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Dave Andersen

Publications and source records attributed to Dave Andersen.

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GHOST commissioning science results -- IV: Chemodynamical analyses of Milky Way satellites Sagittarius II and Aquarius II

We present Gemini/GHOST high-resolution spectra of five stars observed in two low surface brightness Milky Way satellites, Sagittarius II (Sgr2) and Aquarius II (Aqu2). For Aqu2, the velocities and metallicities of the two stars are consistent with membership in a dark matter-dominated ultra faint dwarf galaxy (UFD). The chemical abundance ratios suggest inefficient star formation from only one or a few supernovae (e.g., low Na, Sr, Ba), and enriched potassium (K) from super-AGB stars. For Sgr2, the velocity and metallicity dispersions of its members are not clearly resolved and our detailed chemical abundances show typical ratios for metal-poor stars, with low dispersions. There is only one exception - we report the discovery of an r-process enhanced star (Sgr2584, [Eu/Fe]= $+0.7 \pm 0.2 $; thus, an r-I star). As r-I stars are found in both UFDs (Tuc III, Tuc IV, Grus II) and globular clusters (M15 and M92), then this does not help to further classify the nature of Sgr2. Our exploration of Sgr2 demonstrates the difficulty in classifying some of the faintest (ambiguous) satellites. We advocate for additional diagnostics in analysing the ambiguous systems, such as exploring radial segregation (by mass and/or chemistry), N-body simulations, and the need for dark matter to survive Galactic tidal effects. The spectra analysed in this paper were taken as part of the GHOST commissioning observations, testing faint observation limits (G < 18.8) and the single and double IFU observing modes.

astro-ph.GA

Linear prediction of atmospheric wave-fronts for tomographic Adaptive Optics systems: modelling and robustness assessment

We use a theoretical frame-work to analytically assess temporal prediction error functions on von-Karman turbulence when a zonal representation of wave-fronts is assumed. Linear prediction models analysed include auto-regressive of order up to three, bilinear interpolation functions and a minimum mean square error predictor. This is an extension of the authors' previously published work (see ref. 2) in which the efficacy of various temporal prediction models was established. Here we examine the tolerance of these algorithms to specific forms of model errors, thus defining the expected change in behaviour of the previous results under less ideal conditions. Results show that +/- 100pc wind-speed error and +/- 50 deg are tolerable before the best linear predictor delivers poorer performance than the no-prediction case.

astro-ph.IM