arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1105.3285astro-ph.CO
收藏

Ia超新星特殊速度测量的数据压缩

Data compression of measurements of peculiar velocities of Supernovae Ia

Vid Iršič, Anže Slosar

摘要:

研究了低红移($z<0.1$)Ia型超新星由于其特有的速度而导致的光度距离相关扰动中的信息压缩。我们证明,天真的压缩到角速度功率谱不有效地工作,由于厚度的球壳的超新星被测量。相反,我们表明,测量可以压缩成测量的$f^2P(k)$,其中$f$是对数增长率的线性扰动和$P(k)$是他们的功率谱。我们开发了一个最佳的二次估计,并表明它恢复所有的信息$ΛCDM$模型的调查$N\sim10,000$或更多的超新星。我们明确地证明了鲁棒性相对于假设的基准模型和功率谱箱的数量。利用Ia型超新星的模拟星表,我们估计未来的低红移巡天将能够用$10,000$ Ia型超新星探测$σ_8$到6%的精度。

英文摘要:

We study the compression of information present in the correlated perturbations to the luminosity distance in the low-redshift ($z<0.1$) supernovae Ia due to peculiar velocities of these supernovae. We demonstrate that the naïve compression into angular velocity power spectrum does not work efficiently, due to thickness of the spherical shell over which the supernovae are measured. Instead, we show that measurements can be compressed into measurements of $f^2P(k)$, where $f$ is the logarithmic rate of growth of linear perturbations and $P(k)$ is their power spectrum. We develop an optimal quadratic estimator and show that it recovers all information for $ΛCDM$ models for surveys of $N\sim10,000$ or more supernovae. We explicitly demonstrate robustness with respect to the assumed fiducial model and the number of power spectrum bins. Using mock catalogues of SNe Ia we estimate that future low redshift surveys will be able to probe $σ_8$ to 6% accuracy with $10,000$ SNe Ia.

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。

↑