The COSMOS2015 galaxy stellar mass function Thirteen billion years of stellar mass assembly in ten snapshots

We measure the stellar mass function (SMF) and stellar mass density of galaxies in the COSMOS field up to z similar to 6. We select them in the near-IR bands of the COSMOS2015 catalogue, which includes ultra-deep photometry from UltraVISTA-DR2, SPLASH, and Subaru/Hyper Suprime-Cam. At z > 2.5 we use new precise photometric redshifts with error sigma(z) = 0.03(1 + z) and an outlier fraction of 12%, estimated by means of the unique spectroscopic sample of COSMOS (similar to 100 000 spectroscopic measurements in total, more than one thousand having robust z(spec) > 2.5). The increased exposure time in the DR2, along with our panchromatic detection strategy, allow us to improve the completeness at high z with respect to previous UltraVISTA catalogues (e.g. our sample is > 75% complete at 10(10) M circle dot and z = 5). We also identify passive galaxies through a robust colourcolour selection, extending their SMF estimate up to z = 4. Our work provides a comprehensive view of galaxy-stellar-mass assembly between z = 0.1 and 6, for the first time using consistent estimates across the entire redshift range. We fit these measurements with a Schechter function, correcting for Eddington bias. We compare the SMF fit with the halo mass function predicted from Lambda CDM simulations, finding that at z > 3 both functions decline with a similar slope in thehigh-mass end. This feature could be explained assuming that mechanisms quenching star formation in massive haloes become less effective at high redshifts; however further work needs to be done to confirm this scenario. Concerning the SMF low-mass end, it shows a progressive steepening as it moves towards higher redshifts, with a decreasing from -1.47(-0.02)(+0.02) at z similar or equal to 0.1 to -2.11(-0.13)(+0.30) at z similar or equal to 5. This slope depends on the characterisation of the observational uncertainties, which is crucial to properly remove the Eddington bias. We show that there is currently no consensus on the method to quantify such errors: different error models result in different best-fit Schechter parameters.