Distance estimate and progenitor characteristics of SN 2005cs in M51

We present the excitation energies and lifetimes for the  3 s 3 p 3 d  ⁴ F  _7/2,9/2  levels for five aluminium-like ions of the iron group. Apart from the wavelengths, this also includes the transition probabilities and branching ratios for the electric-dipole allowed (E1) and forbidden (M1, E2, M2) lines into the energetically lower lying levels of the  3 s ²3 d , 3 s 3 p ²  and  3 s 3 p 3 d   configurations. Applying systematically enlarged multiconfiguration Dirac–Fock (MCDF) wavefunctions, here the effects of relativity and the electron–electron correlation are treated within the same (computational) model.     

Short time-scale variability in the Faint Sky Variability Survey

We present the V -band variability analysis of the point sources in the Faint Sky Variability Survey on time-scales from 24 min to tens of days. We find that about one per cent of the point sources down to   V = 24  are variables. We discuss the variability-detection probabilities for each field depending on field sampling, amplitude and time-scale of the variability. The combination of colour and variability information allows us to explore the fraction of variable sources for different spectral types. We find that about 50 per cent of the variables show variability time-scales shorter than 6 h. The total number of variables is dominated by main-sequence sources. The distribution of variables with spectral type is fairly constant along the main sequence, with 1 per cent of the sources being variable, except at the blue end of the main sequence, between spectral types F0 and F5, where the fraction of variable sources increases to about 2 per cent. For bluer sources, above the main sequence, this percentage increases to about 3.5. We find that the combination of the sampling and the number of observations allows us to determine the variability time-scales and amplitudes for a maximum of 40 per cent of the variables found. About a third of the total number of short time-scale variables found in the survey were not detected in either B or/and I band. These show a similar variability time-scale distribution to that found for the variables detected in all three bands.     

中性大气掩星标准反演技术

结合JPL和GFZ的中性大气标准反演算法，发展了上海天文台中性大气掩星标准反演算法流程．对CHAMP观测资料进行中性大气反演，并将反演结果与ECMWF(the European Center for Medium Range Weather Forecasting)的预报大气数据进行比较．作为讨论，指出将来的发展方向．     

基于历史TLE的空间目标轨道预报误差演化规律研究

北美防空司令部(North American Aerospace Defense Command, NORAD)发布的双行根数(Two Line Element, TLE)是广大航天工作者最常用的轨道根数,与其对应的轨道模型是SGP4/SDP4 (Simplified General Perturbation Version 4/Simplified Deep-space Perturbation Version 4)解析模型.由于TLE中并没有包含相应的轨道精度信息,编目轨道的应用范围受到很大的限制.基于Space-Track网站发布的历史TLE数据和配套的SGP4/SDP4动力学模型,采用定轨标预报的方法统计并生成了大量目标轨道的预报误差,通过对预报轨道的时间区间划分给出了每个目标的预报误差随预报时间变化的拟合系数,并进一步对不同类型轨道预报误差的演化规律和特征进行了分类讨论,给出了4种轨道类型目标的轨道预报误差随时间演化的平均解析模型,为拓展双行根数的应用提供有价值的参考.     

大面积硅微条探测器在轨数据压缩算法的设计与实现

硅微条探测器具有位置分辨高、响应快、低噪声、低功耗等优点,广泛应用在各大加速器试验中,测量粒子径迹.新世纪以来,逐渐应用于空间探测领域.计划中的"悟空"2号暗物质粒子探测卫星的硅微条探测器将至数十万计,将产生海量的原始数据.如何实现探测器快速实时的数据压缩,是其需要解决的一大难题.立足于面向空间应用的硅微条探测器在轨实时压缩算法,算法采用FPGA (Field-programmable Gate Array)搭建流水线结构的方式实现,在提高系统集成度、节省逻辑资源的同时,批量数据处理时最高可将数据压缩率提升至38.4 M通道/s.算法结构具有通用性,设计思想和方案将为"悟空"2号的径迹探测器的研制提供参考.     

Research on the Methods of Performance Monitoring and Evaluation for Active Hydrogen Maserstwo

Active hydrogen maser is the main frequency standard for establishing and maintaining the time scale. It has the characteristics of high short-term stability and low phase noise. At present, it plays an increasingly important role in the international atomic time (TAI) and various local time scales. Firstly, in combination with the internal state parameters of an active hydrogen maser, the correlation between the internal state parameters and the comparison data of a hydrogen maser is analyzed, and a method for the performance monitoring of a hydrogen maser is proposed. Secondly, according to the characteristics of hydrogen maser performance, a method for evaluating the performance of a hydrogen maser is given. The hydrogen maser performance includes mainly two aspects, namely the frequency stability and predictability. The performances of two types of active hydrogen masers (CH1-75 and MHM-2010) are evaluated by this method. The correlation analysis of the atomic clock state parameters and comparison data shows that the state parameter monitoring can effectively predict the variation of the hydrogen maser performance. The evaluation results of atomic clock frequency stability and predictability show that the atomic clock with a higher medium-and-long term frequency stability has a better predictability. There are two methods for the predictability evaluation, one is based on the data published by BIPM (Bureau International des Poids et Measures), and another one is based on the quadratic model. Both methods are consistent with the weights published by BIPM. Therefore, the two methods can be used as a quantitative method to evaluate the predictability of a hydrogen maser.     

Study of Star/Galaxy Classification Based on the XGBoost Algorithm

Machine learning has achieved great success in many areas today. The lifting algorithm has a strong ability to adapt to various scenarios with a high accuracy, and has played a great role in many fields. But in astronomy, the application of lifting algorithms is still rare. In response to the low classification accuracy of the dark star/galaxy source set in the Sloan Digital Sky Survey (SDSS), a new research result of machine learning, eXtreme Gradient Boosting (XGBoost), has been introduced. The complete photometric data set is obtained from the SDSS-DR7, and divided into a bright source set and a dark source set according to the star magnitude. Firstly, the ten-fold cross-validation method is used for the bright source set and the dark source set respectively, and the XGBoost algorithm is used to establish the star/galaxy classification model. Then, the grid search and other methods are used to adjust the XGBoost parameters. Finally, based on the galaxy classification accuracy and other indicators, the classification results are analyzed, by comparing with the models of function tree (FT), Adaptive boosting (Adaboost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Stacked Denoising AutoEncoders (SDAE), and Deep Belief Nets (DBN). The experimental results show that, the XGBoost improves the classification accuracy of galaxies in the dark source classification by nearly 10% as compared to the function tree algorithm, and improves the classification accuracy of sources with the darkest magnitudes in the dark source set by nearly 5% as compared to the function tree algorithm. Compared with other traditional machine learning algorithms and deep neural networks, the XGBoost also has different degrees of improvement.