天文选址的夜间云量处理方法

云量是影响天文台址质量最重要的因素之一,对夜间云量的检测和处理尤为重要.采用地面云量相机对全天云量进行监测,所拍摄的图像需要有效的方法进行处理以量化云量.夜间云量图像受月光的影响严重,因此将夜间的云量图像分为有月夜和无月夜两类进行处理.针对无月夜情况,给出了夜间云量的处理过程.对图像中的亮星进行定位和测光,确定星等差.以晴夜图像中亮星的星等差为参照,将星等差低于阈值条件的亮星概率作为晴夜的概率标准.选取了3类图像对该方法进行测试并确定云量,分析了阈值条件对结果的影响.最后,讨论了该方法的适用范围和不确定性.     

吉林天文观测基地光学观测环境及相关研究进展

地基光学天文望远镜是人类探索与研究宇宙的重要手段, 对已有地基光学台址的光学观测环境进行监测分析, 可以为后期设备针对性改造以及观测者调整观测策略提供参考依据, 对提升地基光学设备的观测效能具有重要的意义. 吉林天文观测基地(简称``基地'')隶属于中国科学院国家天文台长春人造卫星观测站, 位于吉林省吉林市大绥河镇小绥河村南沟约5 km处(东经126.3^\circ, 北纬43.8^\circ, 海拔高度313m). 基地大气视宁度均值范围约为1.3$''$--1.4$''$、天顶附近V波段的天光背景亮度为20.64magcdotarcsec^-2、年晴夜数最高可达270余天, 具有良好的天文观测条件. 吉林天文观测基地于2016年投入运行, 现有1.2m光电望远镜、迷你光电阵列望远镜、大视场光电望远镜阵列、新型多功能阵列结构光电探测平台等多台(套)光电望远镜设备. 利用上述设备, 主要围绕空间目标探测与识别、精密轨道确定、光电探测新方法以及变源天体的多色测光等开展相关研究工作, 与多家国内高校及科研院所保持着良好的合作关系.     

太阳选址全国日照条件分析

利用国家气象信息中心气象资料室整编的中国756个基本、基准气象站在1971～2000年间的日照时数和日照百分率年值、月值数据集,研究了全国范围的日照条件,结合1951～2008年间我国云量、水汽要素资料,分析了影响日照的因素.研究结果表明,我国日照时数和日照百分率的分布形势一致,东南少而西北多,从东南向西北递增.在我国东部地区日照时数由南向北逐步递增,西部地区日照条件整体好于东部,符合常规.藏西南狮泉河一带日照条件最优;40°N纬度带附近和藏南地区次之,江南华南、四川盆地以及云贵高原东部地区的日照条件较差;其余地区日照条件居中.日照时数受云量、水汽要素的影响,呈负相关关系.     

基于天文开源软件的卫星干涉测量数据处理系统

VLBI (Very Long Baseline Interferometry)技术观测卫星需要对干涉测量数据进行相关和后处理,通过相关、时延校准、条纹搜索,最终得到卫星的基线几何时延.基于天文开源软件建立起一套卫星干涉测量数据处理系统.该系统可工作在实时和事后两种状态,实现相关、中性大气、电离层、钟模型以及仪器硬件的时延校准、条纹搜索、生成基线时延和时延率序列.使用该系统处理北斗GEO (Geosynchronous Earth Orbit)卫星的干涉测量试验数据,得到了精度在1–2 ns量级的卫星基线时延序列.     

基于SE-Inception-v3的星系形态分类模型

随着天文探测技术的快速发展, 海量的星系图像数据不断产生, 能够及时高效地对星系图像进行形态分类对研究星系的形成与演化至关重要. 针对传统的星系形态分类模型特征选择困难、分类速度慢、准确率受限等难题, 提出一种以Inception-v3神经网络为主干结构, 融合压缩激励(Squeeze and Excitation Network, SE)通道注意力机制的星系形态分类模型. 该模型在斯隆数字巡天(Sloan Digital Sky Survey, SDSS)样本的测试集准确率高达99.37%. 旋涡星系、圆形星系、中间星系、雪茄状星系与侧向星系的F1值分别为99.33%、99.58%、99.33%、99.41%与99.16%. 该模型与Inception-v3、MobileNet (Mobile Neural Network)和ResNet (Residual Neural Network)网络模型相比, SE-Inception-v3宽度和深度优势表现出更强的特征提取能力, 可以高效识别不同形态的星系, 为未来大型巡天计划的大规模星系形态分类问题提供了一种新方法.     

