Characterization of Residual Morphological Substructure Using Supervised and Unsupervised Deep Learning

TL;DR

This study investigates the application of deep learning frameworks to characterize different galactic substructures hosted within parametric light-profile subtracted ``residual''images of a large sample galaxies from the CANDELS survey and finds that the supervised CNN latent features in PCA space correlate with the SPF values and distinguish between qualitatively strong and weak residual substructures.

Abstract

Automated characterization of galactic substructure is an essential step in understanding the transformative physical processes driving galaxy evolution. In this study, we investigate the application of deep learning (DL) frameworks to characterize different galactic substructures hosted within parametric light-profile subtracted ``residual'' images of a large sample galaxies from the CANDELS survey. We develop a supervised Convolutional Neural Network (CNN) and unsupervised Convolutional Variational Autoencoder (CvAE) and train it on the single-Sérsic profile fitting based residual images of $10,046$ bright and massive galaxies ($H<24.5\,{\rm mag}$ and $M_{\rm stellar} \geq 10^{9.5}\,M_{\odot}$) spanning $1<z<3$, in conjunction with their visual-based classification labels indicating the nature of residual substructures hosted within them. Using our unique data preprocessing approach, we prepare our residual images such that the inputs to our DL networks comprise only ``galaxy of interest'', and augment them such that our sample span uniformly across different residual characteristics. We assess the latent space of the CNN and CvAE using Principle Component Analysis (PCA) along with independently quantified metrics of residual strength (significant pixel flux $SPF$, Bumpiness, and Residual Flux Fraction). We also employ an unsupervised Gaussian Mixture Modeling (GMM) based clustering scheme with Support Vector Classification (SVC) to identify groupings in PCA space that correspond to similar residual substructure. We find that our supervised CNN latent features in PCA space correlate with the $SPF$ values and distinguish between qualitatively strong and weak residual substructures. While our unsupervised CvAE latent space also correlates with visual and quantitative residual characteristics, but lacks clear discriminatory power when characterizing different residual substructures.

Figures (16)

• Figure 1: Visualization of the H-band original images (left in each column pair) and their corresponding residual images (right panel in each column pair) for example CANDELS galaxies from our main sample, generated by performing single-Sérsic light-profile fitting using GALFITPeng02 by van_der_wel_12.
• Figure 2: An example view of our galaxies and their hosted residual substructure. In each column pairs, we show HST WFC3/F160W H-band original images (left) and their corresponding GALFIT-based residual images from van_der_wel_12 (right) for five example galaxies per class.
• Figure 3: Visual illustration of our preprocessing step to prepare our residual images for the DL framework. We show the original image, source extraction based segmentation map highlighting the detected sources, residual image, region of interest corresponding the central galaxy in the image, and the GOI region of the residual image with a $50\,{\rm pix}\times 50\,{\rm pix}$ postage stamp size for reference.
• Figure 4: The histograms of our five residual characteristic classes among our parent sample of $\sim 10,000$ galaxies (black), training sub-sample (red), testing sub-sample (blue), and augmented training data sets (magenta). For each class, we also show its fractional contribution to the data set with respective colored text.
• Figure 5: Example visualization of our data augmentation step (see § \ref{['sec:train_test']}). For three example residual images from our sample, we show five random horizontal flip and $45\deg$ rotation steps.