科研之友
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摘要
The Galaxy Zoo project has provided a plethora of valuable morphological data on a large number of galaxies from various surveys, and their team members have identified and/or corrected for many biases. Here we study a new bias related to spiral arm pitch angles, which first requires selecting a sample of spiral galaxies that show observable structure. One obvious way is to select galaxies using a threshold in spirality, which we define as the fraction of Galaxy Zoo humans who have reported seeing spiral structure. Using such a threshold, we use the automated tool SpArcFiRe (SPiral ARC FInder and REporter) to measure spiral arm pitch angles. We observe that the mean pitch angle of spiral arms increases linearly with redshift for 0.05 < z < 0.085. We hypothesize that this is a selection effect due to tightly wound arms becoming less visible as image quality degrades, leading to fewer such galaxies being above the spirality threshold as redshift increases. We corroborate this hypothesis by first artificially degrading images of nearby galaxies, and then using a machine learning algorithm trained on Galaxy Zoo data to provide a spirality for each artificially degraded image. We find that SpARcFiRe's ability to accurately measure pitch angles decreases as the image degrades, but that spirality decreases more quickly in galaxies with tightly wound arms, leading to the selection effect. This new bias means one must be careful in selecting a sample on which to measure spiral structure. Finally, we also include a sensitivity analysis of SpArcFiRe's internal parameters.
- 出版日期2018-10
