Beyond Spheroids and Discs: Classifications of CANDELS Galaxy Structure at 1.4 < z < 2 via Principal Component Analysis
Michael A. Peth, Jennifer M. Lotz, Peter E. Freeman, Conor McPartland,, S. Alireza Mortazavi, Gregory F. Snyder, Guillermo Barro, Norman A. Grogin,, Yicheng Guo, Shoubaneh Hemmati, Jeyhan S. Kartaltepe, Dale D. Kocevski, Anton, M. Koekemoer, Daniel H. McIntosh, Hooshang Nayyeri

TL;DR
This study employs principal component analysis on multiple morphological indicators of galaxies at redshift 1.4 to 2 to develop a nuanced classification system that better captures galaxy structures and their relation to star formation.
Contribution
It introduces a PCA-based classification scheme that surpasses traditional methods by distinguishing subtle galaxy structural differences and correlating them with quiescence and star formation activity.
Findings
PCA captures 75% of morphological variance.
PC1 and PC2 predict galaxy quiescence.
New classification separates compact and clumpy galaxies.
Abstract
Important but rare and subtle processes driving galaxy morphology and star-formation may be missed by traditional spiral, elliptical, irregular or S\'ersic bulge/disk classifications. To overcome this limitation, we use a principal component analysis of non-parametric morphological indicators (concentration, asymmetry, Gini coefficient, , multi-mode, intensity and deviation) measured at rest-frame -band (corresponding to HST/WFC3 F125W at 1.4 2) to trace the natural distribution of massive () galaxy morphologies. Principal component analysis (PCA) quantifies the correlations between these morphological indicators and determines the relative importance of each. The first three principal components (PCs) capture 75 per cent of the variance inherent to our sample. We interpret the first principal component (PC) as bulge strength, the second PC…
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