Spectral Synthesis via Mean Field approach to Independent Component Analysis

We apply a new statistical analysis technique, the Mean Field approach to Independent Component Analysis(MF-ICA) in a Bayseian framework, to galaxy spectral analysis. This algorithm can compress a stellar spectral library into a few Independent Components(ICs), and the galaxy spectrum can be reconstructed by these ICs. Compared to other algorithms which decompose a galaxy spectrum into a combination of several simple stellar populations, the MF-ICA approach offers a large improvement in efficiency. To check the reliability of this spectral analysis method, three different methods are used:(1) parameter recovery for simulated galaxies,(2) comparison with parameters estimated by other methods, and(3) consistency test of parameters derived with galaxies from the Sloan Digital Sky Survey. We find that our MF-ICA method can not only fit the observed galaxy spectra efficiently, but can also accurately recover the physical parameters of galaxies. We also apply our spectral analysis method to the DEEP2 spectroscopic data, and find it can provide excellent fitting results for low signal-to-noise spectra.     

A non-parametric method of estimating the physical parameters of stellar atmosphere

The physical parameters of stellar atmosphere, e.g. the effective temperature, surface gravity and chemical abundance, are the main factors for the differences in stellar spectra, and the automatic measurement of these parameters is an important content in the automatic processing of the immense amount of spectral data provided by LAMOST and other patrol telescopes. Aiming at the estimation of the physical parameters for every star in large samples of stellar spectral data, a variable window-width algorithm is proposed in this article. It consists of the following three steps: (1) A PCA (principal component analysis) treatment of historical stellar spectral data is carried out to obtain a low-dimensional characteristic data of the spectra. (2) Establish the correlation between the characteristic data and the physical parameters using a non-parametric estimator with variable window-width. (3) By means of this estimator, the three physical parameters of the star are directly calculated. As shown by results of experiments, in comparison with the fixed window-width estimator and other algorithms reported in literature, our algorithm is more accurate and robust.     

基于深度学习的LAMOST星系星族参数测量

星系的光谱包含其内部恒星的年龄和金属丰度等信息, 从观测光谱数据中测量这些信息对于深入了解星系的形成和演化至关重要. LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope)巡天发布了大量的星系光谱, 这些高维光谱与它们的物理参数之间存在着高度的非线性关系. 而深度学习适合于处理多维、海量的非线性数据, 因此基于深度学习技术构建了一个8个卷积层$+$4个池化层$+$1个全连接层的卷积神经网络, 对LAMOST Data Release 7 (DR7)星系的年龄和金属丰度进行自动估计. 实验结果表明, 使用卷积神经网络通过星系光谱预测的星族参数与传统方法基本一致, 误差在0.18dex以内, 并且随着光谱信噪比的增大, 预测误差越来越小. 实验还对比了卷积神经网络与随机森林回归模型、深度神经网络的参数测量结果, 结果表明卷积神经网络的结果优于其他两种回归模型.     

A Hybrid Algorithm for Spectral Analysis

In this paper we present a method that combines evolution strategies (ES) and standard optimization algorithms to solve the problem of fitting line profiles of stellar spectra. This method provides a reliable decomposition and a reduction in computing time over conventional algorithms. Using a stellar spectrum as input, we implemented an evolution strategy to find an approximation of the continuum spectrum and spectral lines. After a few generations, the parameters found by ES are given as starting search point to a standard optimization algorithm, which then finds the correct spectral decomposition. We used Gaussian functions to fit spectral lines and the Planck function to represent the continuum spectrum. Our experimental results present the application of this method to real spectra, showing that they can be approximated very accurately. This revised version was published online in July 2006 with corrections to the Cover Date.     

Adaptive stellar spectral subclass classification based on Bayesian SVMs

Stellar spectral classification is one of the most fundamental tasks in survey astronomy. Many automated classification methods have been applied to spectral data. However, their main limitation is that the model parameters must be tuned repeatedly to deal with different data sets. In this paper, we utilize the Bayesian support vector machines (BSVM) to classify the spectral subclass data. Based on Gibbs sampling, BSVM can infer all model parameters adaptively according to different data sets, which allows us to circumvent the time-consuming cross validation for penalty parameter. We explored different normalization methods for stellar spectral data, and the best one has been suggested in this study. Finally, experimental results on several stellar spectral subclass classification problems show that the BSVM model not only possesses good adaptability but also provides better prediction performance than traditional methods.     

