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Register a new userA LAMOST BHB Catalog and Kinematics Therein I: Catalog and Halo Properties
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We collect a sample of stars observed both in LAMOST and Gaia which have colors implying a temperature hotter than 7000 K. We train a machine learning algorithm on LAMOST spectroscopic data which has been tagged with stellar classifications and metallicities, and use this machine to construct a catalog of Blue Horizontal Branch stars (BHBs) with metallicity information. Another machine is trained using Gaia parallaxes to predict absolute magnitudes for these stars. The final catalog of 13,693 BHBs is thought to be about 86% pure, with $sigma_{[Fe/H]}sim$0.35 dex and $sigma_{G}sim$0.31 mag. These values are confirmed via comparison to globular clusters, although a covariance error seems to affect our magnitude and abundance estimates. We analyze a subset of this catalog in the Galactic Halo. We find that BHB populations in the outer halo appear redder, which could imply a younger population, and that the metallicity gradient is relatively flat around [Fe/H] = -1.9 dex over our sample footprint. We find that our metal rich BHB stars are on more radial velocity dispersion dominated orbits ($beta sim 0.70$) at all radii than our metal poor BHB stars ($beta sim 0.62$).
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Wolf-Rayet (WR) galaxies are a rare population of galaxies that host living high-mass stars during their WR phase (i.e. WR stars) and are thus expected to provide interesting constraints on the stellar Initial Mass Function, massive star formation, stellar evolution models, etc. Spatially resolved spectroscopy should in principle provide a more efficient way of identifying WR galaxies than single-fiber surveys of galactic centers such as SDSS-I & II, as WR stars should be more preferentially found in discs. Using IFU data from the ongoing SDSS-IV MaNGA survey, we have performed a thorough search for WR galaxies. We first identify H II regions in each datacube and carry out full spectral fitting to the stacked spectra. We then visually inspect the residual spectrum of each H II region and identify WR regions that present a significant blue bump at 4600-4750 A. The resulting WR catalog includes 267 WR regions of ~500pc (radius) sizes, distributed in 90 galaxies from the current sample of MaNGA (MaNGA Product Launch 7). We find WR regions are exclusively found in galaxies that show bluest colors and highest star formation rates for their mass. Most WR galaxies have late-type morphologies and show relatively large asymmetry in their images, implying that WR regions are more preferentially found in interacting/merging galaxies. We estimate the stellar mass function of WR galaxies and the mass-dependent detection rate. The detection rate of WR galaxies is typically ~2%, with weak dependence on stellar mass. This detection rate is about 40 times higher than previous studies with SDSS single fiber data, and by a factor of 2 lower than the CALIFA-based WR catalog. We make comparisons with SDSS and CALIFA studies, and conclude that different detection rates can be explained mainly by three factors: spatial coverage, spectral signal-to-noise ratio, and redshift ranges of the parent sample.
We present a spectroscopic catalog of 67082 M dwarfs from the LAMOST pilot survey. For each spectrum of the catalog, spectral subtype, radial velocity, equivalent width of H${alpha}$, a number of prominent molecular band indices and the metal sensitive parameter $zeta$ are provided . Spectral subtype have been estimated by a remedied Hammer program (Original Hammer: Covey et al. 2007), in which indices are reselected to obtain more accurate auto-classified spectral subtypes. All spectra in this catalog have been visually inspected to confirm the spectral subtypes. Radial velocities have been well measured by our developed program which uses cross-correlation method and estimates uncertainty of radial velocity as well. We also examine the magnetic activity properties of M dwarfs traced by H${alpha}$ emission line. The molecular band indices included in this catalog are temperature or metallicity sensitive and can be used for future analysis of the physical properties of M dwarfs. The catalog is available on the website url{http://sciwiki.lamost.org/MCatalogPilot/}.
RV variable stars are important in astrophysics. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) spectroscopic survey has provided ~ 6.5 million stellar spectra in its Data Release 4 (DR4). During the survey, ~ 4.7 million unique sources were targeted and ~ 1 million stars observed repeatedly. The probabilities of stars being RV variables are estimated by comparing the observed radial velocity variations with the simulated ones. We build a catalog of 80,702 RV variable candidates with probability greater than 0.60 by analyzing the duplicate-observed multi-epoch sources covered by the LAMOST DR4. Simulations and cross-identifications show that the purity of the catalog is higher than 80%. The catalog consists of 77% binary systems and 7% pulsating stars as well as 16% pollution by single stars. 3,138 RV variables are classified through cross-identifications with published results in literatures. By using the 3,138 sources common to both LAMOST and a collection of published RV variable catalogs we are able to analyze LAMOSTs RV variable detection rate. The efficiency of the method adopted in this work relies not only on the sampling frequency of observations but also periods and amplitudes of RV variables. With the progress of LAMOST, Gaia and other surveys, more and more RV variables would will be confirmed and classified. This catalog is valuable for other large-scale surveys, especially for RV variable searches. The catalog will be released according to the LAMOST Data Policy via http://dr4.lamost.org.
Context: In 6 years of operation, INTEGRAL/ISGRI revealed more than 500 sources. Many of these sources are variable. Taking into account that nearly half of INTEGRAL/ISGRI sources are new and many of them are still unidentified, the variability properties of the sources can serve as additional parameters that may help to classify and identify the unknown sources. Aims: In order to study the variability properties of the sources detected by INTEGRAL/ISGRI we develop a method to quantify the variability of a source. We describe here our techniques and compile a catalog of the sources that fit our criteria of variability. Methods: We use the natural time binning of INTEGRAL observations called Science Window ($approx 2000$ seconds) and test the hypothesis that the detected sources are constant using a $chi^2$ all-sky map in three energy bands (20-40, 40-100, 100-200 keV). We calculate an intrinsic variance of the flux in individual pixels and use it to define the fractional variability of a source. The method is sensitive to the source variability on time scales of one Science Window and higher. We concentrate only on the sources which were already reported to be detected by INTEGRAL. Results: We present a catalog of 202 sources which are found to be significantly variable. For the catalog sources we give the measure of variability and fluxes with corresponding errors in 20-40, 40-100, 100-200 keV energy bands, and we present some statistics about the population of variable sources. The description of the physical properties of the variable sources will be given in a forthcoming paper.
In order to better understand the role of high-mass stellar feedback in regulating star formation in giant molecular clouds, we carried out a Hubble Space Telescope (HST) Treasury Program Measuring Young Stars in Space and Time (MYSST) targeting the star-forming complex N44 in the Large Magellanic Cloud (LMC). Using the F555W and F814W broadband filters of both the ACS and WFC3/UVIS, we built a photometric catalog of 461,684 stars down to $m_mathrm{F555W} simeq 29$ mag and $m_mathrm{F814W} simeq 28$ mag, corresponding to the magnitude of an unreddened 1 Myr pre-main-sequence star of $approx0.09$ $M_odot$ at the LMC distance. In this first paper we describe the observing strategy of MYSST, the data reduction procedure, and present the photometric catalog. We identify multiple young stellar populations tracing the gaseous rim of N44s super bubble, together with various contaminants belonging to the LMC field population. We also determine the reddening properties from the slope of the elongated red clump feature by applying the machine learning algorithm RANSAC, and we select a set of Upper Main Sequence (UMS) stars as primary probes to build an extinction map, deriving a relatively modest median extinction $A_{mathrm{F555W}}simeq0.77$ mag. The same procedure applied to the red clump provides $A_{mathrm{F555W}}simeq 0.68$ mag.
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