The main Legacy of GOGREEN will be to provide the high-density complement to wide-field redshift surveys, extending our knowledge of galaxy evolution to a more complete range of environments. The timing of our survey is well suited for many upcoming surveys, like eRosita and Euclid, which will also sample large amounts of high-redshift clusters with wide-field imaging, but require the type of data GOGREEN will provide for calibration of observable quantities.
The low-luminosity Active Galactic Nucleus (AGN) content of clusters is poorly constrained at any redshift, and completely unknown at the GOGREEN redshift range. Thus, the fraction of these galaxies in optically selected clusters is important for understanding the X-ray selection function used in cluster cosmology surveys like eRosita and XMM-XXL. Given that AGN activity is linked to star formation activity; measuring the galaxies’ environmental dependence provides an independent look at how they would respond to a change in their gas supply. The importance of large samples and well-understood selection effects are proven to be essential for understanding AGN-triggering mechanisms. GOGREEN will also aid advancements in this field with spectroscopic data that will make it possible to measure the velocity and phase-space distribution of the AGN population.
An additional byproduct of our survey will be spectroscopic redshifts for > 600 faint field galaxies at 1.0 < z < 1.5, with homogeneous and well-understood selection criteria. Particularly at z > 1.3, none of the existing wide-field spectroscopic surveys have enough red sensitivity enough to match GOGREEN.
Comparing with the Gemini Deep Deep Survey (GDDS), GOGREEN will have:
♦ Double the size
♦ An additional half magnitude in depth
♦ Unparalleled spectroscopic measurements of galaxy mass functions, separated by galaxy type
GOGREEN will provide a crucial calibration sample for photometric redshifts out to z = 1.5, leaving behind a scientific value that will be extensively used in the future by numerous collaborations (like the ones mentioned above, and moreover, LSST and PanStarrs).