A Direct Measurement of Major Galaxy Mergers at z < 3

Astronomy and Astrophysics – Astrophysics

Scientific paper

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Accepted to the Astron. Journal, 42 pages with 17 figures, some are in color. Please see http://www.astro.caltech.edu/~cc/merg

Scientific paper

10.1086/377318

This paper presents direct evidence for hierarchical galaxy assembly out to redshifts z ~ 3. We identify major mergers using the model-independent CAS (concentration, asymmetry, clumpiness) physical morphological system on galaxies detected, and photometrically selected, in the WFPC2 and NICMOS Hubble Deep Field North. We specifically use the asymmetric distributions of rest-frame optical light measured through the asymmetry parameter (A) to determine the fraction of galaxies undergoing major mergers as a function of redshift (z), stellar mass (M_{*}), and absolute magnitude (M_{B}). We find that the fraction of galaxies consistent with undergoing a major merger increases with redshift for all galaxies, but most significantly, at 5 - 10 sigma confidence, for the most luminous and massive systems. The highest merger fractions we find are 40% - 50% for galaxies with M_{B} < -21, or M_{*} > 10^10 M_0 at z > 2.5, i.e., objects identified as Lyman-break galaxies. We model the merger fraction evolution in the form: f x (1+z)^m, finding m values ~4-6 for the most luminous and massive galaxies. We use these merger fractions combined with merger time scales calculated from N-body simulations to derive galaxy merger rates to z ~ 3. We also use stellar masses of HDF-N galaxies to determine the mass accretion rate of field galaxies involved in major mergers, and how this changes with redshift. We further find that the fraction of stellar mass density in galaxies involved in major mergers increases with redshift. By comparing merger fractions predicted in Cold Dark Matter semi-analytic models with our results there is a reasonably good agreement for the largest and brightest systems, although there are more low-mass galaxy mergers at lower redshifts than what these models predict. (abridged)

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