findAbar: how astronomers may perceive the bar in galaxies differently

TL;DR

This study reveals significant variability among astronomers in classifying galaxy bars, influenced more by gender and career stage than expertise, raising concerns about the consistency of morphological assessments and automation methods.

Contribution

It demonstrates the extent of subjective differences in galaxy bar classification and highlights the need for standardized practices and improved automated tools.

Findings

Variations exist in bar length, axis-ratio, pitch-angle, and presence among astronomers.

Gender and career stage significantly influence classification discrepancies.

Current automation methods fail to reliably identify bars in galaxy images.

Abstract

Bars are ubiquitous morphological features in the observed distribution of galaxies. There are similarly many methods for classifying these features and, without a strict theoretical definition or common standard practice, this is often left to circumstance. So, we were concerned whether astronomers even agree on the bar which they perceive in a given galaxy and whether this could impact perceived scientific results. As an elementary test, we twenty-one astronomers with varied experience in studying resolved galaxies and circumstances, have each assessed 200 galaxy images, spanning the early phase of bar evolution in two different barred galaxy simulations. We find variations exist within the classification of all the standard bar parameters assessed: bar length, axis-ratio, pitch-angle and even whether a bar is present at all. If this is indicative of the wider community, it has implications for interpreting morphological trends, such as bar-end effects. Furthermore, we find that it is surprisingly not expertise but gender, followed by career stage, which gives rise to the largest discrepancies in the reported bar parameters. Currently, automation does not seem to be a viable solution, with bar classifications from two automated bar-finding algorithms tested and failing to find bars in snapshots where most astronomers agree a bar must exist. Increasing dependence on machine learning or crowdsourcing with a training dataset can only serve to obfuscate any existing biases if these originate from the specific astronomer producing the training material. On the strength of this small sample, we encourage an interim best practice to reduce the impact of any possible classification bias and set goals for the community to resolve the issue in the future.