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arXiv 0704.2949astro-ph
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优化未来星系成像巡天以对抗暗能量和修正的引力模型

Optimizing future imaging survey of galaxies to confront dark energy and modified gravity models

Kazuhiro Yamamoto, David Parkinson, Takashi Hamana, Robert C. Nichol, Yasushi Suto

摘要:

我们认为,在何种程度上,未来的星系成像调查可以区分暗能量和修改后的重力模型的起源宇宙加速。动力学暗能量模型可能与修正引力模型具有相似的膨胀率,但预测了不同的结构历史增长。我们用$w_0$和$w_a$这两个参数来参数化宇宙膨胀,用Linder的$γ$来参数化密度涨落的线性增长率。暗能量模型一般预测$γ\approx 0.55$,而DGP模型$γ\approx 0.68$。为了确定未来的成像调查是否可以将$γ$限制在20%(或$Δγ<0.1$)以内,我们对弱透镜调查(如正在进行的Hyper Suprime-Cam(HSC)项目)进行Fisher矩阵分析。在总观测时间固定的条件下,计算了品质因数(FoM)作为曝光时间$\texp$的函数。我们发现,断层扫描技术有效地提高了FoM,它有一个广泛的峰值周围$\texp\simeq {\rm several}\sim 10$分钟;浅和广泛的调查是首选约束的$γ$参数。虽然$Δγ< 0.1$不能单独通过HSC弱透镜巡天来实现,但可以通过结合后续的光谱巡天(如WFMOS和/或未来的CMB观测)来改善约束。

英文摘要:

We consider the extent to which future imaging surveys of galaxies can distinguish between dark energy and modified gravity models for the origin of the cosmic acceleration. Dynamical dark energy models may have similar expansion rates as models of modified gravity, yet predict different growth of structure histories. We parameterize the cosmic expansion by the two parameters, $w_0$ and $w_a$, and the linear growth rate of density fluctuations by Linder's $γ$, independently. Dark energy models generically predict $γ\approx 0.55$, while the DGP model $γ\approx 0.68$. To determine if future imaging surveys can constrain $γ$ within 20 percent (or $Δγ<0.1$), we perform the Fisher matrix analysis for a weak lensing survey such as the on-going Hyper Suprime-Cam (HSC) project. Under the condition that the total observation time is fixed, we compute the Figure of Merit (FoM) as a function of the exposure time $\texp$. We find that the tomography technique effectively improves the FoM, which has a broad peak around $\texp\simeq {\rm several}\sim 10$ minutes; a shallow and wide survey is preferred to constrain the $γ$ parameter. While $Δγ< 0.1$ cannot be achieved by the HSC weak-lensing survey alone, one can improve the constraints by combining with a follow-up spectroscopic survey like WFMOS and/or future CMB 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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