arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2209.04564astro-ph.CO
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弱引力透镜化非线性莱曼阿尔法森林

Weak Lensing the non-Linear Ly-alpha Forest

Patrick Shaw, Rupert A. C. Croft, R. Benton Metcalf

AI总结:

本文评估了Metcalf等人提出的莱曼-α森林弱引力透镜估计器在更真实的流体动力学模拟和光线追踪透镜势上的性能,发现数据的非线性导致信噪比降低和偏差增加,并提出了高斯化和偏差校正等缓解方法。

摘要:

我们评估了 Metcalf 等人提出的莱曼-α森林弱引力透镜估计器在流体动力学模拟森林数据和光线追踪模拟透镜势上的性能。我们将结果与先前工作中使用的高斯随机场模拟莱曼-α森林数据和透镜势获得的结果进行了比较。我们发现该估计器能够从更真实的数据中重建透镜势,并研究了其对光谱信噪比的依赖性。该森林数据中由引力不稳定性和流体动力学引起的非线性和非高斯性导致信噪比降低:对于无噪声数据,信噪比降低约2.7倍;对于信噪比为一阶的光谱(与当前观测数据相当),信噪比降低约1.5倍。与高斯场透镜势相比,使用N体模拟的光线追踪势在所有噪声水平下进一步导致信噪比降低约1.3倍。还观察到森林数据中的非线性会使重建势的偏差增加5-25%,而光线追踪透镜势进一步使偏差增加20-30%。我们展示了缓解这些问题的方法,包括可用于实际观测的高斯化和偏差校正。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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