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arXiv 0805.2155astro-ph
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根据宇宙微波背景改进的团簇质量剖面估算

Improved estimation of cluster mass profiles from the cosmic microwave background

Jaiyul Yoo, Matias Zaldarriaga

摘要:

发展了一种从宇宙微波背景(CMB)重建星系团质量分布和大尺度结构的新方法。通过分析CMB透镜效应的可能性,我们分析证明了CMB透镜效应的标准二次估计是无偏的,只有在无透镜效应的限制下才能达到最优条件;当透镜效应很大时,它们会逐渐变得有偏和次优,特别是对于正在进行的Sunyaev-Zel'dovich调查可以发现的集群。采用另一种方法的CMB可能性,我们构建了一个新的极大似然估计,利用delensed CMB温度场的基础上假设的模型。我们分析表明,这个估计渐近接近最优条件,我们假设的模型是完善的,我们数值计算表明,我们的估计快速收敛到真正的模型,因为我们迭代地将其应用到CMB地图。对于现实的CMB实验,我们证明了适用的极大似然估计与测试对数值模拟中存在的CMB二次污染物。新的极大似然估计方法在显著提高信噪比的同时,还可以测量不同红移下的团簇-质量交叉关联函数,从而探测暗能量的演化.

英文摘要:

We develop a new method for reconstructing cluster mass profiles and large-scale structure from the cosmic microwave background (CMB). By analyzing the likelihood of CMB lensing, we analytically prove that standard quadratic estimators for CMB lensing are unbiased and achieve the optimal condition only in the limit of no lensing; they become progressively biased and sub-optimal, when the lensing effect is large, especially for clusters that can be found by ongoing Sunyaev-Zel'dovich surveys. Adopting an alternative approach to the CMB likelihood, we construct a new maximum likelihood estimator that utilizes delensed CMB temperature fields based on an assumed model. We analytically show that this estimator asymptotically approaches the optimal condition as our assumed model is refined, and we numerically show that our estimator quickly converges to the true model as we iteratively apply it to CMB maps. For realistic CMB experiments, we demonstrate the applicability of the maximum likelihood estimator with tests against numerical simulations in the presence of CMB secondary contaminants. With significant improvement on the signal-to-noise ratio, our new maximum likelihood estimator can be used to measure the cluster-mass cross-correlation functions at different redshifts, probing the evolution of dark energy.

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