论文标题
宇宙黎明模型中的系统不确定性
Systematic uncertainties in models of the cosmic dawn
论文作者
论文摘要
在RedShifts $ Z \ GTRSIM 6 $上进行回离和再加热的模型继续增长,以期预期近距离21 cm,宇宙微波背景和银河系测量。但是,模型中有许多潜在的系统不确定性来源,如果不算出,可能会偏向和/或降低即将到来的约束。在这项工作中,我们研究了IGM的平均电源和热历史模型中的三个常见不确定性来源:基础宇宙学,Halo质量功能(HMF)以及恒星种群合成(SPS)模型的选择。我们发现,宇宙学不确定性会影响汤森(Thomson)散射的光学深度,并在$ \ sim $ 5-10 mk级别上的全局21厘米信号的振幅和全局21厘米信号的振幅。通过选择HMF和SPS型号带来的差异更具戏剧性,可与$τ_e$上的$1σ$误差线和$ \ sim 20 $ MK在全球21厘米信号振幅上效应。最后,我们共同拟合所有HMF/sps组合的星系亮度功能和全局21 cm信号,发现(i)这样做需要额外的免费参数来补偿建模系统的建模,以及(ii)在不同的HMF和SPS选择中的限制中的限制,假设与5美元的MK噪声相比,那些在其他HMF和SP的兴趣参数中,则是在全球范围内进行的。 $ \ gtrsim 20 $ mk错误。我们的工作强调了为减少建模不确定性以实现对未来数据集的精确推断而进行的专门努力。
Models of the reionization and reheating of the intergalactic medium (IGM) at redshifts $z \gtrsim 6$ continue to grow more sophisticated in anticipation of near-future 21-cm, cosmic microwave background, and galaxy survey measurements. However, there are many potential sources of systematic uncertainty in models that could bias and/or degrade upcoming constraints if left unaccounted for. In this work, we examine three commonly-ignored sources of uncertainty in models for the mean reionization and thermal histories of the IGM: the underlying cosmology, halo mass function (HMF), and choice of stellar population synthesis (SPS) model. We find that cosmological uncertainties affect the Thomson scattering optical depth at the few percent level and the amplitude of the global 21-cm signal at the $\sim$5-10 mK level. The differences brought about by choice of HMF and SPS models are more dramatic, comparable to the $1 σ$ error-bar on $τ_e$ and a $\sim 20$ mK effect on the global 21-cm signal amplitude. Finally, we jointly fit galaxy luminosity functions and global 21-cm signals for all HMF/SPS combinations and find that (i) doing so requires additional free parameters to compensate for modeling systematics and (ii) the spread in constraints on parameters of interest for different HMF and SPS choices, assuming $5$ mK noise in the global signal, is comparable to those obtained when adopting the "true" HMF and SPS with $\gtrsim 20$ mK errors. Our work highlights the need for dedicated efforts to reduce modeling uncertainties in order to enable precision inference with future datasets.