Galaxy Morphology in CANDELS: Addressing Evolutionary Changes Across $0.2 \leq z \leq 2.4$ with Hybrid Classification Approach
I. Kolesnikov, V. M. Sampaio, R. R. de Carvalho, C. Conselice

TL;DR
This study introduces a hybrid classification method for galaxy morphology across redshifts 0.2 to 2.4, revealing consistent morphological fractions and highlighting biases in previous visual classifications, with implications for galaxy evolution understanding.
Contribution
A novel hybrid supervised-unsupervised approach applied in redshift bins improves galaxy classification accuracy and addresses biases in prior studies.
Findings
Galaxy morphology fractions are nearly constant across redshifts.
Flux dimming and angular scale effects can misclassify up to 18% of galaxies.
Massive spheroids increase with decreasing redshift, indicating galaxy merging processes.
Abstract
Morphological classification of galaxies becomes increasingly challenging with redshift. We apply a hybrid supervised-unsupervised method to classify galaxies in the CANDELS fields at into spheroid, disk, and irregular systems. Unlike previous works, our method is applied to redshift bins of width 0.2. Comparison between models applied to a wide redshift range versus bin-specific models reveals significant differences in galaxy morphology beyond and a consistent disagreement. This suggests that using a single model across wide redshift ranges may introduce biases due to the large time intervals involved compared to galaxy evolution timescales. Using the FERENGI code to assess the impact of cosmological effects, we find that flux dimming and smaller angular scales may lead to the misclassification of up to of disk galaxies…
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Taxonomy
TopicsAdvanced Computational Techniques and Applications · Advanced Clustering Algorithms Research · Computational Physics and Python Applications
