Wide field small aperture telescopes (WFSATs) are commonly used for fast sky survey. Telescope arrays composed by several WFSATs are capable to scan sky several times per night. Huge amount of data would be obtained by them and these data need to be processed immediately. In this paper, we propose ARGUS (Astronomical taRGets detection framework for Unified telescopes) for real-time transit detection. The ARGUS uses a deep learning based astronomical detection algorithm implemented in embedded devices in each WFSATs to detect astronomical targets. The position and probability of a detection being an astronomical targets will be sent to a trained ensemble learning algorithm to output information of celestial sources. After matching these sources with star catalog, ARGUS will directly output type and positions of transient candidates. We use simulated data to test the performance of ARGUS and find that ARGUS can increase the performance of WFSATs in transient detection tasks robustly.
The point spread function (PSF) is the impulse response of an optical system. PSFs of an adaptive optics system have very strong variations both in temporal and spatial domain and a stable PSF reconstruction algorithm is required to provide prior information for scientific data processing. In this paper, we report our recent progress in developing a framework for PSF modelling with non-parametric model. The non-parametric PSF model uses compressive wavefront sensing method to build PSFs from wavefront measurements. Then a PSF-NET is used to learn map between PSFs estimated from wavefront sensing and PSFs in different field of views in a ground layer adaptive optics system. We use simulated data to test performance of the non--parametric PSF model and the results show its effectiveness.
From ground-based extremely large telescopes to small telescope arrays used for time domain astronomy, point spread function plays an important role both for scientific data post-processing and instrument performance estimation. In this paper, we propose a new method which can restore astronomical images and obtain the point spread function of the whole optical system at the same time. Our method uses simulated high resolution astronomical images and real observed blurred images to train a deep neural network (Cycle-GAN). The Cycle- GAN contains a pair of generative adversarial neural networks and each generative adversarial neural network contains a generator and a discriminator. After training, one generator (PSF-Gen) can learn the point spread function and the other generator (Dec-Gen) can learn the deconvolution kernel. We test our method with real observation data from solar telescope and small aperture telescopes. We find that the Dec-Gen can give promising restoration results for solar images and can reduce the PSF spatial variation for images obtained by smaller telescopes. Besides, we also find that the PSF-Gen can provide a non-parametric PSF model for short exposure images, which would then be used as prior model for PSF reconstruction algorithms in adaptive optics systems.
MEMS deformable mirrors (DM) have many merits of low drive voltage, high response speed, small power consumption, low cost and small size. Its surface shape and displacement versus applied voltage are significant factors of MEMS DM. Phase-shifting interferometer (PSI) has many advantages such as non-contact, quickness and high precision. A phase-only liquid crystal spatial light modulator (LC-SLM), as a linear phase-shifter in PSI, is linear calibrated for its phase-shift characteristics. The PSI is set up to measure the static characteristic of MEMS DM. Five-step phase-shifting method is used to calculate the phase distribution from interference fringes, and Global phase unwrapping algorithm to solve the holes, noise and breakpoint of interfere images. Compared to the measurement results using Zygo instrument, these two experimental results are very close. The experiment results show, this measuring system is very reliable, convenient and cheap. Moreover, this test system need not stitch some fringe images to get the whole surface shape of the mirror like the Zygo instrument.
The Antarctic is an ideal place for optical and infrared astronomy observations. Chinese scientists are planning
to build a 2.5m telescope in Dome A. The telescope will be built in a tower about 15 meters high to avoid the
ground layer atmospheric turbulence. The Ground layer Adaptive Optics system (GLAO) will also be suggested
to be installed to further reduce the seeing. The GLAO system with one laser guide star, one deformable mirror
and one wide field Shack-Hartmann wavefront sensor is designed and simulated. The Strehl ratio has increased
2 to 3 times in visible and infrared band in 20 arc min field of view.
A modal control optimization method for adaptive optics on the tempo-spatial domain is presented. The spatial modes of
the adaptive optics system can be obtained by the singular value decomposition of the response function matrix of the
adaptive optics system. The number of correction modes is determined dynamically by the root mean square estimation
of the residual aberration after the correction with different number of modes. A Smith compensator is designed to
reduce the time delay effect on the closed-loop system. The modal optimization method is experimentally verified by
compensating phase distortion produced by artificial atmospheric turbulence in laboratory. Experimental results show
that the correction capability of the adaptive optics system can be greatly improved in comparison to that of the generic
modal gain integrator approach with the fixed number of correction modes. The modal control optimization method is an
attractive and practical alternative to adaptive optics control.