论文标题
弱透镜非线性ly-alpha森林
Weak Lensing the non-Linear Ly-alpha Forest
论文作者
论文摘要
我们评估了Metcalf等人的Lyman- $α$ forest弱重力镜头估计量的性能。关于来自流体动力模拟的森林数据和射线追踪的模拟镜头电位。我们将结果与从高斯随机场模拟的$α$森林数据和镜头电位中使用的结果进行了比较。我们发现，估计器能够从更现实的数据中重建镜头电位，并研究对频谱信号对噪声的依赖。由于引力不稳定性和流体动力学引起的这种森林数据中的非线性和非高斯性使信号的噪声减少了$ \ sim2.7 $的$ \ sim2.7 $，而无噪声数据的频谱为$ \ sim 1.5 $，Spectra for Spectra for Spectra for Signal to Signal to Signal to signal of Order Unity（与当前的观察数据相当）。与高斯田间镜头电势相比，使用N体模拟的射线追踪电势会在所有噪声水平下的噪声降低为$ \ sim1.3 $的进一步信号。还观察到森林数据中的非线性性将重建电位的偏见增加$ 5-25 \％$，而射线追踪的镜头电位将偏见增加了$ 20-30 \％$。我们演示了缓解这些问题的方法，包括高斯和偏置校正，可以在实际观察中使用。
We evaluate the performance of the Lyman-$α$ forest weak gravitational lensing estimator of Metcalf et al. on forest data from hydrodynamic simulations and ray-traced simulated lensing potentials. We compare the results to those obtained from the Gaussian random field simulated Ly$α$ forest data and lensing potentials used in previous work. We find that the estimator is able to reconstruct the lensing potentials from the more realistic data, and investigate dependence on spectrum signal to noise. The non-linearity and non-Gaussianity in this forest data arising from gravitational instability and hydrodynamics causes a reduction in signal to noise by a factor of $\sim2.7$ for noise free data and a factor of $\sim 1.5$ for spectra with signal to noise of order unity (comparable to current observational data). Compared to Gaussian field lensing potentials, using ray-traced potentials from N-body simulations incurs a further signal to noise reduction of a factor of $\sim1.3$ at all noise levels. The non-linearity in the forest data is also observed to increase bias in the reconstructed potentials by $5-25\%$, and the ray-traced lensing potential further increases the bias by $20-30\%$. We demonstrate methods for mitigating these issues including Gaussianization and bias correction which could be used in real observations.
