Artículo

The SELGIFS data challenge: generating synthetic observations of CALIFA galaxies from hydrodynamical simulations

Guidi, G. ; Casado, J. ; Ascasibar, Y. ; García Benito, R. ; Galbany, L. ; Sánchez Blázquez, P. ; Sánchez, S.F. ; Rosales-Ortega, F.F. ; Scannapieco, Cecilia Icon

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Fecha

09/2018

Editorial

Wiley Blackwell Publishing, Inc

Revista

Monthly Notices of the Royal Astronomical Society

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ISSN

0035-8711

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Inglés

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Resumen

In this work we present a set of synthetic observations that mimic the properties of the integral field spectroscopy (IFS) survey Calar Alto Legacy Integral-Field Area (CALIFA), generated using radiative transfer techniques applied to hydrodynamical simulations of galaxies in a cosmological context. The simulated spatially-resolved spectra include stellar and nebular emission, kinematic broadening of the lines, and dust extinction and scattering. The results of the radiative transfer simulations have been post-processed to reproduce the main properties of the CALIFA V500 and V1200 observational setups. The data has been further formatted to mimic the CALIFA survey in terms of field-of-view size, spectral range and sampling. We have included the effect of the spatial and spectral point spread functions affecting CALIFA observations, and added detector noise after characterizing it on a sample of 367 galaxies. The simulated data cubes are suited to be analysed by the same algorithms used on real IFS data. In order to provide a benchmark to compare the results obtained applying IFS observational techniques to our synthetic data cubes and test the calibration and accuracy of the analysis tools, we have computed the spatially-resolved properties of the simulations. Hence, we provide maps derived directly from the hydrodynamical snapshots or the noiseless spectra, in a way that is consistent with the values recovered by the observational analysis algorithms. Both the synthetic observations and the product data cubes are public and can be found in the collaboration website http://astro.ft.uam.es/selgifs/data_challenge/.

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