Principal components in active galactic nuclei variability data and the estimation of the flux contributions from different components

It has been found that the near-infrared flux variations of Seyfert galaxies satisfy relations of the form   F_i ≈α _{i j} +β _{i j} F_j   , where F_i , F_j are the fluxes in filters i and j ; and  α _{i , j} , β _{i , j}   are constants. These relations have been used to estimate the constant contributions of the non-variable underlying galaxies. The paper attempts a formal treatment of the estimation procedure, allowing for the possible presence of a third component, namely non-variable hot dust. In an analysis of a sample of 38 Seyfert galaxies, inclusion of the hot dust component improves the model fit in approximately half the cases. All derived dust temperatures are below 300 K, in the range 540–860 K or above 1300 K. A noteworthy feature is the estimation of confidence intervals for the component contributions: this is achieved by bootstrapping. It is also pointed out that the model implies that such data could be fruitfully analysed in terms of principal components.     

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.     

The SuperCOSMOS Sky Survey – III. Astrometry

In this, the third in a series of three papers concerning the SuperCOSMOS Sky Survey, we describe the astrometric properties of the data base. We describe the algorithms employed in the derivation of the astrometric parameters of the data, and demonstrate their accuracies by comparison with external data sets using the first release of data, the South Galactic Cap survey. We show that the celestial coordinates, which are tied to the International Celestial Reference Frame via the Tycho–2 reference catalogue, are accurate to better than ±0.2 arcsec at J , R ∼19,18 , rising to ±0.3 arcsec at J , R ∼22,21 , with positional-dependent systematic effects from bright to faint magnitudes at the ∼0.1-arcsec level. The proper motion measurements are shown to be accurate to typically ±10 mas yr⁻¹ at J , R ∼19,18 , rising to ±50 mas yr⁻¹ at J , R ∼22,21 , and are tied to zero using the extragalactic reference frame. We show that the zero-point errors in the proper motions are ≤1 mas yr⁻¹ for R >17 , and are no larger than ∼10 mas yr⁻¹ for R <17 mas yr⁻¹ .     

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.     

Silicon limitation on primary production and its destiny in Jiaozhou Bay, China——Ⅳ：Study on cross-bay transect from estuary to ocean

The authors analyzed the data collected in the Ecological Station Jiaozhou Bay from May 1991 to November 1994, including 12 seasonal investigations, to determine the characteristics, dynamic cycles and variation trends of the silicate in the bay. The results indicated that the rivers around Jiaozhou Bay provided abundant supply of silicate to the bay. The silicate concentration there depended on river flow variation. The horizontal variation of silicate concentration on the transect showed that the silicate concentration decreased with distance from shorelines. The vertical variation of it showed that silicate sank and deposited on the sea bottom by phytoplankton uptake and death, and zooplankton excretion. In this way, silicon would endlessly be transferred from terrestrial sources to the sea bottom. The silicon took up by phytoplankton and by other biogeochemical processes led to insufficient silicon supply for phytoplankton growth. In this paper, a 2D dynamic model of river flow versus silicate concentration was established by which silicate concentrations of 0.028–0.062 μmol/L in seawater was yielded by inputting certain seasonal unit river flows (m³/s), or in other words, the silicate supply rate; and when the unit river flow was set to zero, meaning no river input, the silicate concentrations were between 0.05–0.69 μmol/L in the bay. In terms of the silicate supply rate, Jiaozhou Bay was divided into three parts. The division shows a given river flow could generate several different silicon levels in corresponding regions, so as to the silicon-limitation levels to the phytoplankton in these regions. Another dynamic model of river flow versus primary production was set up by which the phytoplankton primary production of 5.21–15.55 (mgC/m²·d)/(m³/s) were obtained in our case at unit river flow values via silicate concentration or primary production conversion rate. Similarly, the values of primary production of 121.98–195.33 (mgC/m²·d) were achieved at zero unit river flow condition. A primary production conversion rate reflects the sensitivity to silicon depletion so as to different phytoplankton primary production and silicon requirements by different phytoplankton assemblages in different marine areas. In addition, the authors differentiated two equations (Eqs. 1 and 2) in the models to obtain the river flow variation that determines the silicate concentration variation, and in turn, the variation of primary production. These results proved further that nutrient silicon is a limiting factor for phytoplankton growth. This study was funded by NSFC (No. 40036010), and the Director's Fund of the Beihai Sea Monitoring Center, the State Oceanic Administration.