statmorph-lsst: Quantifying and correcting morphological biases in galaxy surveys

TL;DR

This paper assesses how imaging quality affects galaxy morphology metrics, quantifies biases, and introduces corrections and new measurements to improve morphological analysis in upcoming surveys like LSST.

Contribution

It provides empirical bias correction functions, evaluates metric robustness, and introduces two new morphological measurements for galaxy surveys.

Findings

Geometrical measurements are robust within 10% at most depths and resolutions.

Light concentration metrics decrease systematically with resolution, affecting galaxy classification.

Proposed new measurements aim to mitigate existing biases in morphological analysis.

Abstract

Quantitative morphology provides a key probe of galaxy evolution across cosmic time and environments. However, these metrics can be biased by changes in imaging quality - resolution and depth - either across the survey area or the sample. To prepare for the upcoming Rubin LSST data, we investigate this bias for all metrics measured by statmorph and single-component S\'ersic fitting with Galfit. We find that geometrical measurements (ellipticity, axis ratio, Petrosian radius, and effective radius) are robust within 10% at most depths and resolutions. Light concentration measurements ($C$, Gini, $M_{20}$) systematically decrease with resolution, leading low-mass or high-redshift bulge-dominated sources to appear indistinguishable from disks. S\'ersic index $n$, while unbiased, suffers from a 20-40% uncertainty due to degeneracies in the S\'ersic fit. Disturbance measurements ($A$, $A_S$, $D$) depend on signal-to-noise and are thus affected by noise and surface-brightness dimming. We quantify this dependence for each parameter, offer empirical correction functions, and show that the evolution in $C$ observed in JWST galaxies can be explained purely by observational biases. We propose two new measurements - isophotal asymmetry $A_X$ and substructure $St$ - that aim to resolve some of these biases. Finally, we provide a Python package statmorph-lsst implementing these changes and a full dataset that enables tests of custom functions (see text for links).