论文标题
测试宇宙距离双重性关系的深度学习方法
Deep learning method in testing the cosmic distance duality relation
论文作者
论文摘要
宇宙距离双重关系（DDR）受到使用深度学习方法的类型超新星（SNE IA）和强重力透镜（SGL）系统的组合的约束。为了利用完整的SGL数据，我们使用深度学习重建了从SNE IA到SGL的最高红移的光度距离，然后将其与从SGL获得的角直径距离进行了比较。考虑到晶状体质量谱的影响，我们限制了三种晶状体质量模型中可能违反DDR的侵犯。结果表明，在SIS模型和EPL模型中，DDR受到较高的信心水平的侵犯，违规参数$η_0= -0.193^{+0.021} _ { - 0.019} $和$η_0= -0.247^{+0.014} _ {+0.014} _ { - 0.014} _ { - 0.014} _ { - 0.013} $ { - { - 0.013} $。但是，在PL模型中，DDR在1 $σ$置信度级别内进行了验证，违规参数$η_0= -0.014^{+0.053} _ { - 0.045} $。我们的结果表明，对DDR的约束在很大程度上取决于镜头质量模型。给定特定的镜头质量模型，可以使用深度学习以$ \ textit {o}（10^{ - 2}）的精度来限制DDR。
The cosmic distance duality relation (DDR) is constrained from the combination of type-Ia supernovae (SNe Ia) and strong gravitational lensing (SGL) systems using deep learning method. To make use of the full SGL data, we reconstruct the luminosity distance from SNe Ia up to the highest redshift of SGL using deep learning, then it is compared with the angular diameter distance obtained from SGL. Considering the influence of lens mass profile, we constrain the possible violation of DDR in three lens mass models. Results show that in the SIS model and EPL model, DDR is violated at high confidence level, with the violation parameter $η_0=-0.193^{+0.021}_{-0.019}$ and $η_0=-0.247^{+0.014}_{-0.013}$, respectively. In the PL model, however, DDR is verified within 1$σ$ confidence level, with the violation parameter $η_0=-0.014^{+0.053}_{-0.045}$. Our results demonstrate that the constraints on DDR strongly depend on the lens mass models. Given a specific lens mass model, DDR can be constrained at a precision of $\textit{O}(10^{-2})$ using deep learning.
