论文标题

避免通过CMB镜头对中​​微子质量测量的重态反馈影响

Avoiding baryonic feedback effects on neutrino mass measurements from CMB lensing

论文作者

McCarthy, Fiona, Foreman, Simon, van Engelen, Alexander

论文摘要

中微子质量总和的测量是即将进行的宇宙微波背景重力镜头测量的主要应用之一。该测量可以通过对与所谓的“重男性效应”相关的不确定性建模对物质聚类的不确定性进行混淆,这是由于气体动力学,恒星形成以及活性银河系核和超级新星的反馈引起的。特别是,关于男性效应对CMB镜头的形式的错误假设可能会通过统计不确定性的很大一部分偏向中微子质量测量。在本文中,我们研究了减轻这种偏见的三种方法:(1)在限制中微子质量时限制使用小规模的CMB镜头信息; (2)使用外部示踪剂去除对CMB镜头图的低降射贡献; (3)在一个参数模型上进行边缘化,以实现对大规模结构的重生效应。我们使用Fisher矩阵预测测试了类似于Simons天文台和CMB-S4的实验,使用各种最近的流体动力学模拟来代表可能的Baryonic效应范围,并使用由Rubin observatory的LSST测量的宇宙剪切方法,作为方法中的Tracer(2)。我们发现,(1)和(2)或(3)本身将有效地减少对中微子质量测量值诱导的偏见至可忽略的水平,而不会显着增加相关的统计不确定性。

A measurement of the sum of neutrino masses is one of the main applications of upcoming measurements of gravitational lensing of the cosmic microwave background (CMB). This measurement can be confounded by modelling uncertainties related to so-called "baryonic effects" on the clustering of matter, arising from gas dynamics, star formation, and feedback from active galactic nuclei and supernovae. In particular, a wrong assumption about the form of baryonic effects on CMB lensing can bias a neutrino mass measurement by a significant fraction of the statistical uncertainty. In this paper, we investigate three methods for mitigating this bias: (1) restricting the use of small-scale CMB lensing information when constraining neutrino mass; (2) using an external tracer to remove the low-redshift contribution to a CMB lensing map; and (3) marginalizing over a parametric model for baryonic effects on large-scale structure. We test these methods using Fisher matrix forecasts for experiments resembling the Simons Observatory and CMB-S4, using a variety of recent hydrodynamical simulations to represent the range of possible baryonic effects, and using cosmic shear measured by the Rubin Observatory's LSST as the tracer in method (2). We find that a combination of (1) and (2), or (3) on its own, will be effective in reducing the bias induced by baryonic effects on a neutrino mass measurement to a negligible level, without a significant increase in the associated statistical uncertainty.

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