Abstract

The Galaxy Zoo has provided morphological data on many galaxies. Several biases have been identified in the Galaxy Zoo data. Here we report on a newly discovered selection effect: astronomers interested in studying spiral galaxies may select a set of spiral galaxies based upon a threshold in spirality (the fraction of Galaxy Zoo humans who report seeing spiral structure). SpArcFiRe is an automated tool that decomposes a spiral galaxy into its constituent spiral arms, providing objective, quantitative data on their structure. SpArcFiRe measures the pitch angle of spiral arms. We have observed that when selecting a set of spiral galaxies based on a threshold on spirality, the pitch angle of spiral arms appear increase with redshift. We hypothesize that this is a selection effect: tightly-wound spiral arms become less visible as images degrade 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 spirality decreases as image quality degrades. Thus, the mean pitch angle of those galaxies remaining above the 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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