The Gaia-ESO survey: Matching chemodynamical simulations to observations of the Milky Way

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Miniatura
Fecha
2018-01
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
ISSN de la revista
Título del volumen
Editor
Oxford University Press
Nombre de Curso
Licencia CC
CC BY 4.0
Licencia CC
Resumen
The typical methodology for comparing simulated galaxies with observational surveys is usually to apply a spatial selection to the simulation to mimic the region of interest covered by a comparable observational survey sample. In this work, we compare this approach with a more sophisticated post-processing in which the observational uncertainties and selection effects (photometric, surface gravity and effective temperature) are taken into account. We compare a 'solar neighbourhood analogue' region in a model MilkyWay-like galaxy simulated with RAMSES-CH with fourth release Gaia-ESO survey data. We find that a simple spatial cut alone is insufficient and that the observational uncertainties must be accounted for in the comparison. This is particularly true when the scale of uncertainty is large compared to the dynamic range of the data, e.g. in our comparison, the [Mg/Fe] distribution is affected much more than the more accurately determined [Fe/H] distribution. Despite clear differences in the underlying distributions of elemental abundances between simulation and observation, incorporating scatter to our simulation results to mimic observational uncertainty produces reasonable agreement. The quite complete nature of the Gaia-ESO survey means that the selection function has minimal impact on the distribution of observed age and metal abundances but this would become increasingly more important for surveys with narrower selection functions. © 2017 The Author(s).
Notas
Indexación Scopus
Palabras clave
Gaia, Biomechanics, LAMOST, Galaxies: evolution, Galaxies: formation, Galaxy: abundances, Methods: numerical
Citación
Monthly Notices of the Royal Astronomical Society Volume 473, Issue 1, Pages 185 - 1971 January 2018
DOI
10.1093/MNRAS/STX2316
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