Warped Disk Galaxies: Statistical Properties from DESI Legacy Imaging Surveys DR8

TL;DR

This study uses deep learning to classify and analyze a large sample of warped edge-on disk galaxies from DESI surveys, revealing their distinct properties and environmental dependencies, thus advancing understanding of disk warp formation.

Contribution

It presents the largest catalog of warped disk galaxies using deep learning classification, and statistically links warp occurrence to galaxy and environmental properties.

Findings

Warped galaxies are bluer, less massive, and have higher gas fractions.

Warped galaxies are found in denser environments, especially within 50 kpc.

Warped galaxies tend to have larger sizes and different structural parameters.

Abstract

Warped structures are often observed in disk galaxies, yet their physical origin is still under investigation. We present a systematic study of warped edge-on disk galaxies based on imaging data from the DESI Legacy Imaging Surveys DR8, with the expectation that this large sample size, enabled by wide-area surveys, will offer new perspectives on the formation of disk warps. Using a deep learning approach, we trained an EfficientNet-B3 convolutional neural network to classify the morphology of edge-on-disk galaxies into warped and non-warped categories. Our model was trained on a curated and visually verified set of labeled galaxy images and applied to a large dataset of over 595,651 edge-on disk galaxies selected from the Galaxy Zoo DESI catalog. Our results provide the largest warp catalog to date, consisting of 23996 warped edge-on disk galaxies, and reveal statistical trends between warp occurrence and galaxy properties. Compared to their non-warped counterparts, these warped disk galaxies tend to have bluer colors, lower stellar masses, higher gas fractions and star-formation rates, smaller S\'ersic indices and larger disk sizes. In addition, warped disk galaxies show higher projected number densities of neighboring galaxies than their non-warped counterparts, particularly within \( R_{\mathrm{proj}} \lesssim 50~\mathrm{kpc} \), where the local number density is roughly twice as high.