Galaxy spectra are essential to probe the spatial distribution of galaxies in our Universe. To better interpret current and future spectroscopic galaxy redshift surveys, it is important to be able to simulate these data sets. We describe USPEC, a forward modeling tool to generate galaxy spectra taking into account some intrinsic galaxy properties as well as instrumental responses of a given telescope. The model for the intrinsic properties of the galaxy population, i.e., the luminosity functions, and size and spectral coefficients distributions, was developed in an earlier work for broad-band imaging surveys , and we now aim to test the model further using spectroscopic data. We apply USPEC to the SDSS/CMASS sample of Luminous Red Galaxies (LRGs). We construct selection cuts that match those used to build this LRG sample, which we then apply to data and simulations in the same way. The resulting real and simulated average spectra show a good statistical agreement overall, with residual differences likely coming from a bluer galaxy population of the simulated sample. We also do not explore the impact of non-solar element ratios in our simulations. For a quantitative comparison, we perform Principal Component Analysis (PCA) of the sets of spectra. By comparing the PCs constructed from simulations and data, we find good agreement for all components. The distributions of the eigencoefficients also show an appreciable overlap. We are therefore able to properly simulate the LRG sample taking into account the SDSS/BOSS instrumental responses. The differences between the two samples can be ascribed to the intrinsic properties of the simulated galaxy population, which can be reduced by further improvements of our modeling method in the future. We discuss how these results can be useful for the forward modeling of upcoming large spectroscopic surveys.
- galaxy surveys
- redshift surveys
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Fagioli, M. (Creator), Riebartsch, J. (Creator), Nicola, A. (Creator), Herbel, J. (Creator), Amara, A. (Creator), Refregier, A. (Creator), Chang, C. (Creator), Gamper, L. (Creator) & Tortorelli, L. (Creator), IOP Publishing, 12 Nov 2018