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Study of Emission-Line
Galaxies with
Integral-Field Spectroscopy

SELGIFS Data Challenge

The main goal of this project is to provide the scientific community with a reliable set of synthetic IFS observations, obtained from hydrodynamical simulations of galaxies in a cosmological context, together with maps of physical properties, directly measured on the simulations, that allow to evaluate the merits and drawbacks of different strategies that may be followed in order to infer the physical properties of real galaxies from IFS observations.

In particular, we have generated a set of synthetic observations mimicking the properties of the Calar Alto Legacy Integral Field Area (CALIFA) survey. Our datacubes include stellar and nebular emission, kinematic broadening of the lines, dust extinction and scattering. We have mimicked CALIFA in terms of data format, field of view size, spatial and spectral resolution, range and sampling, and we have added random detector noise based on a simple characterization of a sample of 20 galaxies. The simulated datacubes are suited to be analyzed by the same algorithms used on real observations. In order to provide a benchmark to compare the results, as well as to test the calibration and accuracy of the different analysis methods, we also provide the spatially-resolved properties of the simulations.

A thorough description of these data can be found in Guidi et al. (2017, MNRAS, submitted) . You are welcome to use them for your own purposes as long as you cite this paper and provide a link to this web page. We also encourage you to contact us in case you are interested in obtaining simulated data mimicking the properties of other IFS instruments/surveys and/or in converting your hydrodynamical simulations into CALIFA-like datacubes.

Click here to download the full dataset (~ 1 Gb!) or select individual files with the controls below. A download link will appear when the selection is complete:

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SELGIFS (Study of Emission-Line Galaxies with Integral-Field Spectroscopy)

Marie Curie Actions - International Research Staff Exchange Scheme, FP7-PEOPLE-2013-IRSES-612701


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