The electron pressure in the outer atmosphere of ε Eri (K2 V)

Observations of ε Eri (K2 V) have been made with the Space Telescope Imaging Spectrograph on the Hubble Space Telescope . The spectra obtained show a number of emission lines which can be used to determine, or place limits on, the electron density and pressure. Values of the electron pressure are required in order to make quantitative models of the transition region and inner corona from absolute line fluxes, and to constrain semi-empirical models of the chromosphere. Using line flux ratios in Si  iii and O  iv a mean electron pressure of P _e= N _e T _e=4.8×10¹⁵ cm⁻³ K is derived. This value is compatible with the lower and upper limits to P _e found from flux ratios in C  iii , O  v and Fe  xii . Some inconsistencies which may be because of small uncertainties in the atomic data used are discussed.     

抗差估计的初值选择

对L1 范估计的平差值和LS估计的平差值作为抗差估计的初值进行了比较 ,指出在 ρ函数是严凸函数时 ,二者无明显的区别。因为LS估计简单且易于计算 ,因此建议用LS估计的平差值作为抗差估计的初值。     

Particle-mesh simulations of the Lyα forest

We have analysed short-exposure high-resolution images obtained on the William Herschel Telescope (WHT), Big Telescope Azimuthal (BTA) and CanadaFranceHawaii Telescope (CFHT) in order to investigate mechanical vibrations that are capable of reducing the high angular resolution allowed by speckle-interferometric methods. After filtering the photon noise we have computed power spectra of the image centres of gravity. In these spectra we have found vibration features that differ from one telescope to another and that vary in particular with the zenith angle. In the case of the WHT we discuss these results in terms of possible causes and in terms of degradation of the transfer function. We present some means to improve the data.     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.     

青藏块体东北缘地壳水平运动状态

应用青藏块体东北缘1999～2003年多期GPS观测资料，计算了不同时段GPS点水平运动速率。通过分析发现：甘青块体可分为东部块体和西部块体，东、西部块体的运动状态存在明显的差异；受2001年11月14日昆仑Ms8．1地震的影响，震后地壳运动状态发生了明显的改变。