Title: The systematics of strong lens modeling quantified: the effects of constraint selection and redshift information on magnification, mass, and multiple image predictability Show Chinese title

Title: 强透镜建模的系统性量化：约束选择和红移信息对放大率、质量以及多重像可预测性的影响

Abstract: Until now, systematic errors in strong gravitational lens modeling have been acknowledged but never been fully quantified. Here, we launch an investigation into the systematics induced by constraint selection. We model the simulated cluster Ares 362 times using random selections of image systems with and without spectroscopic redshifts and quantify the systematics using several diagnostics: image predictability, accuracy of model-predicted redshifts, enclosed mass, and magnification. We find that for models with $>15$ image systems, the image plane rms does not decrease significantly when more systems are added; however the rms values quoted in the literature may be misleading as to the ability of a model to predict new multiple images. The mass is well constrained near the Einstein radius in all cases, and systematic error drops to $<2\%$ for models using $>10$ image systems. Magnification errors are smallest along the straight portions of the critical curve, and the value of the magnification is systematically lower near curved portions. For $>15$ systems, the systematic error on magnification is $\sim2\%$. We report no trend in magnification error with fraction of spectroscopic image systems when selecting constraints at random; however, when using the same selection of constraints, increasing this fraction up to $\sim0.5$ will increase model accuracy. The results suggest that the selection of constraints, rather than quantity alone, determines the accuracy of the magnification. We note that spectroscopic follow-up of at least a few image systems is crucial, as models without any spectroscopic redshifts are inaccurate across all of our diagnostics. Show Chinese abstract

Abstract: 到目前为止，强引力透镜建模中的系统误差已被承认但从未被完全量化。 在这里，我们开展了一项关于约束选择引起的系统性的研究。 我们使用带有和不带有光谱红移的图像系统的随机选择，对模拟星系团Ares进行了362次建模，并使用几种诊断方法来量化系统性：图像可预测性、模型预测红移的准确性、包围质量以及放大率。 我们发现，对于具有$>15$个图像系统的模型，当添加更多系统时，图像平面上的均方根不会显著减少；然而，文献中引用的均方根值可能误导了模型预测新多重图像的能力。 在所有情况下，爱因斯坦半径附近的质量都被很好地约束，对于使用$>10$个图像系统的模型，系统误差降至$<2\%$。 放大率误差在临界曲线的直线部分最小，而在弯曲部分附近放大率值系统性地较低。 对于$>15$系统，放大率的系统误差为$\sim2\%$。 我们在随机选择约束时没有发现放大率误差与光谱图像系统比例之间的趋势；然而，当使用相同的约束选择时，将此比例增加到$\sim0.5$会提高模型的准确性。 结果表明，约束的选择而非数量本身决定了放大率的准确性。 我们注意到，至少对几个图像系统进行光谱后续观测是至关重要的，因为没有任何光谱红移的模型在我们所有的诊断中都不准确。