Morphology in DECaLS galaxies

Using bona fide spirals and ellipticals as control samples, we assessed the discriminatory power of each index, and compared them with CNN-based T-Types and Galaxy Zoo DECaLS labels. We used the indices as input for a light gradient boosting machine (LightGBM) to obtain probabilistic classifications. Concentration is the most reliable parameter from the concentration and asymmetry and smoothness system (CAS), while asymmetry-based indices (A and S) are limited to detecting disturbed morphologies.

MEGG indices (M20, Entropy, Gini, G2) provide stronger separation and trace a gradient with T-Type. By using a simple binary (0 or 1) label for ellipticals and spirals, classifiers trained on nonparametric indices achieve high accuracy and well-calibrated probabilities, dominated by entropy, concentration, and Gini. We release the first public catalog of CA[AS]S+MEGG indices for DECaLS, together with galmex.

We combine the nonparametric indices with machine learning framework to derive spiral and elliptical separation for galaxies below z~0.15 through a probabilistic approach.