A super-high angular resolution principle for coded-mask X-ray imaging beyond the diffraction limit of a single pinhole

High angular resolution X-ray imaging is always useful in astrophysics and solar physics. In principle, it can be performed by using coded-mask imaging with a very long mask-detector distance. Previously, the diffraction-interference effect was thought to degrade coded-mask imaging performance dramatically at the low energy end with its very long mask-detector distance. The diffraction-interference effect is described with numerical calculations, and the diffraction-interference cross correlation reconstruction method （DICC） is developed in order to overcome the imaging performance degradation. Based on the DICC, a super-high angular resolution principle （SHARP） for coded-mask X-ray imaging is proposed. The feasibility of coded mask imaging beyond the diffraction limit of a single pinhole is demonstrated with simulations. With the specification that the mask element size is 50 × 50 μm^2 and the mask-detector distance is 50 m, the achieved angular resolution is 0.32arcsec above about 10keV and 0.36arcsec at 1.24keV （λ = 1 nm）, where diffraction cannot be neglected. The on-axis source location accuracy is better than 0.02 arcsec. Potential applications for solar observations and wide-field X-ray monitors are also briefly discussed.

Development of the super high angular resolution principle for X-ray imaging

Development of the Super High Angular Resolution Principle （SHARP） for coded-mask X-ray imaging is presented. We prove that SHARP can be considered as a generalized coded mask imaging method with a coding pattern comprised of diffraction-interference fringes in the mask pattern. The angular resolution of SHARP can be improved by detecting the fringes more precisely than the mask＇s element size, i.e. by using a detector with a pixel size smaller than the mask＇s element size. The proposed mission SHARP-X for solar X-ray observations is also briefly discussed.

Optical fiber positioning based on four-quadrant detector with Gaussian fitting method

With the development of large-scale spectral surveys, fiber positioning technology has been developing rapidly. Because of the performance advantages of a four-quadrant（4Q） detector, a fiber positioning and real-time monitoring system based on the 4Q detector is proposed. The detection accuracy of this system is directly determined by the precision of the center of the spot. A Gaussian fitting algorithm based on the 4Q detector is studied and applied in the fiber positioning process to improve the calculated accuracy of the spot center. The relationship between the center position of the incident spot and the detector output signal is deduced. An experimental platform is built to complete the simulated experiment. Then we use the Gaussian fitting method to process experimental data, compare the fitting value with the theoretical one and calculate the corresponding error.

Real-time space object tracklet extraction from telescope survey images with machine learning

In this study,a novel approach based on the U-Net deep neural network for image segmentation is leveraged for real-time extraction of tracklets from optical acquisitions.As in all machine learning(ML)applications,a series of steps is required for a working pipeline:dataset creation,preprocessing,training,testing,and post-processing to refine the trained network output.Online websites usually lack ready-to-use datasets;thus,an in-house application artificially generates 360 labeled images.Particularly,this software tool produces synthetic night-sky shots of transiting objects over a specified location and the corresponding labels:dual-tone pictures with black backgrounds and white tracklets.Second,both images and labels are downscaled in resolution and normalized to accelerate the training phase.To assess the network performance,a set of both synthetic and real images was inputted.After the preprocessing phase,real images were fine-tuned for vignette reduction and background brightness uniformity.Additionally,they are down-converted to eight bits.Once the network outputs labels,post-processing identifies the centroid right ascension and declination of the object.The average processing time per real image is less than 1.2 s;bright tracklets are easily detected with a mean centroid angular error of 0.25 deg in 75%of test cases with a 2 deg field-of-view telescope.These results prove that an ML-based method can be considered a valid choice when dealing with trail reconstruction,leading to acceptable accuracy for a fast image processing pipeline.