星周尘埃壳层的消光值

本文首次提出了从观测得到的具有星周尘埃壳层的恒星的能谱分布求取星周尘埃云的消光,并由此可通过改正星周消光改正后的星际消光法求得恒星距离的方法。     

Trend removal in spatially correlated datasets

Typically, datasets originated from mining exploration sites, industrially polluted and hazardous waste sites are correlated spatially over the region under investigation. Ordinary kriging (OK) is a well-established geostatistical tool used for predicting variables, such as precious metal contents, biomass, species counts, and environmental pollutants at unsampled spatial locations based on data collected from the neighboring sampled locations at these sites. One of the assumptions required to perform OK is that the mean of the characteristic of concern is constant for the entire region under consideration (e.g., there is no spatial trend present in the contaminant distribution across the site). This assumption may be violated by dalasets obtained from environmental applications. The occurrence of spatial trend in a dataset collected from a polluted site is an indication of the presence of two or more statistical populations (strata) with significantly different mean concentrations. Use of OK in these situations can result in inaccurate kriging estimates with higher SDs which, in turn, can lead to incorrect decisions regarding all subsequent environmental monitoring and remediation activities. A univariate and a multivariate approach have been described to identify spatial trend that may be present at the site. The trend then is removed by subtracting the respective means from the corresponding populations. The results of OK before and after trend removal are being compared. Using a real dataset, it is shown that standard deviations (SDs) of the kriging estimates obtained after trend removal are uniformly smaller than the corresponding SDs of the estimates obtained without the trend removal.     

Peculiar oscillations in the microwave emission of Jupiter during the impact of K fragment of comet Shoemaker-Levy 9

In this article we report the peculiar oscillations in the intensity of microwave (4.15 GHz) emission seen during the impact of K fragment of comet Shoemaker-Levy 9 on July 19, 1994. The oscillations begin at 10h 13m 25s UT suddenly with a frequency of ~0.3 Hz and gradually the frequency of these oscillations increases to ~ 1 Hz. The oscillations are not due to local atmosphere or the radio interference from signals of geostationary satellite. They are intrinsic to the microwave emission from Jupiter during the impact of K fragment. Peak-to-peak amplitude of the oscillations is about 34% of the total microwave emission from Jupiter. If we assume that only 50% of the microwave emission is non-thermal and only the non-thermal emission suffered oscillations, then the oscillations are about 68% of the non-thermal emission from Jupiter. The observations also indicate that there are three continuum enhancements during this event and periodic oscillations almost all through. The third enhancement was the largest and during this enhancement there were some additional aperiodic variations. The aperiodic variations were of the order of few minutes and were possibly generated by the gravity waves. The periodic oscillations could be synchrotron emission modulated by the plasma oscillation in the outer magnetosphere. kg]Key words     

束－等离子体不稳定性直接放大电磁波及其对太阳Ⅲ型爆的解释

弱磁化相对论电子束注入等离子体时，由非共振波粒相互作用激发的束－等离子体不稳定性可以直接放大电磁波，计算结果表明：在偏离共振条件的区域，电磁波仍可在较宽的频率范围被放大，并在每个共振峰下形成平台结构。随着谐波数的增高，增长率峰值逐渐变小，峰宽也变窄。本文还分析了电磁波的增长率随背景参数ω＿（ｐｅ）／Ω＿ｅ及高能电子的入射方向和辐射方向的变化规律，在典型的日冕条件下，此类不稳定性所放大的电磁波的增长率大小、带宽、方向性、偏振及谐波等性质，可以用来解释太阳Ⅲ型射电爆发现象，本文的研究亦可用来解释其他天体等离子体辐射。     

Comptonization in the vicinity of the black hole horizon

Using a Monte Carlo method, we derive spectra arising from Comptonization taking place close to a Kerr black hole. We consider a model consisting of a hot thermal corona Comptonizing seed photons emitted by a cold accretion disc. We find that general relativistic effects are crucial for the emerging spectra in models, which involve significant contribution of radiation produced in the black hole ergosphere. As a result of this contribution, spectra of hard X-ray emission produced in the vicinity of a rapidly rotating black hole strongly depend on the inclination of the line of sight, with larger inclinations corresponding to harder spectra. Remarkably, such anisotropy could be responsible for properties of the X-ray spectra of Seyfert galaxies, which appear to be intrinsically harder in type 2 objects than in type 1, as reported recently.     

Light Curve Analysis of Early-Type Overcontact Systems

A method is presented to include irradiation effects in the modeling of over-contact binaries. The Roche potential is numerically modified in a way to account for mechanical effects of the mutual irradiation of the two binary components. The efficiency of radiative interaction is parametrized by means of the fraction of radiative over gravitational forces for each component of the binary. The modified Roche geometry is implemented as binary model in an eclipse light curve solution code, which is based on the general Wilson–Devinney scheme. As an example the method is applied to the early-type over-contact system V606 Cen.     

