Stellar spectra association rule mining method based on the weighted frequent pattern tree

Effective extraction of data association rules can provide a reliable basis for classification of stellar spectra. The concept of stellar spectrum weighted itemsets and stellar spectrum weighted association rules are introduced, and the weight of a single property in the stellar spectrum is determined by information entropy. On that basis, a method is presented to mine the association rules of a stellar spectrum based on the weighted frequent pattern tree. Important properties of the spectral line are highlighted using this method. At the same time, the waveform of the whole spectrum is taken into account. The experimental results show that the data association rules of a stellar spectrum mined with this method are consistent with the main features of stellar spectral types.

Validation of LAMOST stellar parameters with the PASTEL catalog

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） published its first data release （DR1） in 2013, which is currently the largest dataset of stellar spectra in the world. We combine the PASTEL catalog and SIMBAD radial velocities as a testing standard to validate stellar parameters （effec- tive temperature Tefr, surface gravity log g, metallicity [Fe/H] and radial velocity Vr） derived from DR1. Through cross-identification of the DR1 catalogs and the PASTEL catalog, we obtain a preliminary sample of 422 stars. After removal of stellar param- eter measurements from problematic spectra and applying effective temperature con- straints to the sample, we compare the stellar parameters from DR1 with those from PASTEL and SIMBAD to demonstrate that the DR1 results are reliable in restricted ranges of Tefr. We derive standard deviations of 110 K, 0.19 dex and 0.11 dex for Tell, log 9 and [Fe/H] respectively when Teff〈 8000 K, and 4.91 km s-1 for Vr when Teff 〈 10 000 K. Systematic errors are negligible except for those of Vr. In addition, metallicities in DR1 are systematically higher than those in PASTEL, in the range of PASTEL [Fe/H] 〈 -1.5.

A Periodicity Analysis of the Light Curve of 3C 454.3

We analyzed the radio light curves of 3C 454.3 at frequencies 22 and 37 GHz taken from the database of Metsaeovi Radio Observatory, and found evidence of quasi-periodic activity. The light curves show great activity with very complicated non-sinusoidal variations. Two possible periods, a very weak one of 1.57 ± 0.12 yr and a very strong one of 6.15 ±0.50 yr were consistently identified by two methods, the Jurkevich method and power specmun estimation. The period of 6.15 ± 0.50 yr is consistent with results previously reported by Ciaramella et al. and Webb et al. Applying the binary black hole model to the central structure we found black hole masses of 1.53 × 10^9M⊙ and 1.86 × 10^8M⊙, and predicted that the next radio outburst is to take place in 2006 March and April.

Automatic determination of stellar atmospheric parameters and construction of stellar spectral templates of the Guoshoujing Telescope (LAMOST)

A number of spectroscopic surveys have been carried out or are planned to study the origin of the Milky Way. Their exploitation requires reliable automated methods and softwares to measure the fundamental parameters of the stars. Adopting the ULySS package, we have tested the effect of different resolutions and signal-to- noise ratios （SNR） on the measurement of the stellar atmospheric parameters （effective temperature Teff, surface gravity log g, and metaUicity [Fe/H]）. We show that ULySS is reliable for determining these parameters with medium-resolution spectra （R ～2000）. Then, we applied the method to measure the parameters of 771 stars selected in the commissioning database of the Guoshoujing Telescope （LAMOST）. The results were compared with the SDSS/SEGUE Stellar Parameter Pipeline （SSPP）, and we derived precisions of 167 K, 0.34dex, and 0.16dex for Teff, logg and [Fe/H] respectively. Furthermore, 120 of these stars are selected to construct the primary stellar spectral template library （Version 1.0） of LAMOST, and will be deployed as basic ingredients for the LAMOST automated parametrization pipeline.

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.

On the LSP3 estimates of surface gravity for LAMOST-Kepler stars with asteroseismic measurements

Asteroseismology allows for deriving precise values of the surface gravity of stars. The accurate asteroseismic determinations now available for the large number of stars in the Kepler fields can be used to check and calibrate surface gravities that are currently being obtained spectroscopically for a huge number of stars targeted by large-scale spectroscopic surveys, such as the on-going Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） Galactic survey. The LAMOST spectral surveys have obtained a large number of stellar spectra in the Kepler fields. Stellar atmospheric parameters of those stars have been determined with the LAMOST Stellar Parameter Pipeline at Peking University （LSP3）, by template matching with the MILES empirical spectral library. In the current work, we compare surface gravities yielded by LSP3 with those of two asteroseismic samples-- the largest Kepler asteroseismic sample and the most accurate Kepler asteroseismic sample. We find that LSP3 surface gravities are in good agreement with asteroseismic values of Hekker et al., with a dispersion of -0.2 dex. Except for a few cases, asteroseismic surface gravities ofHuber et al. and LSP3 spectroscopic values agree for a wide range of surface gravities. However, some patterns in the differences can be identified upon close inspection. Potential ways to further improve the LSP3 spectroscopic estimation of stellar atmospheric parameters in the near future are briefly discussed. The effects of effective temperature and metallicity on asteroseismic determinations of surface gravities for giant stars are also discussed.

