A new methodology to test galaxy formation models using the dependence of clustering on stellar mass

• Published in: Monthly notices of the Royal Astronomical Society 2015-09, Vol.452 (1), p.852-871
• Main Authors: Campbell, David J. R., Baugh, Carlton M., Mitchell, Peter D., Helly, John C., Gonzalez-Perez, Violeta, Lacey, Cedric G., Lagos, Claudia del P., Simha, Vimal, Farrow, Daniel J.
• Format: Article
• Language: English
• Subjects: Anisotropy; Astronomical models; Clustering; Cosmology; Dark matter; Estimates; Galaxy formation; Mathematical analysis; Mathematical models; Methodology; Simulation; Star & galaxy formation; Stellar mass
• ISSN: 0035-8711; 1365-2966
• DOI: 10.1093/mnras/stv1315

We present predictions for the two-point correlation function of galaxy clustering as a function of stellar mass, computed using two new versions of the galform semi-analytic galaxy formation model. These models make use of a high resolution, large volume N-body simulation, set in the 7-year Wilkinson Microwave Anisotropy Probe cosmology. One model uses a universal stellar initial mass function (IMF), while the other assumes different IMFs for quiescent star formation and bursts. Particular consideration is given to how the assumptions required to estimate the stellar masses of observed galaxies (such as the choice of IMF, stellar population synthesis model, and dust extinction) influence the perceived dependence of galaxy clustering on stellar mass. Broad-band spectral energy distribution fitting is carried out to estimate stellar masses for the model galaxies in the same manner as in observational studies. We show clear differences between the clustering signals computed using the true and estimated model stellar masses. As such, we highlight the importance of applying our methodology to compare theoretical models to observations. We introduce an alternative scheme for the calculation of the merger time-scales for satellite galaxies in galform, which takes into account the dark matter subhalo information from the simulation. This reduces the amplitude of small-scale clustering. The new merger scheme offers improved or similar agreement with observational clustering measurements, over the redshift range 0