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Structure and Assembly of the Most Massive Galaxies Present at z ∼ 2 − 3

  • Timothy Weinzirl
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Part of the Springer Theses book series (Springer Theses)

Abstract

We present our results on the structure and activity of massive galaxies at \(z = 1 - 3\) using one of the largest (166 with M ≥ 5 × 1010 M ) and most diverse samples of massive galaxies derived from the GOODS-NICMOS survey: (1) Sérsic fits to deep NIC3 F160W images indicate that the rest-frame optical structures of massive galaxies are very different at \(z = 2 - 3\) compared to z ∼ 0. Approximately 40 % of massive galaxies are ultra-compact (r e ≤ 2 kpc), compared to less than 1 % at z ∼ 0. Furthermore, most ( ∼ 65 %) systems at \(z = 2 - 3\) have a low Sérsic index n ≤ 2, compared to ∼ 13 % at z ∼ 0. We present evidence that the n ≤ 2 systems at \(z = 2 - 3\) likely contain prominent disks, unlike most massive z ∼ 0 systems. (2) There is a correlation between structure and star formation rates (SFR). The majority ( ∼ 85 %) of non-AGN massive galaxies at \(z = 2 - 3\), with SFR high enough to yield a 5σ (30 μJy) 24 μm Spitzer detection have low n ≤ 2. Such n ≤ 2 systems host the highest SFR. (3) The frequency of AGN is ∼ 40 % at \(z = 2 - 3\). Most ( ∼ 65 %) AGN hosts have disky (n ≤ 2) morphologies. Ultra-compact galaxies appear quiescent in terms of both AGN activity and star formation. (4) Large stellar surface densities imply massive galaxies at \(z = 2 - 3\) formed via rapid, highly dissipative events at z > 2. The large fraction of n ≤ 2 disky systems suggests cold mode accretion complements gas-rich major mergers at z > 2. In order for massive galaxies at \(z = 2 - 3\) to evolve into present-day massive E/S0s, they need to significantly increase (n, r e ). Dry minor and major mergers may play an important role in this process.

Keywords

Star Formation Point Spread Function Active Galactic Nucleus Surface Brightness Stellar Mass 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Timothy Weinzirl
    • 1
  1. 1.Department of AstronomyUniversity of Texas at AustinAustinUSA

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