Photometrically estimating the spatially-resolved stellar mass-to-light ratios for low-redshift galaxies

The stellar mass-to-light ratio(M_*/L) of galaxies in a given wave band shows tight correlations with optical colors, which have been widely applied as cheap estimators of galaxy stellar masses. These estimators are usually calibrated using either broadband spectral energy distributions(SEDs) or spectroscopy at galactic centers. However, it is unclear whether the same estimators provide unbiased M_*/L for different regions within a galaxy. In this work we employ integral field spectroscopy from the Mapping Nearby Galaxies at Apache Point Observatory(Ma NGA) survey. We also examine the correlations of spatially resolved M_*/L obtained from full spectral fitting, with different color indices, as well as galaxy morphology types, distances to the galactic center, and stellar population parameters such as stellar age and metallicity.We find that the(g-r) color is better than any other color indices, and it provides almost unbiased M_*/L for all the SDSS five bands and for all types of galaxies or regions, with only slight biases depending on stellar age and metallicity. Our analysis indicates that combining multiple colors and/or including other properties to reduce the systematics and scatters of the estimator does not work better than a single color index defined by two bands. Therefore, we have obtained a best estimator with the(g-r) color and applied it to the Ma NGA galaxies. Both the two-dimensional map and radial profile of M_*/L are reproduced well in most cases. Our estimator may be applied to obtain surface mass density maps for large samples of galaxies from imaging surveys at both low and high redshifts.     

Estimating stellar atmospheric parameters based on Lasso features

With the rapid development of large scale sky surveys like the Sloan Digital Sky Survey （SDSS）, GAIA and LAMOST （Guoshoujing telescope）, stellar spectra can be obtained on an ever-increasing scale. Therefore, it is necessary to estimate stel- lar atmospheric parameters such as Teff, log g and [Fe/H] automatically to achieve the scientific goals and make full use of the potential value of these observations. Feature selection plays a key role in the automatic measurement of atmospheric parameters. We propose to use the least absolute shrinkage selection operator （Lasso） algorithm to select features from stellar spectra. Feature selection can reduce redundancy in spectra, alleviate the influence of noise, improve calculation speed and enhance the robustness of the estimation system. Based on the extracted features, stellar atmospheric param- eters are estimated by the support vector regression model. Three typical schemes are evaluated on spectral data from both the ELODIE library and SDSS. Experimental results show the potential performance to a certain degree. In addition, results show that our method is stable when applied to different spectra.     

Estimating stellar effective temperatures and detected angular parameters using stochastic particle swarm optimization

Considering features of stellar spectral radiation and sky surveys, we established a computational model for stellar effective temperatures, detected angular parameters and gray rates. Using known stellar flux data in some bands, we estimated stellar effective temperatures and detected angular parameters using stochastic particle swarm optimization(SPSO). We first verified the reliability of SPSO, and then determined reasonable parameters that produced highly accurate estimates under certain gray deviation levels.Finally, we calculated 177 860 stellar effective temperatures and detected angular parameters using data from the Midcourse Space Experiment(MSX) catalog. These derived stellar effective temperatures were accurate when we compared them to known values from literatures. This research makes full use of catalog data and presents an original technique for studying stellar characteristics. It proposes a novel method for calculating stellar effective temperatures and detecting angular parameters, and provides theoretical and practical data for finding information about radiation in any band.     

