arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2110.01488astro-ph.CO
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非线性星系团的参数推断:理论不确定性的解释

Parameter inference with non-linear galaxy clustering: accounting for theoretical uncertainties

Mischa Knabenhans, Thejs Brinckmann, Joachim Stadel, Aurel Schneider, Romain Teyssier

摘要:

我们实现EuclidEmulator(版本1),一个仿真器的非线性校正的物质功率谱,到MCMC预测代码Montepython。我们比较Halofit,HMCode和EuclidEmulator1的性能,无论是在功率谱预测的水平和宇宙学参数的后验概率分布的水平,不同的宇宙学模型和不同的星系功率谱波数截止。我们确认,功率谱预测的选择有一个不可忽略的影响,计算的灵敏度时,做宇宙学参数预测,即使是保守的波数截止$0.2\,h\,{\rm Mpc}^{-1}$。我们发现,EuclidEmulator1是平均高达$17\%$更敏感的宇宙学参数比其他两个代码,最显着的改进是哈勃参数高达$42\%$和状态方程的暗能量高达$26\%$,根据情况而定。此外,我们指出,功率谱预测的选择有助于计算一个显着偏置的平均宇宙学的风险时,做参数估计。对于四个测试的情况下,我们发现偏差,平均宇宙学参数,0.5和2 $σ$之间(从低于$1σ$高达$6σ$的个别参数)。本文提供了一个概念证明,即通过考虑量身定制的理论不确定性可以减轻这种风险,因为这可以将偏倚降低2至5倍,具体取决于所考虑的情况,同时保持后验可信度轮廓较小:在所有情况下,标准差都放大了$\leq1.4$倍。

英文摘要:

We implement EuclidEmulator (version 1), an emulator for the non-linear correction of the matter power spectrum, into the MCMC forecasting code MontePython. We compare the performance of Halofit, HMCode, and EuclidEmulator1, both at the level of power spectrum prediction and at the level of posterior probability distributions of the cosmological parameters, for different cosmological models and different galaxy power spectrum wave number cut-offs. We confirm that the choice of the power spectrum predictor has a non-negligible effect on the computed sensitivities when doing cosmological parameter forecasting, even for a conservative wave number cut-off of $0.2\,h\,{\rm Mpc}^{-1}$. We find that EuclidEmulator1 is on average up to $17\%$ more sensitive to the cosmological parameters than the other two codes, with the most significant improvements being for the Hubble parameter of up to $42\%$ and the equation of state of dark energy of up to $26\%$, depending on the case. In addition, we point out that the choice of the power spectrum predictor contributes to the risk of computing a significantly biased mean cosmology when doing parameter estimations. For the four tested scenarios we find biases, averaged over the cosmological parameters, of between 0.5 and 2$σ$ (from below $1σ$ up to $6σ$ for individual parameters). This paper provides a proof of concept that this risk can be mitigated by taking a well-tailored theoretical uncertainty into account as this allows to reduce the bias by a factor of 2 to 5, depending on the case under consideration, while keeping posterior credibility contours small: the standard deviations are amplified by a factor of $\leq1.4$ in all cases.

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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