马尔可夫链束流随机化：PLANCK束流测量误差对宇宙学参数估计影响的研究
Markov Chain Beam Randomization: a study of the impact of PLANCK beam measurement errors on cosmological parameter estimation
摘要：
我们介绍了一种新的方法来传播光束形状的不确定性，用于测量宇宙微波背景的宇宙学参数确定从这些测量。该方法，我们称之为马尔可夫链波束随机化，MCBR，随机样本从一组模板或功能，描述波束的不确定性。该方法比直接的数值积分系统的“滋扰”参数快得多，并不限于简单的，理想化的情况下，是分析边缘化。它不假设数据是正态分布的，并且不需要特定系统不确定性的高斯先验。我们表明，MCBR适当的帐户，并提供了边缘化的参数的错误。该方法可以推广，并用于传播任何系统的不确定性，一组模板是可用的。我们将该方法应用于普朗克卫星，并考虑未来的实验。只要在去除1/f噪声后进行光束拟合，光束测量误差对宇宙学参数的影响应该很小。
英文摘要：
We introduce a new method to propagate uncertainties in the beam shapes used to measure the cosmic microwave background to cosmological parameters determined from those measurements. The method, which we call Markov Chain Beam Randomization, MCBR, randomly samples from a set of templates or functions that describe the beam uncertainties. The method is much faster than direct numerical integration over systematic `nuisance' parameters, and is not restricted to simple, idealized cases as is analytic marginalization. It does not assume the data are normally distributed, and does not require Gaussian priors on the specific systematic uncertainties. We show that MCBR properly accounts for and provides the marginalized errors of the parameters. The method can be generalized and used to propagate any systematic uncertainties for which a set of templates is available. We apply the method to the Planck satellite, and consider future experiments. Beam measurement errors should have a small effect on cosmological parameters as long as the beam fitting is performed after removal of 1/f noise.
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