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

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 Sérsic fitting with Galfit. We find that geometrical measurements (centroid, 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. Sérsic index \(n\), while unbiased, suffers from a 20-40% uncertainty due to degeneracies in the Sérsic fit. Disturbance measurements (\(A\), \(A_S\), \(D\)) depend on signal-to-noise ratio 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).