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Accurate Stellar Kinematics at Faint Magnitudes: application to the Bootes~I dwarf spheroidal galaxy

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 Added by Gerry Gilmore
 Publication date 2011
  fields Physics
and research's language is English




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We develop, implement and characterise an enhanced data reduction approach which delivers precise, accurate, radial velocities from moderate resolution spectroscopy with the fibre-fed VLT/FLAMES+GIRAFFE facility. This facility, with appropriate care, delivers radial velocities adequate to resolve the intrinsic velocity dispersions of the very faint dSph dwarf galaxies. Importantly, repeated measurements let us reliably calibrate our individual velocity errors ($0.2 leq delta_Vleq 5$ km s$^{-1}$) and directly detect stars with variable radial velocities. We show, by application to the Bootes-1 dwarf spheroidal, that the intrinsic velocity dispersion of this system is significantly below 6.5,km/s reported by previous studies. Our data favor a two-population model of Bootes-1, consisting of a majority `cold stellar component, with velocity dispersion $2.4^{+0.9}_{-0.5}$,km/s, and a minority `hot stellar component, with velocity dispersion $sim 9$,km/s, although we can not completely rule out a single component distribution with velocity dispersion $4.6^{0.8}_{-0.6}$,km/s. We speculate this complex velocity distribution actually reflects the distribution of velocity anisotropy in Bootes-1, which is a measure of its formation processes.



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99 - Anna Frebel 2016
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We address the problem of separating stars from galaxies in future large photometric surveys. We focus our analysis on simulations of the Dark Energy Survey (DES). In the first part of the paper, we derive the science requirements on star/galaxy separation, for measurement of the cosmological parameters with the Gravitational Weak Lensing and Large Scale Structure probes. These requirements are dictated by the need to control both the statistical and systematic errors on the cosmological parameters, and by Point Spread Function calibration. We formulate the requirements in terms of the completeness and purity provided by a given star/galaxy classifier. In order to achieve these requirements at faint magnitudes, we propose a new method for star/galaxy separation in the second part of the paper. We first use Principal Component Analysis to outline the correlations between the objects parameters and extract from it the most relevant information. We then use the reduced set of parameters as input to an Artificial Neural Network. This multi-parameter approach improves upon purely morphometric classifiers (such as the classifier implemented in SExtractor), especially at faint magnitudes: it increases the purity by up to 20% for stars and by up to 12% for galaxies, at i-magnitude fainter than 23.
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