基于FPN-ViT的星系形态分类研究

随着人工智能技术的发展, 利用深度学习方法进行星系形态分类研究取得了较大进展, 但在分类精度、自动化及其星系的空间特征表示上仍然存在不足之处. Vision Transformer (ViT)模型目前在星系形态分类上具有较好的鲁棒性, 但是在处理多尺度图像时存在一定的局限性, 因此提出将特征金字塔(Feature Pyramid Networks, FPN)引入ViT模型(FPN-ViT)中进行星系形态的分类研究中. 结果表明: 基于FPN-ViT模型进行星系形态分类的平均准确率、精确率、召回率以及F1分数等各项评估指标均在95%以上, 与传统的ViT模型相比各项指标均有一定程度的提升. 同时, 在原始星系图像中加入不同程度的高斯噪声和椒盐噪声, 验证FPN-ViT模型对低信噪比数据也能获得较好的分类性能. 此外, 为了对模型进行综合评估, 采用t分布随机邻接嵌入(t-distributed Stohastic Neighbor Embedding, t-SNE)算法对分类结果进行了可视化分析, 能够更加直接地看出FPN-ViT模型对于星系形态分类的效果. 因此, 将FPN网络应用于ViT模型对星系形态的分类研究中是一种全新尝试, 对后续研究具有重要意义.     

基于EfficientNet的星系形态分类研究

星系的结构和形态能够反映星系自身的物理性质,其形态的分类是后续分析研究的一个重要环节.EfficientNet模型使用复合系数对深度网络模型的深度、宽度、输入图像分辨率进行更加结构化的统一缩放,是一种新的深度网络优化扩展方法.将该模型应用于星系数据形态的分类研究中,结果表明基于EfficientNetB5模型的平均准确率、精确率、召回率以及F1分数(精确率与召回率的调和平均数)都在96.6%以上,与残差网络(Residual network, ResNet)中ResNet-26模型的分类结果相比有较大的提升.实验结果证明EfficientNet的深度网络优化扩展方法可行且有效,可应用于星系的形态分类.     

天文选址数字云量白天观测处理方法

云量观测是天文选址的重要考察项目.本文报告一种数字云量观测的处理方法,可以快速准确地计算选址点的云量值,避免了目视云量观测的人为误差.云量处理实验结果表明,该方法是合理可靠的, 在天文选址后期工作中能有效使用.该方法应用于西藏物玛观测点的云量观测统计,给出与同期目视云量的相关比较,并讨论数字云量处理的精度和改进方案.     

基于图像相减和随机森林的AST3巡天暂现源及变源搜寻方法

AST3-2 (Antarctic Survey Telescopes)光学巡天望远镜位于南极大陆最高点冰穹A,其产生的大量观测数据对数据处理的效率提出了较高要求.同时南极通信不便,数据回传有诸多困难,有必要在南极本地实现自动处理AST3-2观测数据,进行变源和暂现源观测的数据处理,但是受到低功耗计算机的限制,数据的快速自动处理的实现存在诸多困难.将已有的图像相减方案同机器学习算法相结合,并利用AST3-2 2016年观测数据作为测试样本,发展一套的暂现源及变源的筛选方法成为可行的选择.该筛选方法使用图像相减法初步筛选出可能的变源,再用主成分分析法抽取候选源的特征,并选择随机森林作为机器学习分类器,在测试中对正样本的召回率达到了97%,验证了这种方法的可行性,并最终在2016年观测数据中探测出一批变星候选体.     

Processing Method of Night-Time Cloudiness for Astronomical Site Selection

The cloudiness is one of the most important factors which affect the quality of an astronomical site, the monitoring and processing of the night- time cloudiness are especially important. The ground-based cloudiness camera is adopted to carry out the monitoring of the all-sky cloudiness, the images taken need to be processed by means of an effective method so as to quantize the cloudiness. The night-time cloudiness images are seriously affected by the moon- light, and therefore, the night-time cloudiness images are processed by dividing them into the moonlight and moonless two sorts. In the light of the condition of moonless night, the processing method of night-time cloudiness is given. The positioning and photometry of the bright stars in the image are conducted to determine their magnitude differences. By referring to the magnitude differences of the bright stars in the clear-night image, the probability of the bright stars of which the magnitude differences are lower than the threshold value are regarded as the probability standard of clear nights. Three sorts of images are selected to test the method. The cloudiness is determined, and the effect of the threshold condition on the result is analyzed. Finally, the applicable range and uncertainty of the method are discussed.     

The entropic prior for distributions with positive and negative values

The maximum entropy method has been used to reconstruct images in a wide range of astronomical fields, but in its traditional form it is restricted to the reconstruction of strictly positive distributions. We present an extension of the standard method to include distributions that can take both positive and negative values. The method may therefore be applied to a much wider range of astronomical reconstruction problems. In particular, we derive the form of the entropy for positive/negative distributions and use direct counting arguments to find the form of the entropic prior. We also derive the measure on the space of positive/negative distributions, which allows the definition of probability integrals and hence the proper quantification of errors.     

