X射线在不同21厘米模拟代码中如何加热星系际介质（IGM）：Licorice与Beorn的比较
How X-rays heat the IGM in different 21-cm simulation codes: a comparison between Licorice and Beorn
AI总结：
本文对比三维RT代码Licorice与一维RT代码Beorn对IGM X射线加热的模拟，发现二者光度场等整体结果一致但分布差异致21厘米功率谱差约30%，该偏差会使天体物理参数后验分布出现大于1σ的典型偏差。
AI中文摘要：
任何利用贝叶斯推断方法对中性氢的21厘米信号进行的解释，其准确性仅能与用于建模星系际介质（IGM）状态的底层模拟代码的准确性相当。三维辐射转移（RT）模拟代码或许能捕捉复杂物理过程，但计算成本高昂，因此人们开发了更快速、近似程度更高的代码。为增进对21厘米科学领域模拟代码收敛性的理解，我们对比了三维RT模拟代码Licorice与一维RT代码Beorn所建模的X射线对IGM的加热过程。我们利用Beorn处理从Licorice模拟中提取的源，采用相同的源物理性质，以获得两个版本的X射线加热IGM温度结果。我们观测到两种设置下的光度场、平均温度及整体21厘米信号具有良好一致性，但温度与21厘米信号的分布存在差异，这导致21厘米功率谱出现约30%的差异。我们尝试分离导致这些差异的近似处理，发现一维RT代码中常用的近似处理会产生该量级的效应。在马尔可夫链蒙特卡洛（MCMC）流程中利用Licorice功率谱的模拟器，我们将功率谱之间的差异转化为天体物理参数后验分布之间的差异。我们观测到一维后验分布之间存在典型偏差，其大于1σ（噪声水平对应100小时的平方公里阵列（SKA）观测）。
英文摘要：
Any interpretation of the 21-cm signal of neutral hydrogen using Bayesian inference methods can only be as accurate as the underlying simulation code used to model the state of the intergalactic medium (IGM). 3D radiative transfer (RT) simulation codes may capture complex physics, but are computationally expensive and, therefore, faster, more approximate codes have been developed. To improve our understanding of the convergence of simulation codes in the 21-cm science community, we present a comparison of the X-ray heating of the IGM modelled in Licorice, a 3D RT simulation code, and Beorn, a 1D RT code. We use Beorn to process sources extracted from Licorice simulations, using the same physics of the sources, in order to obtain two versions of the temperature of the IGM heated by X-rays. We observe a good agreement between the luminosity fields, mean temperatures, and global 21-cm signal of the two setups, but discrepancies in the distribution of temperature and 21-cm signal, which result in a $\sim 30\%$ difference in the 21-cm power spectrum. We attempt to isolate the approximations that lead to these differences and find that common approximations used in 1D RT codes produce effects of that magnitude. Using an emulator of the Licorice power spectra in an MCMC pipeline, we translate these differences between power spectra into differences between posterior distributions over the astrophysical parameters. We observe a typical bias between 1D posteriors of $\gtrsim 1 σ$ (with a noise level corresponding to 100h of SKA observations).
