In the era of multi-messenger astronomy, gamma-ray bursts (GRBs) with known redshifts, especially high-redshift GRBs, are a powerful tool for studying the structure and evolution of the early Universe. We review the background, the history, and the application of measurement methods of GRB redshifts in astronomy. Based on different observation targets, two measurement methods are mainly introduced. One is on GRB afterglow, the other is on GRB host galaxy. There are various processing methods belonging to measurement methods based on afterglow, including spectral measurement method of afterglow and afterglow spectral energy distribution fitting method with improved methods. There are also numerous measurement methods based on host galaxy, such as spectral measurement method of host galaxy, template matching method of host galaxy, some automatic spectroscopic redshift measurement methods, and machine learning methods. We subsequently introduce the principles, effects, and performance of these methods. We enumerate several detection and measurement instruments, which have been used in observation. The characteristics, advantages, disadvantages, and applicability of the GRB redshift measurement methods are summarized and analyzed. Furthermore, we provide a data set of 611 GRBs with measured redshift. The data set has been collected since 1997. Analysis and statistics are presented based on this data set. We summarize the characteristics of GRBs such as location, time, and accuracy. Finally, we introduce Space-based multi-band astronomical Variable Objects Monitor (SVOM) mission dedicated to searching high redshift GRBs. We also introduce the application prospect of various redshift measurement methods in SVOM mission.
It is one of the basic tasks to realize the automatic classification of celestial spectrum. This paper presents a method of star classification based on two-dimensional spectral data. The data used in the experiment are two-dimensional spectral data of LAMOST DR6 from the National Astronomical Observatory. There are about 2000 two-dimensional spectral data in total to use to classify F, G and G, K stars spectra. By observing a large number of two-dimensional data of stars, different region data are selected as input data according to the stars. Then the data are normalized and put into the full connection neural network for training. The whole neural network consists of one input layer, nine hidden layers and one output layer. Each hidden layer is activated by rule function, and the output layer is activated by softmax function. Experiments show that the accuracy of classification is 80% by classifying the two-dimensional spectra of F, G and G, K. It shows that the two-dimensional characteristics of the spectrum can effectively classify the spectrum.
Sensor node energy is the key to the long life cycle of a sensor network. This paper considers the correlation of sensor nodes in continuous time on-chip data transfer, and compares the derivative-based prediction, polynomial regression, back propagation and convolutional neural networks prediction algorithms for data transfer and energy consumption minimization. Experimental results show that convolutional neural networks are the optimal control solution.
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