Identifying star clusters in a field: A comparison of different algorithms

Star clusters are often hard to find, as they may lie in a dense field of background objects or, because in the case of embedded clusters, they are surrounded by a more dispersed population of young stars. This paper discusses four algorithms that have been developed to identify clusters as stellar density enhancements in a field, namely stellar density maps from star counts, the nearest neighbour method and the Voronoi tessellation, and the separation of minimum spanning trees. These methods are tested and compared to each other by applying them to artificial clusters of different sizes and morphologies. While distinct centrally concentrated clusters are detected by all methods, clusters with low overdensity or highly hierarchical structure are only reliably detected by methods with inherent smoothing (star counts and nearest neighbour method). Furthermore, the algorithms differ strongly in computation time and additional parameters they provide. Therefore, the method to choose primarily depends on the size and character of the investigated area and the purpose of the study (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

恒星大气物理参量的非参数估计方法

恒星大气物理参量（有效温度、表面重力、化学丰度）是导致恒星光谱差异的主要因素．恒星大气物理参量的自动测量是LAMOST等大规模巡天望远镜所产生的海量天体光谱数据自动处理中一个重要研究内容．针对测量大样本的恒星光谱数据估计每个恒星的大气物理参量，提出了一种基于变窗宽核函数的估计算法：变窗宽算法是对固定窗宽算法的改进，分为3个步骤：（1）将历史恒星光谱数据进行PCA处理，得到光谱的低维特征数据；（2）利用特征数据与其物理参数的对应关系，建立一种变窗宽的非参数估计模型；（3）利用该估计模型，直接计算待测恒星光谱的3个物理参量（有效温度、表面重力、金属丰度）．实验结果表明：该方法与固定窗宽估计模型以及在其他文献中报道的方法相比，具有较高的估计精度和鲁棒性．     

Stellar Spectral Classification Based on Capsule Network

The rapid development of large-scale sky survey project has produced a large amount of stellar spectral data, which make the automatic classification of stellar spectral data a challenging task. In this paper, we have proposed a stellar spectral classification method based on a capsule network. At first, by using the one-dimensional convolutional network and short-time Fourier transform (STFT), the one-dimensional spectra of the F5, G5, and K5 types selected from the LAMOST Data Release 5 (DR5) are converted into the two-dimensional Fourier spectrum images. Then, the two-dimensional Fourier spectrum images are classified automatically by the capsule network. Because the capsule network can preserve the hierarchical pose relationships among the entities in the image, and it does not need any pooling layers, the experimental results show that the capsule network has a better classification performance, for the classifications of the F5, G5, and K5-type stellar spectra, its classification accuracy is superior to other classification methods.     

基于胶囊网络的恒星光谱分类研究

大型巡天项目的快速发展,产生大量的恒星光谱数据,也使得实现恒星光谱数据的自动分类成为一项具有挑战性的工作.提出一种新的基于胶囊网络的恒星光谱分类方法,首先利用1维卷积网络和短时傅里叶变换将来源于LAMOST(Large Sky Area Multi-Object Fiber Spectroscopy Telescope)Data Release 5(DR5)的F5、G5、K5型1维恒星光谱转化成2维傅里叶谱图像,再通过胶囊网络对2维谱图像进行自动分类.由于胶囊网络具有保留图像中实体之间的分层位姿关系和无需池化层的优点,实验结果表明:胶囊网络具有较好的分类性能,对于F5、G5、K5型恒星光谱的分类,准确率优于其他分类方法.     

Surface magnetic fields on two accreting T Tauri stars: CV Cha and CR Cha

We have produced brightness and magnetic field maps of the surfaces of CV Cha and CR Cha: two actively accreting G- and K-type T Tauri stars in the Chamaeleon I star-forming cloud with ages of 3–5 Myr. Our magnetic field maps show evidence for strong, complex multipolar fields similar to those obtained for young rapidly rotating main-sequence stars. Brightness maps indicate the presence of dark polar caps and low-latitude spots – these brightness maps are very similar to those obtained for other pre-main-sequence and rapidly rotating main-sequence stars.
Only two other classical T Tauri stars have been studied using similar techniques so far: V2129 Oph and BP Tau. CV Cha and CR Cha show magnetic field patterns that are significantly more complex than those recovered for BP Tau, a fully convective T Tauri star.
We discuss possible reasons for this difference and suggest that the complexity of the stellar magnetic field is related to the convection zone; with more complex fields being found in T Tauri stars with radiative cores (V2129 Oph, CV Cha and CR Cha). However, it is clearly necessary to conduct magnetic field studies of T Tauri star systems, exploring a wide range of stellar parameters in order to establish how they affect magnetic field generation, and thus how these magnetic fields are likely to affect the evolution of T Tauri star systems as they approach the main sequence.     

