Stellar Spectral Classification using Principal Component Analysis and Artificial Neural Networks

A fast and robust method of classifying a library of optical stellar spectra for O to M type stars is presented. The method employs, as tools: (1) principal component analysis (PCA) for reducing the dimensionality of the data and (2) multilayer back propagation network (MBPN) based artificial neural network (ANN) scheme to automate the process of classification. We are able to reduce the dimensionality of the original spectral data to very few components by using PCA and are able to successfully reconstruct the original spectra. A number of NN architectures are used to classify the library of test spectra. Performance of ANN with this reduced dimension shows that the library can be classified to accuracies similar to those achieved by Gulati et al. but with less computational load. Furthermore, the data compression is so efficient that the NN scheme successfully classifies to the desired accuracy for a wide range of architectures. The procedure will greatly improve our capabilities in handling and analysing large spectral data bases of the future.     

A Study on the Photometric Redshifts of Faint Blue Galaxies in CDFS

From the COMBO-17 digital sky survey data, 1,231 faint blue galaxies with photometric redshifts of 0.1 < z < 0.3 in the sky region CDFS (Chandra Deep Field South) are selected as the sample. We have studied the distributions of the photometric redshifts of these sample galaxies, in the conditions that the photometric redshifts are obtained respectively by using only optical data and by using both optical and near-infrared data. The results indicate that there are 183 galaxies whose photometric redshifts derived from both optical and infrared data are greater than 1.2, that the rms error of the derived photometric redshifts is 0.046, and that to increase the photometric SNR is also helpful for discriminating those misjudged low-redshift galaxies by using only the optical data. We have studied as well the typical spectral energy distributions (SEDs) of these galaxies in the reference system at rest. It is found that for the high-redshift galaxies the observed near-infrared flux tends to be greater than the optical flux, while for the low-redshift galaxies the observed near-infrared flux tends to be less than the optical flux.     

Photometric redshifts with surface brightness priors

We use galaxy surface brightness as prior information to improve photometric redshift (photo- z ) estimation. We apply our template-based photo- z method to imaging data from the ground-based VVDS survey and the space-based GOODS field from HST , and use spectroscopic redshifts to test our photometric redshifts for different galaxy types and redshifts. We find that the surface brightness prior eliminates a large fraction of outliers by lifting the degeneracy between the Lyman and 4000-Å breaks. Bias and scatter are improved by about a factor of 2 with the prior in each redshift bin in the range  0.4 < z < 1.3  , for both the ground and space data. Ongoing and planned surveys from the ground and space will benefit, provided that care is taken in measurements of galaxy sizes and in the application of the prior. We discuss the image quality and signal-to-noise ratio requirements that enable the surface brightness prior to be successfully applied.     

Automated Stellar Classification for Large Surveys with EKF and RBF Neural Networks

An automated classification technique for large size stellar surveys is proposed. It uses the extended Kalman filter as a feature selector and pre-classifier of the data, and the radial basis function neural networks for the classification. Experiments with real data have shown that the correct classification rate can reach as high as 93%, which is quite satisfactory. When different system models are selected for the extended Kalman filter, the classification results are relatively stable. It is shown that for this particular case the result using extended Kalman filter is better than using principal component analysis.     

COSMOS场中星系恒星形成的演化研究

基于COSMOS(Cosmic Evolution Survey)/Ultra VISTA(Ultra-deep Visible and Infrared Survey Telescope for Astronomy)场中多波段测光数据,利用质量限选取了红移分布在0z3.5的星系样本.通过UVJ(U-V和V-J)双色图分类判据将星系分类成恒星形成星系(SFGs)和宁静星系(QGs).对于红移分布在0z1.5范围内且M*1011M⊙的QGs来说,该星系在样本中所占比例高于70%.在红移0z3.5范围内,恒星形成星系的恒星形成率(SFR)与恒星质量(M*)之间有着很强的主序(MS)关系.对于某一固定的恒星质量M*来说,星系的SFR和比恒星形成率(s SFR)会随着红移增大而增大,这表明在高红移处恒星形成星系更加活跃,有激烈的恒星形成.相对于低质量的星系来说,高质量的SFGs有较低的s SFR,这意味着低质量星系的增长更多的是通过星系本身的恒星形成.通过结合来自文献中数据点信息,发现更高红移(2z8)星系的s SFR随红移的演化趋势变弱,其演化关系是s SFR∝(1+z)0.94±0.17.     

