SpArcFiRe: morphological selection effects due to reduced visibility of tightly winding arms in distant spiral galaxies

Abstract

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.

Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

“…For most data types, outcomes have progressed to the insight phase, for example: enhanced understanding of human biases in classification of Galaxy Zoo project images (Cabrera‐Vives, Miller, & Schneider, ; Peng, English, Silva, Davis, & Hayes, ); determination of the evolution of the effective radius and stellar mass of KiDS (de Jong et al, ) galaxies based on photometric redshifts derived from ML (Roy et al, ); and new relationships between physical and envrionmental properties of galaxies by applying an SVM to the results of a cosmological simulation (Hui et al, ).…”

SpArcFiRe: Enhancing Spiral Galaxy Recognition Using Arm Analysis and Random Forests

“…Usually more information is gleaned from a continuous distribution than a discrete classification-in particular a user of the output can choose a confidence threshold themselves for classification that is more suitable for a certain task rather than relying on the table creator's subjective determination of where that threshold should lie. Peng et al [32], for example, used regression for a task where they needed to analyze how spirality prediction degraded as a function of image quality, a task for which classification gives limited information.…”

Enhancing Automatic Prediction of Spirality using SpArcFiRe's Spiral Arm Analysis and Random Forests

“…Usually more information is gleaned from a continuous distribution than a discrete classification -in particular a user of the output can choose a confidence threshold themselves for classification that is more suitable for a certain task rather than relying on the table creator's subjective determination of where that threshold should lie. Peng et al [20], for example, used regression for a task where they needed to analyze how spirality prediction degraded as a function of redshift, a task for which classification gives limited information.…”