论文标题
灰尘对宇宙微波背景的光谱失真测量的影响
Impact of Dust on Spectral Distortion Measurements of the Cosmic Microwave Background
论文作者
论文摘要
宇宙微波背景(CMB)的光谱失真对早期宇宙中的外来物理学注射敏感。拟议的原始通货膨胀探索器(Pixie)任务具有原始敏感性,可以对新物理学提供有意义的限制,但前提是可以对前景发射进行充分的建模。我们通过考虑一系列受理论和观察先验约束的晶粒大小分布和组成(Zelko&Finkbeiner 2020)来量化星际尘对Compton $ $ $和$μ$测量的影响。我们发现,Pixie可以在适度数量的灰尘参数上边缘化,但仍会恢复$ y $和$μ$的估计,尽管不确定性增加。随着更多的前景组件的包括(同步器,免费免费),$ y $降级的估计值,以及在标准$λ$ CDM $ 2 \ times10^{ - 8} $中有时考虑的$μ$的测量范围,没有辅助下降频率的前景不可行的情况。另一个问题是CMB单极的吸收,这是必须包括的微妙效果。我们量化了一种模型差异误差的一种形式,发现通过将我们的星际中尘模型与改良的黑体拟合而引入的误差太大,无法检测到CMB光谱畸变。最大的挑战可能是宇宙红外背景(CIB)。我们发现$μ$和$ y $对CIB的建模选择非常敏感,并量化了一系列假设期望的偏见。
Spectral distortions of the cosmic microwave background (CMB) are sensitive to energy injection by exotic physics in the early universe. The proposed Primordial Inflation Explorer (PIXIE) mission has the raw sensitivity to provide meaningful limits on new physics, but only if foreground emission can be adequately modeled. We quantify the impact of interstellar dust on Compton $y$ and $μ$ measurements by considering a range of grain size distributions and compositions constrained by theoretical and observational priors (Zelko & Finkbeiner 2020). We find that PIXIE can marginalize over a modest number of dust parameters and still recover $y$ and $μ$ estimates, though with increased uncertainty. As more foreground components are included (synchrotron, free-free), the estimates of $y$ degrade, and measurement of $μ$ in the range sometimes considered for the standard $Λ$CDM of $2\times10^{-8}$ becomes infeasible without ancillary low-frequency foreground information. An additional concern is dust absorption of the CMB monopole, a subtle effect that must be included. We quantify one form of model discrepancy error, finding that the error introduced by fitting our interstellar medium dust model with a modified blackbody is too large for CMB spectral distortions to be detectable. The greatest challenge may be the cosmic infrared background (CIB). We find that $μ$ and $y$ are extremely sensitive to modeling choices for the CIB, and quantify biases expected for a range of assumptions.