The Galaxy–Halo Connection for 1.5\lesssim z\lesssim 5 as Revealed by the Spitzer Matching Survey of the UltraVISTA Ultra-deep Stripes

William I. Cowley, Karina I. Caputi, Smaran Deshmukh, Matthew L. N. Ashby, Giovanni G. Fazio, Olivier Le Fèvre, Johan P. U. Fynbo, Olivier Ilbert, Henry J. McCracken, Bo Milvang-Jensen, Rachel S. Somerville

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The Spitzer Matching Survey of the UltraVISTA ultra-deep Stripes (SMUVS) provides unparalleled depth at 3.6 and 4.5 μm over ∼0.66 deg2 of the COSMOS field, allowing precise photometric determinations of redshift and stellar mass. From this unique data set we can connect galaxy samples, selected by stellar mass, to their host dark matter halos for 1.5<z<5.0, filling in a large hitherto unexplored region of the parameter space. To interpret the observed galaxy clustering, we use a phenomenological halo model, combined with a novel method to account for uncertainties arising from the use of photometric redshifts. We find that the satellite fraction decreases with increasing redshift and that the clustering amplitude (e.g., comoving correlation length/large-scale bias) displays monotonic trends with redshift and stellar mass. Applying ΛCDM halo mass accretion histories and cumulative abundance arguments for the evolution of stellar mass content, we propose pathways for the coevolution of dark matter and stellar mass assembly. Additionally, we are able to estimate that the halo mass at which the ratio of stellar-to-halo mass is maximized is {10}{12.5-0.08+0.10} {M}ȯ at z∼ 2.5. This peak halo mass is here inferred for the first time from stellar mass-selected clustering measurements at z≳ 2, and it implies a mild evolution of this quantity for z≲ 3, consistent with constraints from abundance-matching techniques.
Original languageEnglish
Article number69
Number of pages21
JournalThe Astrophysical Journal
Issue number1
Publication statusPublished - 24-Jan-2018


  • galaxies: evolution
  • galaxies: formation
  • galaxies: high-redshift
  • large-scale structure of universe
  • methods: statistical

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