天文选址相机的研制

详细介绍了天文选址用CCD相机的开发研制．系统探测器选用具有累进扫描模式的ICX098ALCCD芯片，采用相关双采样信号读出电路、USB通讯接口实现数据与命令的传输．对系统工作性能进行了详细的测试：12℃条件下暗流约为52adu／see，系统增益(system gain)为2.2e／adu，读出噪声(readout noise)为30e．最后给出了在日本冈山天体物理观测所对大气宁静度的实测结果，该结果与冈山已有仪器测量值吻合．     

asmooth: a simple and efficient algorithm for adaptive kernel smoothing of two-dimensional imaging data

An efficient algorithm for adaptive kernel smoothing (AKS) of two-dimensional imaging data has been developed and implemented using the Interactive Data Language ( idl ). The functional form of the kernel can be varied (top-hat, Gaussian, etc.) to allow different weighting of the event counts registered within the smoothing region. For each individual pixel, the algorithm increases the smoothing scale until the signal-to-noise ratio (S/N) within the kernel reaches a pre-set value. Thus, noise is suppressed very efficiently, while at the same time real structure, that is, signal that is locally significant at the selected S/N level, is preserved on all scales. In particular, extended features in noise-dominated regions are visually enhanced. The asmooth algorithm differs from other AKS routines in that it allows a quantitative assessment of the goodness of the local signal estimation by producing adaptively smoothed images in which all pixel values share the same S/N above the background .
We apply asmooth to both real observational data (an X-ray image of clusters of galaxies obtained with the Chandra X-ray Observatory) and to a simulated data set. We find the asmooth ed images to be fair representations of the input data in the sense that the residuals are consistent with pure noise, that is, they possess Poissonian variance and a near-Gaussian distribution around a mean of zero, and are spatially uncorrelated.     

Object classification at the Nearby Supernova Factory

We present the results of applying new object classification techniques to the supernova search of the Nearby Supernova Factory. In comparison to simple threshold cuts, more sophisticated methods such as boosted decision trees, random forests, and support vector machines provide dramatically better object discrimination: we reduced the number of nonsupernova candidates by a factor of 10 while increasing our supernova identification efficiency. Methods such as these will be crucial for maintaining a reasonable false positive rate in the automated transient alert pipelines of upcoming large optical surveys. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Background–source separation in astronomical images with Bayesian probability theory – I. The method

A probabilistic technique for the joint estimation of background and sources with the aim of detecting faint and extended celestial objects is described. Bayesian probability theory is applied to gain insight into the co-existence of background and sources through a probabilistic two-component mixture model, which provides consistent uncertainties of background and sources. A multiresolution analysis is used for revealing faint and extended objects in the frame of the Bayesian mixture model. All the revealed sources are parametrized automatically providing source position, net counts, morphological parameters and their errors.
We demonstrate the capability of our method by applying it to three simulated data sets characterized by different background and source intensities. The results of employing two different prior knowledge on the source signal distribution are shown. The probabilistic method allows for the detection of bright and faint sources independently of their morphology and the kind of background. The results from our analysis of the three simulated data sets are compared with other source detection methods. Additionally, the technique is applied to ROSAT All-Sky Survey data.     

The VOISE algorithm: a versatile tool for automatic segmentation of astronomical images

The auroras on Jupiter and Saturn can be studied with a high sensitivity and resolution by the Hubble Space Telescope ( HST ) ultraviolet (UV) and far-ultraviolet Space Telescope Imaging Spectrograph (STIS) and Advanced Camera for Surveys (ACS) instruments. We present results of automatic detection and segmentation of Jupiter's auroral emissions as observed by the HST ACS instrument with the VOronoi Image SEgmentation (VOISE). VOISE is a dynamic algorithm for partitioning the underlying pixel grid of an image into regions according to a prescribed homogeneity criterion. The algorithm consists of an iterative procedure that dynamically constructs a tessellation of the image plane based on a Voronoi diagram, until the intensity of the underlying image within each region is classified as homogeneous. The computed tessellations allow the extraction of quantitative information about the auroral features, such as mean intensity, latitudinal and longitudinal extents and length-scales. These outputs thus represent a more automated and objective method of characterizing auroral emissions than manual inspection.     

The Sloan Digital Sky Survey monitor telescope pipeline

The photometric calibration of the Sloan Digital Sky Survey (SDSS) is a multi‐step process which involves data from three different telescopes: the 1.0‐m telescope at the US Naval Observatory (USNO), Flagstaff Station, Arizona (which was used to establish the SDSS standard star network); the SDSS 0.5‐m Photometric Telescope (PT) at the Apache Point Observatory (APO), NewMexico (which calculates nightly extinctions and calibrates secondary patch transfer fields); and the SDSS 2.5‐m telescope at APO (which obtains the imaging data for the SDSS proper). In this paper, we describe the Monitor Telescope Pipeline, MTPIPE, the software pipeline used in processing the data from the single‐CCD telescopes used in the photometric calibration of the SDSS (i.e., the USNO 1.0‐m and the PT). We also describe transformation equations that convert photometry on the USNO‐1.0m u ′g ′r ′i ′z ′ system to photometry the SDSS 2.5m ugriz system and the results of various validation tests of the MTPIPE software. Further, we discuss the semi‐automated PT factory, which runs MTPIPE in the day‐to‐day standard SDSS operations at Fermilab. Finally, we discuss the use of MTPIPE in current SDSS‐related projects, including the Southern u ′g ′r ′i ′z ′ Standard Star project, the u ′g ′r ′i ′z ′ Open Star Clusters project, and the SDSS extension (SDSS‐II). (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)