Galaxy Zoo DESI: Detailed Morphology Measurements for 8.7M Galaxies in the DESI Legacy Imaging Surveys

TL;DR

This paper introduces automated deep learning-based morphology measurements for 8.7 million galaxies in the DESI Legacy Imaging Surveys, significantly expanding sky coverage and enabling detailed galaxy classification.

Contribution

It presents a new large-scale, automated galaxy morphology measurement method trained on Galaxy Zoo votes, extending coverage and improving classification accuracy.

Findings

Predictions within 5-10 ext% of volunteer votes for each GZ question.

Sky coverage increased by a factor of 4, from 5,000 to 19,000 deg$^2$.

Enables overlap with surveys like ALFALFA and MaNGA.

Abstract

We present detailed morphology measurements for 8.67 million galaxies in the DESI Legacy Imaging Surveys (DECaLS, MzLS, and BASS, plus DES). These are automated measurements made by deep learning models trained on Galaxy Zoo volunteer votes. Our models typically predict the fraction of volunteers selecting each answer to within 5-10\% for every answer to every GZ question. The models are trained on newly-collected votes for DESI-LS DR8 images as well as historical votes from GZ DECaLS. We also release the newly-collected votes. Extending our morphology measurements outside of the previously-released DECaLS/SDSS intersection increases our sky coverage by a factor of 4 (5,000 to 19,000 deg$^2$) and allows for full overlap with complementary surveys including ALFALFA and MaNGA.