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 two-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 ratio (S/N), particularly at an S/N less than 100. We evaluate different methods to correct for the noise contribution to asymmetry at very low S/N, 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 data set, 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 data sets. Lastly, we provide estimations for the uncertainty on different correction methods at varying S/N and resolution regimes.

中文翻译：

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稳健地确定边缘天文数据中的非参数形态：用宇宙学流体动力学模拟解决不确定性

定量形态，如不对称和浓度，长期以来一直被用作评估大样本中星系星光分布的有效方法。将此类定量指标应用于其他数据产品可以提供一种工具，能够捕获一系列星系特性的二维分布，例如恒星质量或恒星形成率图。在这项工作中，我们利用 Illustris 和 IllustrisTNG 模拟中的星系来评估浓度和不对称指标对星系中恒星质量分布的适用性。具体来说，我们测试了在应用测量不确定性和点扩散函数 (PSF) 后是否恢复了浓度和不对称性的内在值（直接从模拟恒星质量粒子图测量）。我们发现随机噪声对不对称性有不可忽略的系统影响，它与信噪比 (S/N) 成反比，尤其是在 S/N 小于 100 时。我们评估了不同的方法来校正噪声贡献在非常低的信噪比下不对称，以前的研究由于系统学而无法探索。我们提出了噪声和分辨率的代数校正，以恢复固有形态参数。使用 Illustris 作为比较数据集，我们评估了这些拟合在存在不同物理模型的情况下的稳健性，并确认这些校正方法可以应用于其他数据集。最后，我们提供了对不同 S/N 和分辨率方案下不同校正方法的不确定性的估计。