The Galaxy Zoo Catalogs for the Galaxy And Mass Assembly (GAMA) Survey
Benne W. Holwerda (University of Louisville), Clayton Robertson, (University of Louisville), Kyle Cook (University of Louisville), Kevin A., Pimbblet (University of Hull), Sarah Casura (Universit\"at Hamburg), Anne E., Sansom (University of Central Lancashire)

TL;DR
This paper compares galaxy morphology classifications from Galaxy Zoo across DESI and KiDS surveys, validating the consistency of citizen science data despite imaging differences and demonstrating the potential for transfer learning.
Contribution
It provides a cross-survey validation of Galaxy Zoo classifications, highlighting the impact of imaging quality and confirming the reliability of the Zoobot model for transfer learning.
Findings
Global agreement in classifications with some scatter
Higher smooth galaxy votes in DESI due to shallower imaging
Good agreement with expert visual classifications
Abstract
Galaxy Zoo is an online project to classify morphological features in extra-galactic imaging surveys with public voting. In this paper, we compare the classifications made for two different surveys, the Dark Energy Spectroscopic Instrument (DESI) imaging survey and a part of the Kilo-Degree Survey (KiDS), in the equatorial fields of the Galaxy And Mass Assembly (GAMA) survey. Our aim is to cross-validate and compare the classifications based on different imaging quality and depth. We find that generally the voting agrees globally but with substantial scatter i.e. substantial differences for individual galaxies. There is a notable higher voting fraction in favor of ``smooth'' galaxies in the DESI+\rev{{\sc zoobot}} classifications, most likely due to the difference between imaging depth. DESI imaging is shallower and slightly lower resolution than KiDS and the Galaxy Zoo images do not…
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Taxonomy
TopicsAstronomical Observations and Instrumentation · Astronomy and Astrophysical Research · Stellar, planetary, and galactic studies
