The Galaxy Zoo project has provided a plethora of valuable morphological data on a large number of galaxies from various surveys. Several biases have been identified in the Galaxy Zoo data, which users of the data must be aware. Here we report on a newly discovered selection effect. In particular, astronomers interested in studying spiral galaxies may select a set of spiral galaxies based upon a threshold in spirality, which we define as the fraction of Galaxy Zoo humans who have reported seeing spiral structure. One tool that can be used to analyze spiral galaxies is SpArcFiRe, an automated tool that decomposes a spiral galaxy into its constituent spiral arms, providing objective, quantitative data on their structure. One of SpArcFiRe's measures is the pitch angle of spiral arms. We have observed that, when selecting a set of spiral galaxies based on a threshold on Galaxy Zoo spirality, the spiral arms appear to have a mean pitch angle that very clearly increases linearly with redshift for 0.05 z 0.085 even after accounting for the Malmquist bias. We hypothesize that this is a selection effect, based on the fact that tightly-wound spiral arms become less visible as spatial resolution and noise degrade the image with increasing redshift, leading to fewer such galaxies being included in the sample at higher redshifts. We corroborate this hypothesis by artificially degrading images of nearby galaxies, then using a machine learning algorithm trained on Galaxy Zoo data to provide a spirality for each artificially degraded image. It correctly predicts that the detected spirality of a fixed galaxy decreases as image quality degrades. We then use these spiralities to corroborate the hypothesis that the mean pitch angle of those galaxies remaining above a fixed spirality threshold is higher than those eliminated by the selection effect. This demonstrates that users who select samples of galaxies using a threshold of Galaxy Zoo votes must carefully consider the possibility of selection effects on morphological measures, even if the measure itself is believed to be objective and unbiased. Finally, we also perform an empirical sensitivity analysis to demonstrate that SpArcFiRe's output changes in a smooth and predictable fashion to changes in its internal algorithmic parameters.
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