arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.04325astro-ph.CO
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具有前景强化估计器的低偏差、低噪声CMB透镜效应

Lower bias, lower noise CMB lensing with foreground-hardened estimators

Noah Sailer, Emmanuel Schaan, Simone Ferraro

摘要:

宇宙微波背景(CMB)温度图中的河外前景严重限制了标准估计器重建弱透镜势的能力。这些前景不能通过多频清理或掩蔽完全去除,如果不适当地考虑,可能导致大的偏差。对于由多个未聚集的点源组成的前景,可以导出源振幅的估计值并进行解投影,从而消除对透镜重建的任何偏差。我们的模拟表明,所有的河外前景的温度可以近似由一组具有相同的配置文件的源,一个简单的偏置硬化技术是有效的,在减少任何偏见透镜,在最小的噪声成本。我们比较其他方法,如“剪切”重建的性能和偏差,并讨论如何联合deproject任意数量的前景,每个任意配置文件。特别是,对于一个类似西蒙斯观测的实验前景硬化估计,使我们能够扩展重建中使用的最大多极子,增加整体统计功率$\sim 50\%$超过标准的二次估计,无论是在自动和互相关。我们的结论是,源硬化优于标准的透镜二次估计在自相关和互相关,透镜信号噪声比和前景偏差。

英文摘要:

Extragalactic foregrounds in temperature maps of the Cosmic Microwave Background (CMB) severely limit the ability of standard estimators to reconstruct the weak lensing potential. These foregrounds are not fully removable by multi-frequency cleaning or masking and can lead to large biases if not properly accounted for. For foregrounds made of a number of unclustered point sources, an estimator for the source amplitude can be derived and deprojected, removing any bias to the lensing reconstruction. We show with simulations that all of the extragalactic foregrounds in temperature can be approximated by a collection of sources with identical profiles, and that a simple bias hardening technique is effective at reducing any bias to lensing, at a minimal noise cost. We compare the performance and bias to other methods such as "shear-only" reconstruction, and discuss how to jointly deproject any arbitrary number of foregrounds, each with an arbitrary profile. In particular, for a Simons Observatory-like experiment foreground-hardened estimators allow us to extend the maximum multipole used in the reconstruction, increasing the overall statistical power by $\sim 50\%$ over the standard quadratic estimator, both in auto and cross-correlation. We conclude that source hardening outperforms the standard lensing quadratic estimator both in auto and cross-correlation, and in terms of lensing signal-to-noise and foreground bias.

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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