Wide field small aperture telescopes are widely used for optical transient observations. Detection and classification of astronomical targets in observed images are the most important and basic step. In this paper, we propose an astronomical targets detection and classification framework based on deep neural networks. Our framework adopts the concept of the Faster R-CNN and uses a modified Resnet-50 as backbone network and a Feature Pyramid Network to extract features from images of different astronomical targets. To increase the generalization ability of our framework, we use both simulated and real observation images to train the neural network. After training, the neural network could detect and classify astronomical targets automatically. We test the performance of our framework with simulated data and find that our framework has almost the same detection ability as that of the traditional method for bright and isolated sources and our framework has 2 times better detection ability for dim targets, albeit all celestial objects detected by the traditional method can be classified correctly. We also use our framework to process real observation data and find that our framework can improve 25% detection ability than that of the traditional method when the threshold of our framework is 0.6. Rapid discovery of transient targets is quite important and we further propose to install our framework in embedded devices such as the Nvidia Jetson Xavier to achieve real-time astronomical targets detection and classification abilities.

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BackgroundConstipation is one of the most common gastrointestinal complaints with a highly prevalent and often chronic functional gastrointestinal disorder affecting health-related quality of life. The aim of the present study was to evaluate the effects of Salecan on fecal output and small intestinal transit in normal and two models of drug-induced constipation mice.MethodsICR mice were administrated intragastrically (i.g.) by gavage with 100, 200 and 300 mg/kg body weight (BW) of Salecan while the control mice were received saline. The constipated mice were induced by two types of drugs, loperamide (5 mg/kg BW, i.g.) and clonidine (200 μg/kg BW, i.g.), after Salecan treatment while the control mice were received saline. Number, weight and water content of feces were subsequently measured. Small intestinal transit was monitored by phenol red marker meal.ResultsSalecan (300 mg/kg BW) significantly increased the number and weight of feces in normal mice. In two models of drug-induced constipation, Salecan dose-dependently restored the fecal number and fecal weight. The water content of feces was markedly affected by loperamide, but not by clonidine. Treatment with Salecan significantly raised the fecal water content in loperamide-induced constipation mice. Moreover, Salecan markedly stimulated the small intestinal transit in both loperamide- and clonidine-induced constipation model mice.ConclusionsThese results suggest that Salecan has a potential to be used as a hydrophilic laxative for constipation.

Ground based optical telescopes are seriously affected by atmospheric turbulence induced aberrations. Understanding properties of these aberrations is important both for instruments design and image restoration methods development. Because the point spread function can reflect performance of the whole optic system, it is appropriate to use the point spread function to describe atmospheric turbulence induced aberrations. Assuming point spread functions induced by the atmospheric turbulence with the same profile belong to the same manifold space, we propose a non-parametric point spread function -PSF-NET. The PSF-NET has a cycle convolutional neural network structure and is a statistical representation of the manifold space of PSFs induced by the atmospheric turbulence with the same profile. Testing the PSF-NET with simulated and real observation data, we find that a well trained PSF-NET can restore any short exposure images blurred by atmospheric turbulence with the same profile. Besides, we further use the impulse response of the PSF-NET, which can be viewed as the statistical mean PSF, to analyze interpretation properties of the PSF-NET. We find that variations of statistical mean PSFs are caused by variations of the atmospheric turbulence profile: as the difference of the atmospheric turbulence profile increases, the difference between statistical mean PSFs also increases. The PSF-NET proposed in this paper provides a new way to analyze atmospheric turbulence induced aberrations, which would be benefit to develop new observation methods for ground based optical telescopes.

Abstract:In the actual analysis of grey clustering evaluation, the length of a grey clustering interval was partially longer, which is determined by the grey clustering evaluation method based on the center-point triangular whitenization weight function. In response to problems like this, this paper proposes a new grey evaluation method on the basis of the reformative triangular whitenization weight function. Motivated by ideas of the end-point and the center-point triangular whitenization weight functions, we construct a new compact-center-point triangular whitenization weight function. Then, several aspects of the three kinds of triangular whitenization weight functions are compared, such as the crossing properties of grey cluster, clustering coefficients, rules for grey clustering interval, rules for choosing end-points and clustering performance. In the following, this paper proposes an example about the evaluation of a basin initial water rights allocation scheme, which analyzes the three methods to further verify that the new grey clustering evaluation method is feasible and effective. The results indicate that the compact-center-point triangular whitenization weight function precedes the end-point triangular whitenization weight function and the center-point triangular whitenization weight function soundly.

The point spread function reflects the state of an optical telescope and it is important for data post-processing methods design. For wide field small aperture telescopes, the point spread function is hard to model, because it is affected by many different effects and has strong temporal and spatial variations. In this paper, we propose to use the denoising autoencoder, a type of deep neural network, to model the point spread function of wide field small aperture telescopes. The denoising autoencoder is a pure data based point spread function modelling method, which uses calibration data from real observations or numerical simulated results as point spread function templates. According to real observation conditions, different levels of random noise or aberrations are added to point spread function templates, making them as realizations of the point spread function, i.e., simulated star images. Then we train the denoising autoencoder with realizations and templates of the point spread function. After training, the denoising autoencoder learns the manifold space of the point spread function and can map any star images obtained by wide field small aperture telescopes directly to its point spread function, which could be used to design data post-processing or optical system alignment methods.

The Durham Adaptive Optics Simulation Platform (DASP) is a Monte-Carlo modelling tool used for the simulation of astronomical and solar adaptive optics systems. In recent years, this tool has been used to predict the expected performance of the forthcoming extremely large telescope adaptive optics systems, and has seen the addition of several modules with new features, including Fresnel optics propagation and extended object wavefront sensing. Here, we provide an overview of the features of DASP and the situations in which it can be used. Additionally, the user tools for configuration and control are described.The Durham adaptive optics (AO) simulation platform (DASP) has been under development since the early 1990s. Its current framework was established in 2006 to meet the challenges of modelling the forthcoming extremely large telescopes, with primary mirror diameters of over 20 m. Since 2006, DASP has been regularly developed to improve computational performance, increase simulation fidelity, and expand the number of features that can be modelled. It uses a modular design, allowing new developments and algorithms to be added whilst maintaining compatibility. DASP is developed primarily in Python and C, and uses pthreads and MPI for parallelization enabling modelling of the largest proposed telescopes on reasonable timescales.

Wide field small aperture telescopes are working horses for fast sky surveying. Transient discovery is one of their main tasks. Classification of candidate transient images between real sources and artifacts with high accuracy is an important step for transient discovery. In this paper, we propose two transient classification methods based on neural networks. The first method uses the convolutional neural network without pooling layers to classify transient images with low sampling rate. The second method assumes transient images as one dimensional signals and is based on recurrent neural networks with long short term memory and leaky ReLu activation function in each detection layer. Testing with real observation data, we find that although these two methods can both achieve more than 94% classification accuracy, they have different classification properties for different targets. Based on this result, we propose to use the ensemble learning method to further increase the classification accuracy to more than 97%.