Determination of polarization of vacuum-ultraviolet radiation by fluorescence and probe-beam techniques

Polarization analysis of vacuum ultraviolet (VUV) radiation, especially the analysis of elliptical polarization is of considerable scientific interest in many areas of physics including solar physics. Polarization measurements in the VUV region are much less developed than those in visible and infrared spectral regions. It is due to lack of efficient phase shifting elements and small reflection coefficients of polarizers for VUV radiation. We propose two techniques, to determine the arbitrary polarization of VUV radiation with photon energies in the range 10 – 100 eV, which seem to be more efficient and precise than the known techniques, especially to study the polarization of atomic or molecular spectral lines.     

一种基于MEA BP的太阳辐射反演算法

基于光电原理的日照计即将在全国推广应用,以光照度观测数据为主反演太阳辐射数据可以有效弥补太阳辐射观测站数量不足的现状。针对现有的太阳辐射反演方法的不足,提出一种融合主成分分析(PCA)、思维进化算法(MEA)和BP神经网络的复合模型,利用太阳光照度、太阳高度角、温度和湿度观测分钟数据反演太阳辐照度。首先,以晴空指数为依据,基于概率神经网络(PNN)分类法,将天气类型分为晴、云、阴3类,分类准确率达到96.6948%。再利用PCA降维后的4个影响因子,对3类天气分别采用BP、GA-BP和MEA-BP法反演太阳辐照度,与标准辐射表的实测数据对比。结果表明:晴、云、阴的MEA-BP模型的决定系数最高达到0.9958,与单一BP模型相比,RMSE分别降低了49%、32.45%和10.64%;相比于GA-BP模型误差,MAPE最高减少了42.54%。本文所提出的MEA-BP复合模型的泛化能力得到了有效提高。     

夏季西藏4个站点大气向下长波辐射观测分析

对2011—2016年部分夏季时段分别在西藏那曲、拉萨、林芝和阿里观测的大气向下长波辐射（L_↓）进行分析,结果显示:L_↓具有明显的日变化,最大值出现在北京时间15:00前后,而最低值出现在凌晨至10:00,日平均值林芝最高（368 W·m-2）,其次是拉萨（319 W·m-2）、阿里（305 W·m-2）和那曲（299 W·m-2）。晴天L_↓ ?ngstr?m（1915）的经验公式最适合林芝,而Konzelmann（1994）的公式则适合那曲、拉萨和阿里;随着人工观测总云量的增加,L_↓增强趋势明显,满云（云量7~10成）情形4个站点云增强效应均从20 W·m-2上升至50 W·m-2以上,低云量对L_↓的增强效应明显高于总云量。云份额数（C_F）上升所对应天顶方向平均云底高度下降,但云增强效应上升。在晴天（C_F为-5%~5%、平均云底高度大于4 km）时,云增强效应仅为5 W·m-2左右（林芝接近20 W·m-2）,但当C_F为90%以上（云底高度小于3.5 km）时,云增强效应则上升到60 W·m-2（林芝接近50 W·m-2）。固定云底高度,C_F与L_↓云增强效应呈显著相关（r2为0.91~0.97）,远高于云底高度与L_↓云增强效应的相关（r2为0.32~0.58）。     

Kernel Principal Component Analysis for Efficient,Differentiable Parameterization of Multipoint Geostatistics

This paper describes a novel approach for creating an efficient, general, and differentiable parameterization of large-scale non-Gaussian, non-stationary random fields (represented by multipoint geostatistics) that is capable of reproducing complex geological structures such as channels. Such parameterizations are appropriate for use with gradient-based algorithms applied to, for example, history-matching or uncertainty propagation. It is known that the standard Karhunen–Loeve (K–L) expansion, also called linear principal component analysis or PCA, can be used as a differentiable parameterization of input random fields defining the geological model. The standard K–L model is, however, limited in two respects. It requires an eigen-decomposition of the covariance matrix of the random field, which is prohibitively expensive for large models. In addition, it preserves only the two-point statistics of a random field, which is insufficient for reproducing complex structures. In this work, kernel PCA is applied to address the limitations associated with the standard K–L expansion. Although widely used in machine learning applications, it does not appear to have found any application for geological model parameterization. With kernel PCA, an eigen-decomposition of a small matrix called the kernel matrix is performed instead of the full covariance matrix. The method is much more efficient than the standard K–L procedure. Through use of higher order polynomial kernels, which implicitly define a high-dimensionality feature space, kernel PCA further enables the preservation of high-order statistics of the random field, instead of just two-point statistics as in the K–L method. The kernel PCA eigen-decomposition proceeds using a set of realizations created by geostatistical simulation (honoring two-point or multipoint statistics) rather than the analytical covariance function. We demonstrate that kernel PCA is capable of generating differentiable parameterizations that reproduce the essential features of complex geological structures represented by multipoint geostatistics. The kernel PCA representation is then applied to history match a water flooding problem. This example demonstrates that kernel PCA can be used with gradient-based history matching to provide models that match production history while maintaining multipoint geostatistics consistent with the underlying training image.