Estimation of Stellar Atmospheric Parameters from LAMOST DR8 Low-resolution Spectra with 20 ≤ S/N < 30

The accuracy of the estimated stellar atmospheric parameter evidently decreases with the decreasing of spectral signal-to-noise ratio(S/N)and there are a huge amount of this kind observations,especially in case of S/N<30.Therefore,it is helpful to improve the parameter estimation performance for these spectra and this work studied the(T_(eff),log g,[Fe/H])estimation problem for LAMOST DR8 low-resolution spectra with 20≤S/N<30.We proposed a data-driven method based on machine learning techniques.First,this scheme detected stellar atmospheric parameter-sensitive features from spectra by the Least Absolute Shrinkage and Selection Operator(LASSO),rejected ineffective data components and irrelevant data.Second,a Multi-layer Perceptron(MLP)method was used to estimate stellar atmospheric parameters from the LASSO features.Finally,the performance of the LASSO-MLP was evaluated by computing and analyzing the consistency between its estimation and the reference from the Apache Point Observatory Galactic Evolution Experiment high-resolution spectra.Experiments show that the Mean Absolute Errors of T_(eff),log g,[Fe/H]are reduced from the LASP(137.6 K,0.195,0.091 dex)to LASSO-MLP(84.32 K,0.137,0.063 dex),which indicate evident improvements on stellar atmospheric parameter estimation.In addition,this work estimated the stellar atmospheric parameters for 1,162,760 lowresolution spectra with 20≤S/N<30 from LAMOST DR8 using LASSO-MLP,and released the estimation catalog,learned model,experimental code,trained model,training data and test data for scientific exploration and algorithm study.

Elliptical distortion of the Milky Way＇s rotation traced by high-mass star-forming regions

The gravitational potential of the Milky Way is non-axisymmetric, caused by a bar or triaxial halo, which dominates elliptical rotation of the Milky Way. Employing a likelihood analysis, we exploit the astrometric data of masers thoroughly and constrain the elliptical rotation of the Galaxy. Masers in high-mass star-forming regions, observed by VLBA, are more distant tracers than stars observed in the optical bandpass, and thus are more appropriate for studying the global feature of the Milky Way＇s rotation. A clear elliptical potential of the Milky Way is detected, with an ellipticity of ε0-0.09 at the Sun, and the ellipticity increases towards the outer disk. The minor axis of the elliptical potential （the major axis of the rotation orbit） is found to be near the Sun with a displacement of -32°. Based on the rotation model assumed for an elliptical potential, we also make a kinematical calibration of the Galactocentric distance of the Sun, which gives R0 = 7.63±0.34 kpc.

Light curve solutions of six eclipsing binaries at the lower limit of periods for W UMa stars

Photometric observations are presented in V and I bands of six eclipsing binaries at the lower limit of the orbital periods for W UMa stars. Three of them are newly discovered eclipsing systems. The light curve solutions reveal that all shortperiod targets are contact or overcontact binaries and six new binaries are added to the family of short-period systems with estimated parameters. Four binaries have com- ponents that are equal in size and a mass ratio near 1. The phase variability shown by the V-I colors of all targets may be explained by lower temperatures on their back surfaces than those on their side surfaces. Five systems exhibit the O＇Connell effect that can be modeled by cool spots on the side surfaces of their primary components. The light curves of V1067 Her in 2011 and 2012 are fitted by diametrically opposite spots. Applying the criteria for subdivision of W UMa stars to our targets leads to ambiguous results.

Long time data series and data stewardship reference model

The need for accessing historical Earth Observation(EO)data series strongly increased in the last ten years,particularly for long-term science and environmental monitoring applications.This trend is likely to increase even more in the future,in particular regarding the growing interest on global change monitoring which is driving users to request time-series of data spanning 20 years and more,and also due to the need to support the United Nations Framework Convention on Climate Change(UNFCCC).While much of the satellite observations are accessible from different data centers,the solution for analyzing measurements collected from various instruments for time series analysis is both difficult and critical.Climate research is a big data problem that involves high data volume of measurements,methods for on-the-fly extraction and reduction to keep up with the speed and data volume,and the ability to address uncertainties from data collections,processing,and analysis.The content of EO data archives is extending from a few years to decades and therefore,their value as a scientific time-series is continuously increasing.Hence there is a strong need to preserve the EO space data without time constraints and to keep them accessible and exploitable.The preservation of EO space data can also be considered as responsibility of the Space Agencies or data owners as they constitute a humankind asset.This publication aims at describing the activities supported by the European Space Agency relating to the Long Time Series generation with all relevant best practices and models needed to organise and measure the preservation and stewardship processes.The Data Stewardship Reference Model has been defined to give an overview and a way to help the data owners and space agencies in order to preserve and curate the space datasets to be ready for long time data series composition and analysis.