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Identifying Mergers Using Non-parametric Morphological Classification at High Redshifts by Thompson, Robert; Davé, Romeel; Huang, Shuiyao; Katz, Neal is a scholarly article available to read on EtoBox.

What is Identifying Mergers Using Non-parametric Morphological Classification at High Redshifts about?

We investigate the time evolution of non-parametric morphological quantities and their relationship to major mergers between $4\geq z \geq 2$ in high-resolution cosmological zoom simulations of disk galaxies that implement kinetic wind feedback, $H_2$-based star formation, and minimal ISM pressurisation. We show that the resulting galaxies broadly match basic observed physical properties of $z\sim 2$ objects. We measure the galaxies' concentrations ($C$), asymmetries ($A$), and $Gini$ ($G$) and $M_{20}$ coefficients, and correlate these with major merger events identified from the mass growth history. We find that high values of asymmetry provide the best indicator for identifying major mergers of $>1:4$ mass ratio within our sample, with $Gini$-$M_{20}\,$ merger classification only as effective for face-on systems and much less effective for edge-on or randomly-oriented galaxies. The canonical asymmetry cut of $A\geq0.35$, however, is only able to correctly identify major mergers $\sim 10\%$ of the time, while a higher cut of $A\geq 0.8$ more efficiently picks out mergers at this epoch. We further examine the temporal correlation between morphological statistics and mergers, and s

Author

Thompson, Robert; Davé, Romeel; Huang, Shuiyao; Katz, Neal

Published

2015

Language

EN