Point spread function tails and the measurements of diffuse stellar halo light around edge-on disc galaxies

Measuring the integrated stellar halo light around galaxies is very challenging. The surface brightness of these haloes is expected to be many magnitudes below dark sky and the central brightness of the galaxy. Here, I show that in some of the recent literature the effect of very extended Point Spread Function (PSF) tails on the measurements of halo light has been underestimated; especially in the case of edge-on disc galaxies. The detection of a halo along the minor axis of an edge-on galaxy in the Hubble Ultra Deep Field can largely be explained by scattered galaxy light. Similarly, depending on filter and the shape one assumes for the uncertain extended PSF, 20–80 per cent of the halo light found along the minor axis of scaled and stacked Sloan Digital Sky Survey (SDSS) edge-on galaxy images can be explained by scattered galaxy light. Scattered light also significantly contributes to the anomalous halo colours of stacked SDSS images. The scattered light fraction decreases when looking in the quadrants away from the minor axis. The remaining excess light is well modelled with a Sérsic profile halo with shape parameters based on star count halo detections of nearby galaxies. Even though, the contribution from PSF scattered light does not fully remove the need for extended components around these edge-on galaxies, it will be very challenging to make accurate halo light shape and colour measurements from integrated light without very careful PSF measurements and scattered light modelling.     

基于卷积神经网络的快速射电暴候选体分类

针对目前从海量的快速射电暴(Fast Radio Burst, FRB)候选体中人工筛选FRB事件难以为继的现状,提出了一种基于卷积神经网络(Convolutional Neural Networks, CNN)的FRB候选体分类方法.首先,通过真实的观测数据和仿真FRB组成训练和测试样本集.其次,建立了二输入的深度卷积神经网络模型,并对其进行训练、测试和优化,获取FRB候选体分类器.最后,利用来自脉冲星的单脉冲数据对该分类器的有效性和性能进行了验证.实验结果表明,该方法可以快速从大量候选体中准确识别出单脉冲事件,极大地提高了FRB候选体的处理速率和效率.     

Principal component analysis for spectral indices of stellar populations

We apply the method of principal component analysis to a sample of simple stellar populations to select some age-sensitive spectral indices. Besides the well-known age-sensitive index, H β , we find that some other spectral indices have great potential to determine the age of stellar populations, such as G4300, Fe4383, C₂4668, and Mg b . In addition, we find that the sensitivity to age of these spectral indices depends on the metallicity of the simple stellar population (SSP): H β and G4300 are more suited to determine the age of the low-metallicity stellar population, C₂4668 and Mg b are more suited to the high-metallicity stellar population. The results suggest that the principal component analysis method provides a more objective and informative alternative to diagnostics by individual spectral lines.     

Two Novel Approaches for Photometric Redshift Estimation based on SDSS and 2MASS

We investigate two training-set methods: support vector machines (SVMs) and Kernel Regression (KR) for photometric redshift estimation with the data from the databases of Sloan Digital Sky Survey Data Release 5 and Two Micron All Sky Survey. We probe the performances of SVMs and KR for different input patterns. Our experiments show that with more parameters considered, the accuracy does not always increase, and only when appropriate parameters are chosen, the accuracy can improve. For different approaches, the best input pattern is different. With different parameters as input, the optimal bandwidth is dissimilar for KR. The rms errors of photometric redshifts based on SVM and KR methods are less than 0.03 and 0.02, respectively. Strengths and weaknesses of the two approaches are summarized. Compared to other methods of estimating photometric redshifts, they show their superiorities, especially KR, in terms of accuracy.     

Massive lossless data compression and multiple parameter estimation from galaxy spectra

We present a method for radical linear compression of data sets where the data are dependent on some number M of parameters. We show that, if the noise in the data is independent of the parameters, we can form M linear combinations of the data which contain as much information about all the parameters as the entire data set, in the sense that the Fisher information matrices are identical; i.e. the method is lossless. We explore how these compressed numbers fare when the noise is dependent on the parameters, and show that the method, though not precisely lossless, increases errors by a very modest factor. The method is general, but we illustrate it with a problem for which it is well-suited: galaxy spectra, the data for which typically consist of ∼10³ fluxes, and the properties of which are set by a handful of parameters such as age, and a parametrized star formation history. The spectra are reduced to a small number of data, which are connected to the physical processes entering the problem. This data compression offers the possibility of a large increase in the speed of determining physical parameters. This is an important consideration as data sets of galaxy spectra reach 10⁶ in size, and the complexity of model spectra increases. In addition to this practical advantage, the compressed data may offer a classification scheme for galaxy spectra which is based rather directly on physical processes.     

Objective classification of galaxy spectra using the information bottleneck method

A new method for classification of galaxy spectra is presented, based on a recently introduced information theoretical principle, the information bottleneck . For any desired number of classes, galaxies are classified such that the information content about the spectra is maximally preserved. The result is classes of galaxies with similar spectra, where the similarity is determined via a measure of information. We apply our method to ∼6000 galaxy spectra from the ongoing 2dF redshift survey, and a mock-2dF catalogue produced by a cold dark matter (CDM) based semi-analytic model of galaxy formation. We find a good match between the mean spectra of the classes found in the data and in the models. For the mock catalogue, we find that the classes produced by our algorithm form an intuitively sensible sequence in terms of physical properties such as colour, star formation activity, morphology, and internal velocity dispersion. We also show the correlation of the classes with the projections resulting from a principal component analysis.