论文标题
部分可观测时空混沌系统的无模型预测
Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference
论文作者
论文摘要
预计当前基于地面的宇宙学调查（例如暗能量调查（DES））会发现成千上万的Galaxy尺度强透镜，而将来的调查（例如Vera Rubin天文台的时空和时间（LSST））将增加该数字的数量1-2个数量级。未来调查中可发现的大量强镜会使强大的镜头成为竞争激烈和互补的宇宙探针。 为了利用通过即将进行的调查发现将发现的镜头的统计能力增加，需要自动透镜分析技术。我们提出了两种基于模拟的推断（SBI）方法，用于透镜 - 元素透镜的镜头参数估计。我们证明了神经后验估计（NPE）的成功应用，以自动推断12参数透镜质量模型，用于DES样地面成像数据。我们将NPE约束与贝叶斯神经网络（BNN）进行比较，发现它的表现优于BNN，产生后验分布，在大多数情况下，这些分布既更准确又更精确。特别是，在BNN实现中，有几种源光模型参数是系统地偏差的。
Current ground-based cosmological surveys, such as the Dark Energy Survey (DES), are predicted to discover thousands of galaxy-scale strong lenses, while future surveys, such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) will increase that number by 1-2 orders of magnitude. The large number of strong lenses discoverable in future surveys will make strong lensing a highly competitive and complementary cosmic probe. To leverage the increased statistical power of the lenses that will be discovered through upcoming surveys, automated lens analysis techniques are necessary. We present two Simulation-Based Inference (SBI) approaches for lens parameter estimation of galaxy-galaxy lenses. We demonstrate the successful application of Neural Posterior Estimation (NPE) to automate the inference of a 12-parameter lens mass model for DES-like ground-based imaging data. We compare our NPE constraints to a Bayesian Neural Network (BNN) and find that it outperforms the BNN, producing posterior distributions that are for the most part both more accurate and more precise; in particular, several source-light model parameters are systematically biased in the BNN implementation.