Automated estimation of stellar fundamental parameters from low resolution spectra: the PLS method

PLS (Partial Least Squares regression) is introduced into an automatic esti-mation of fundamental stellar spectral parameters. It extracts the most correlative spec-tral component to the parameters (Teff, log g and [Fe/H]), and sets up a linear regres-sion function from spectra to the corresponding parameters. Considering the properties of stellar spectra and the PLS algorithm, we present a piecewise PLS regression method for estimation of stellar parameters, which is composed of one PLS model for Teff, and seven PLS models for log g and [Fe/H] estimation. Its performance is investigated by large experiments on flux calibrated spectra and continuum normalized spectra at dif-ferent signal-to-noise ratios (SNRs) and resolutions. The results show that the piecewise PLS method is robust for spectra at the medium resolution of 0.23 nm. For low resolu-tion 0.5 nm and 1 nm spectra, it achieves competitive results at higher SNR. Experiments using ELODIE spectra of 0.23 nm resolution illustrate that our piecewise PLS models trained with MILES spectra are efficient for O ～ G stars: for flux calibrated spectra, the systematic offsets are 3.8%, 0.14dex, and -0.09 dex for Teff, log g and [Fe/H], with error scatters of 5.2%, 0.44 dex and 0.38 dex, respectively; for continuum normalized spectra, the systematic offsets are 3.8%, 0.12dex, and -0.13 dex for Teff, log g and [Fe/H], with error scatters of 5.2%, 0.49 dex and 0.41 dex, respectively. The PLS method is rapid, easy to use and does not rely as strongly on the tightness of a parameter grid of templates to reach high precision as Artificial Neural Networks or minimum distance methods do.     

Inclusion of binaries in evolutionary population synthesis

Using evolutionary population synthesis we present integrated colours, integrated spectral energy distributions and absorption-line indices defined by the Lick Observatory image dissector scanner (referred to as the Lick/IDS) system, for an extensive set of instantaneous-burst binary stellar populations with and without binary interactions. The ages of the populations are in the range 1–15 Gyr and the metallicities are in the range 0.0001–0.03. By comparing the results for populations with and without binary interactions we show that the inclusion of binary interactions makes the integrated U – B , B – V , V – R and R – I colours and all Lick/IDS spectral absorption indices (except for H_β) substantially smaller. In other words, binary evolution makes a population appear bluer. This effect raises the derived age and metallicity of the population.
We calculate several sets of additional solar-metallicity binary stellar populations to explore the influence of the binary evolution algorithm input parameters (the common-envelope ejection efficiency and the stellar wind mass-loss rate) on the resulting integrated colours. We also look at the dependence on the choice of distribution functions used to generate the initial binary population. The results show that variations in the choice of input model parameters and distributions can significantly affect the results. However, comparing the discrepancies that exist between the colours of various models, we find that the differences are less than those produced between the models with and those without binary interactions. Therefore it is very necessary to consider binary interactions in order to draw accurate conclusions from evolutionary population synthesis work.     

Starspots, Activity Cycles, and Differential Rotation on Cool Stars

We present the first results of searching for stellar cycles by analysis of stellar spottedness using an algorithm developed at the Crimean Astrophysical Observatory. For more than 35 red spotted stars, we find ten targets which demonstrate cyclic variations of average latitudes and total areas of starspots. Activity cycles detected by this method have a typical cycle length about 4–15 years which are analogous to the 11-year solar Schwabe cycle. Most of the program stars demonstrate a rough analogue with the solar butterfly diagram. They show a tendency for the average starspot latitude lowering when the total spot area grows. At the same time these stars show variations of stellar photometric period (which is traced by starspots) with the starspot latitudinal drift analogously to the solar differential rotation effect. We suspect that the starspot latitudinal drift rate and the differential rotation gradient depend on the stellar spectral type.     

