The effects of spectrograph slit modes on the accuracy of stellar radial velocity measurement and atmospheric parameter estimation

Spectrograph slit is conventionally used to enhance the spectral resolution and manage how much light can be allowed to enter spectrograph.The narrow slit provides a higher resolution but sacrifices efficiency of spectrograph and results in a low signal to noise ratio(S/N) spectra product.We take GuoShouJing telescope as an example and carry out a series of experiments to study how its 2/3 slit mode affects the precision of stellar radial velocity measurement and atmosphere parameters estimate.By transforming the resolution and adding a Gaussian White Noise to the extremely high quality spectra from the Sloan Digital Sky Survey,we generate synthetic stellar spectra of various brightness with different S/Ns.Comparing the measurements on these noise added spectra with the original high quality ones,we summarize the influences of the 2/3 slit mode on the measurement accuracy of stellar radial velocity and atmospheric parameters.     

Search for carbon stars and DZ white dwarfs in SDSS spectra survey through machine learning

Carbon stars and DZ white dwarfs are two types of rare objects in the Galaxy.In this paper,we have applied the label propagation algorithm to search for these two types of stars from Data Release Eight(DR8)of the Sloan Digital Sky Survey(SDSS),which is verified to be efcient by calculating precision and recall.From nearly two million spectra including stars,galaxies and QSOs,we have found 260 new carbon stars in which 96 stars have been identified as dwarfs and 7 identified as giants,and 11 composition spectrum systems(each of them consists of a white dwarf and a carbon star).Similarly,using the label propagation method,we have obtained 29 new DZ white dwarfs from SDSS DR8.Compared with PCA reconstructed spectra,the 29 findings are typical DZ white dwarfs.We have also investigated their proper motions by comparing them with proper motion distribution of 9,374 white dwarfs,and found that they satisfy the current observed white dwarfs by SDSS generally have large proper motions.In addition,we have estimated their efective temperatures by fitting the polynomial relationship between efective temperature and g-r color of known DZ white dwarfs,and found 12 of the 29 new DZ white dwarfs are cool,in which nine are between 6,000 K and 6,600 K,and three are below 6,000 K.     

k -Nearest Neighbors for automated classification of celestial objects

The nearest neighbors (NNs) classifiers, especially the k-Nearest Neighbors (kNNs) algorithm, are among the simplest and yet most efficient classification rules and widely used in practice. It is a nonparametric method of pattern recognition. In this paper, k-Nearest Neighbors, one of the most commonly used machine learning methods, work in automatic classification of multi-wavelength astronomical objects. Through the experiment, we conclude that the running speed of the kNN classier is rather fast and the classification accuracy is up to 97.73%. As a result, it is efficient and applicable to discriminate active objects from stars and normal galaxies with this method. The classifiers trained by the kNN method can be used to solve the automated classification problem faced by astronomy and the virtual observatory (VO). Supported by the National Natural Science Foundation of China (Grant Nos. 10473013, 10778724 and 90412016)     

A SVM-kNN method for quasar-star classification

We integrate k-Nearest Neighbors(kNN) into Support Vector Machine(SVM) and create a new method called SVM-kNN.SVM-kNN strengthens the generalization ability of SVM and apply kNN to correct some forecast errors of SVM and improve the forecast accuracy.In addition,it can give the prediction probability of any quasar candidate through counting the nearest neighbors of that candidate which is produced by kNN.Applying photometric data of stars and quasars with spectral classification from SDSS DR7 and considering limiting magnitude error is less than 0.1,SVM-kNN and SVM reach much higher performance that all the classification metrics of quasar selection are above 97.0%.Apparently,the performance of SVM-kNN has slighter improvement than that of SVM.Therefore SVM-kNN is such a competitive and promising approach that can be used to construct the targeting catalogue of quasar candidates for large sky surveys.     

The emission positions of kHz QPOs and Kerr spacetime influence

Based the Alfven wave oscillation model (AWOM) and relativistic precession model (RPM) for twin kHz QPOs, we estimate the emission positions of most detected kHz QPOs to be at r=18+/−3 km (R/15 km), except Cir X-1 at r∼30+/−5 km (R/15 km). For the proposed Keplerian frequency as an upper limit to kHz QPO, the spin effects in Kerr Spacetime are discussed, which have about a 5% (2%) modification for that of the Schwarzchild case for the spin frequency of 1000 (400) Hz. The application to the four typical QPO sources, Cir X-1, Sco X-1, SAX J1808.4-3658 and XTE 1807-294, is mentioned.

     

Exploration of SDSS stellar database by AutoClass

AutoClass is an unsupervised Bayesian classification approach which seeks a maximum posterior probability classification for determining the optimal classes in large data sets. Using stellar photometric data from the Sloan Digital Sky Survey (SDSS) data release 7 (DR7), we utilize AutoClass to select non-stellar objects from this sample in order to build a pure stellar sample. For this purpose, the differences between PSF (point spread function) magnitudes and model magnitudes in five wavebands are taken as...     

富氮杂环配位超分子化合物的合成、结构及光谱特征

以2,6-二(5-甲基-1H-吡唑-3-基)吡啶(简写为H2L)为第一配体,以均苯四甲酸和乙酸为第二配体,合成了2个新的配位超分子化合物[Ni(H2L)2]·(Me O-H2BTA)2(1)和Mn(H2L)(Ac)2(2)(H4BTA∶均苯四甲酸,HAc∶乙酸),并由元素分析、红外光谱、紫外-可见光谱以及X射线单晶衍射等对其进行了表征,分析了其光谱及结构特征。晶体结构分析表明,化合物1属于正交晶系,Pbcn空间群,a=0.95747(8)nm,b=1.99273(13)nm,c=2.50175(13)nm,α=90°,β=90°,γ=90°,V=4.773 3(6)nm3,Z=4;化合物2属于单斜晶系,Cc空间群,a=1.11300(13)nm,b=2.0239(2)nm,c=0.97308(12)nm,α=90°,β=118.555(16)°,γ=90°,V=1.9253(4)nm3,Z=4。