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Quantitative morphologies, such as asymmetry and concentration, have longbeen used as an effective way to assess the distribution of galaxy starlight inlarge samples. Application of such quantitative indicators to other dataproducts could provide a tool capable of capturing the 2-dimensionaldistribution of a range of galactic properties, such as stellar mass orstar-formation rate maps. In this work, we utilize galaxies from the Illustrisand IllustrisTNG simulations to assess the applicability of concentration andasymmetry indicators to the stellar mass distribution in galaxies.Specifically, we test whether the intrinsic values of concentration andasymmetry (measured directly from the simulation stellar mass particle maps)are recovered after the application of measurement uncertainty and a pointspread function (PSF). We find that random noise has a non-negligiblesystematic effect on asymmetry that scales inversely with signal-to-noise,particularly at signal-to-noise less than 100. We evaluate different methods tocorrect for the noise contribution to asymmetry at very low signal-to-noise,where previous studies have been unable to explore due to systematics. Wepresent algebraic corrections for noise and resolution to recover the intrinsicmorphology parameters. Using Illustris as a comparison dataset, we evaluate therobustness of these fits in the presence of a different physics model, andconfirm these correction methods can be applied to other datasets. Lastly, weprovide estimations for the uncertainty on different correction methods atvarying signal-to-noise and resolution regimes.

monthly notices of the royal astronomical society

Towards robust determination of non-parametric morphologies in marginal  astronomical data: resolving uncertainties with cosmological hydrodynamical  simulations