Multisite campaign on the open cluster M67 – II. Evidence for solar-like oscillations in red giant stars

Measuring solar-like oscillations in an ensemble of stars in a cluster, holds promise for testing stellar structure and evolution more stringently than just fitting parameters to single field stars. The most-ambitious attempt to pursue these prospects was by Gilliland et al. who targeted 11 turn-off stars in the open cluster M67 (NGC 2682), but the oscillation amplitudes were too small (<20 μmag) to obtain unambiguous detections. Like Gilliland et al. we also aim at detecting solar-like oscillations in M67, but we target red giant stars with expected amplitudes in the range 50–  500 μmag  and periods of 1 to 8 h. We analyse our recently published photometry measurements, obtained during a six-week multisite campaign using nine telescopes around the world. The observations are compared with simulations and with estimated properties of the stellar oscillations. Noise levels in the Fourier spectra as low as  27 μmag  are obtained for single sites, while the combined data reach  19 μmag  , making this the best photometric time series of an ensemble of red giant stars. These data enable us to make the first test of the scaling relations (used to estimate frequency and amplitude) with an homogeneous ensemble of stars. The detected excess power is consistent with the expected signal from stellar oscillations, both in terms of its frequency range and amplitude. However, our results are limited by apparent high levels of non-white noise, which cannot be clearly separated from the stellar signal.     

Solar–Stellar Irradiance Comparison Experiment II (SOLSTICE II): Pre-Launch and On-Orbit Calibrations

The Solar–Stellar Irradiance Comparison Experiment {II (SOLSTICE {II), aboard the Solar Radiation and Climate Experiment (SORCE) spacecraft, consists of a pair of identical scanning grating monochromators, which have the capability to observe both solar spectral irradiance and stellar spectral irradiance using a single optical system. The SOLSTICE science objectives are to measure solar spectral irradiance from 115 to 320 nm with a spectral resolution of 1 nm, a cadence of 6 h, and an accuracy of 5%, to determine its variability with a long-term relative accuracy of 0.5% per year during a 5-year nominal mission, and to determine the ratio of solar irradiance to that of an ensemble of bright B and A stars to an accuracy of 2%. Those objectives are met by calibrating instrument radiometric sensitivity before launch using the Synchrotron Ultraviolet Radiation Facility at the National Institute for Standards and Technology in Gaithersburg, Maryland. During orbital operations irradiance measurements from an ensemble of bright, stable, main-sequence B and A stars are used to track instrument sensitivity. SORCE was launched on 25 January 2003. After spacecraft and instrument check out, SOLSTICE {II first observed a series of three stars to establish an on-orbit performance baseline. Since 6 March 2003, both instruments have been making daily measurements of both the Sun and stars. This paper describes the pre-flight and in-flight calibration and characterization measurements that are required to achieve the SOLSTICE science objectives and compares early SOLSTICE{II measurements of both solar and stellar irradiance with those obtained by SOLSTICE {I on the Upper Atmosphere Research Satellite.     

Regular black hole motion and stellar orbital resonances

The motion of a black hole about the centre of gravity of its host galaxy induces a strong response from the surrounding stellar population. We treat the case of a harmonic potential analytically and show that half of the stars on circular orbits in that potential shift to an orbit of lower energy, while the other half receive a positive boost and recede to a larger radius. The black hole itself remains on an orbit of fixed amplitude and merely acts as a catalyst for the evolution of the stellar energy distribution function f ( E ). We show that this effect is operative out to a radius of approximately three to four times the hole's influence radius, R _bh. We use numerical integration to explore more fully the response of a stellar distribution to black hole motion. We consider orbits in a logarithmic potential and compare the response of stars on circular orbits, to the situation of a 'warm' and 'hot' (isotropic) stellar velocity field. While features seen in density maps are now wiped out, the kinematic signature of black hole motion still imprints the stellar line-of-sight mean velocity to a magnitude ≃13 per cent the local rms velocity dispersion σ. A study in three dimensions suggests a reduced effect for polar orbits.     

The Ogle-Tr-56 Star–Planet System

We simultaneously fitted light and velocity data for the star–planet system OGLE-TR-56 with the Wilson–Devinney (WD) binary star program. We solved for orbital and planet parameters, along with the ephemeris using all currently available observational data. Parameters for the star (OGLE-TR-56a) were kept fixed at values derived from spectral characteristics and stellar evolutionary tracks. Our results are in good agreement with parameters obtained by other authors and have slightly smaller errors. We found no significant change in orbital period that may be due to orbital decay.