A study of variable stars in the open cluster NGC 1582 and its surrounding field

This paper presents Charge-Coupled Device time-series photometric observations of the open cluster NGC 1582 and its surrounding field with Johnson B, V and R filters by using the Nanshan 1 m telescope administered by Xinjiang Astronomical Observatory. 19 variable stars and three variable candi- dates were detected in a 45＇ - 48.75＇ field around the cluster. 12 of the variable stars are newly-discovered variable objects. The physical properties, classifications and memberships of these 22 objects are studied through their light curves, their positions on the color-magnitude diagram and with archival data from the Naval Observatory Merged Astrometric Dataset. Among these objects, five are eclipsing binary systems, six are pulsating variable stars including one known 6 Scuti star and one newly-discovered RR Lyrae star. The distance to the RR Lyrae star is estimated to be 7.9 - 0.3 kpc, indicating that the star is located far behind the cluster. Four variable stars are probable members of the cluster, and 13 of the 22 objects are confirmed to be field stars.

Chemical abundance analysis of 19 barium stars

We aim at deriving accurate atmospheric parameters and chemical abundances of 19 barium（Ba） stars, including both strong and mild Ba stars, based on the high signal-to-noise ratio and high resolution Echelle spectra obtained from the 2.16 m telescope at Xinglong station of National Astronomical Observatories, Chinese Academy of Sciences. The chemical abundances of the sample stars were obtained from an LTE, plane-parallel and line-blanketed atmospheric model by inputting the atmospheric parameters（effective temperatures Teff, surface gravities log g, metallicity [Fe/H] and microturbulence velocity ξt） and equivalent widths of stellar absorption lines. These samples of Ba stars are giants as indicated by atmospheric parameters, metallicities and kinematic analysis about UVW velocity. Chemical abundances of 17 elements were obtained for these Ba stars. Their Na, Al, α- and iron-peak elements（O, Na, Mg, Al, Si, Ca,Sc, Ti, V, Cr, Mn, Ni） are similar to the solar abundances. Our samples of Ba stars show obvious overabundances of neutron-capture（n-capture） process elements relative to the Sun. Their median abundances of[Ba/Fe], [La/Fe] and [Eu/Fe] are 0.54, 0.65 and 0.40, respectively. The Y I and Zr I abundances are lower than Ba, La and Eu, but higher than the α- and iron-peak elements for the strong Ba stars and similar to the iron-peak elements for the mild stars. There exists a positive correlation between Ba intensity and [Ba/Fe].For the n-capture elements（Y, Zr, Ba, La）, there is an anti-correlation between their [X/Fe] and [Fe/H]. We identify nine of our sample stars as strong Ba stars with [Ba/Fe]〉0.6 where seven of them have Ba intensity Ba=2-5, one has Ba=1.5 and another one has Ba=1.0. The remaining ten stars are classified as mild Ba stars with 0.17〈[Ba/Fe]〈0.54.

A first-order dynamical model of hierarchical triple stars and its application

For most hierarchical triple stars, the classical double two-body model of zeroth-order cannot describe the motions of the components under the current observational accuracy. In this paper, Marchal＇s first-order analytical solution is implemented and a more efficient simplified version is applied to real triple stars. The results show that, for most triple stars, the proposed first-order model is preferable to the zerothorder model both in fitting observational data and in predicting component positions.

How to co-add images? I. A new iterative method for image reconstruction of dithered observations

By employing the previous Voronoi approach and replacing its nearest neighbor approx- imation with Drizzle in iterative signal extraction, we develop a fast iterative Drizzle algorithm, namedfiDrizzle, to reconstruct the underlying band-limited image from undersampled dithered frames. Compared with the existing iDrizzle, the new algorithm improves rate of convergence and accelerates the computational speed. Moreover, under the same conditions （e.g. the same number of dithers and iterations）, fiDrizzle can make a better quality reconstruction than iDrizzle, due to the newly discov- ered High Sampling caused Decelerating Convergence （HSDC） effect in the iterative signal extraction process.fiDrizzle demonstrates its powerful ability to perform image deconvolution from undersampled dithers.

Extracting 21 cm signal by frequency and angular filtering

Extracting the neutral hydrogen （HI） signal is a great challenge for cosmological 21 cm experiments; both the astrophysical foregrounds and receiver noise are typically several orders of magnitude greater than the 21 cm signal. However, the different properties of the 21 cm signal, foreground and noise can be exploited to separate these components. The foregrounds are generally smooth or correlated over the frequency space along a line of sight （LOS）, while both the 21 cm signal and noise vary stochastically along the same LoS. The foreground can be removed by filtering out the smooth component in frequency space. The receiver noise is basically uncorrelated for observations at different times, hence for surveys it is also uncorrelated in different directions, while the 21 cm signal, which traces the large scale structure, is correlated up to certain scales. In this exercise, we apply Wiener filters in frequency and angular space to extract the 21 cm signals. We found that the method works well. Inaccurate knowl- edge about the beam could degrade the reconstruction, but the overall result is still good, showing that the method is fairly robust.

The point spread function reconstruction by using Moffatlets–I

Shear measurement is a crucial task in current and future weak lensing survey projects. The reconstruction of the point spread function （PSF） is one of the essential steps involved in this process. In this work, we present three different methods, Gaussianlets, Moffatlets and Expectation Maximization Principal Component Analysis （EMPCA）, and quantify their efficiency on PSF reconstruction using four sets of simulated Large Synoptic Survey Telescope （LSST） star images. Gaussianlets and Moffatlets are two different sets of basis functions whose profiles are based on Gaussian and Moffat functions respectively. EMPCA is a statistical method performing an iterative procedure to find the principal components （PCs） of an ensemble of star images. Our tests show that： （1） Moffatlets always perform better than Gaussianlets. （2） EMPCA is more compact and flexible, but the noise existing in the PCs will contaminate the size and ellipticity of PSF. By contrast, Moffatlets keep the size and ellipticity very well.