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

Quantitative morphologies, such as asymmetry and concentration, have long
been used as an effective way to assess the distribution of galaxy starlight in
large samples. Application of such quantitative indicators to other data
products could provide a tool capable of capturing the 2-dimensional
distribution of a range of galactic properties, such as stellar mass or
star-formation rate maps. In this work, we utilize galaxies from the Illustris
and IllustrisTNG simulations to assess the applicability of concentration and
asymmetry indicators to the stellar mass distribution in galaxies.
Specifically, we test whether the intrinsic values of concentration and
asymmetry (measured directly from the simulation stellar mass particle maps)
are recovered after the application of measurement uncertainty and a point
spread function (PSF). We find that random noise has a non-negligible
systematic effect on asymmetry that scales inversely with signal-to-noise,
particularly at signal-to-noise less than 100. We evaluate different methods to
correct for the noise contribution to asymmetry at very low signal-to-noise,
where previous studies have been unable to explore due to systematics. We
present algebraic corrections for noise and resolution to recover the intrinsic
morphology parameters. Using Illustris as a comparison dataset, we evaluate the
robustness of these fits in the presence of a different physics model, and
confirm these correction methods can be applied to other datasets. Lastly, we
provide estimations for the uncertainty on different correction methods at
varying signal-to-noise and resolution regimes.
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