

Optical phase shifts generated by the spatial variation of refractive index and thickness inside the transparent samples can be determined by intensity measurements through quantitative phase contrast imaging. In this review, we focus on isotropic quantitative differential phase-contrast microscopy(qDPC), which is a non-interferometric quantitative phase imaging technique and belongs to the class of deterministic phase retrieval from intensity. The qDPC is based on the principle of a weak object transfer function together with the first-order Born approximation in a partially coherent illumination system and wide-field detection, which offers multiple advantages. We review basic principles, imaging systems, and demonstrate examples of differential phase contrast (DPC) imaging for biomedical applications. In addition to the previous work, we present the latest results for isotropic phase contrast enhancements using a deep learning approach. We implemented a supervised learning approach with the U-Net model to reduce the number of measurements required for multi-axis measurements associated with the isotropic phase transfer function. We show that a well-designed and trained neural network provide a fast and efficient way to predict quantitative phase maps for live cells, which can help in determining morphological parameters. The prospects of deep learning in quantitative phase microscopy, particularly for isotropic quantitative phase estimation, are discussed.

Method for differential phase delay resolution of phase referencing VLBI technique and its experimental verification

The phase referencing Very Long Baseline Interferometry (VLBI) technique is a newly developed tool to measure the angular position of a deep space exploration probe in the plane-of-the-sky. Through alternating observations between the probe and a nearby reference radio source, their accurate relative angular separation can be obtained from the radio images generated by this technique. To meet the requirements of the current orbit determination software, differential delay should be firstly derived from those radio images. A method to resolve the differential phase delay from the phase referencing VLBI technique is proposed in this paper, and as well the mathematical model for differential phase ambiguity resolution is established. This method is verified with practical measurement data from the Chang’E-3 mission. The differential phase delay between the Chang’E-3 lander and rover was derived from the phase referencing VLBI measurements, and was then imported into the Shanghai astronomical observatory Orbit Determination Program (SODP) to calculate the position of the rover relative to the lander on the lunar surface. The results are consistent with those acquired directly from radio images, indicating that the differential phase ambiguity has been correctly resolved. The proposed method can be used to promote applications of the phase referencing VLBI technique in future lunar or deep space explorations, and more accurate orbit determination becomes promising.