Lyman-α森林的拖尾对数正态近似：在2.2≤z≤2.7范围内与全流体动力学模拟的比较
A trailing lognormal approximation of the Lyman-$α$ forest: comparison with full hydrodynamic simulations at $2.2\leq z\leq 2.7$
AI总结：
提出在略低红移处模拟的“拖尾”对数正态模型，以更准确恢复Lyα森林IGM热参数和光电离率，并在2.2≤z≤2.7下与Sherwood SPH模拟验证。
摘要：
遥远类星体光谱中的Lyman-α（Lyα）森林编码了最小尺度上潜在宇宙密度场的信息。利用宇宙学流体动力学模拟对即将到来的大规模、高保真森林数据进行建模在计算上具有挑战性，因此需要准确的半解析技术。其中一种方法基于星系际介质（IGM）中的重子密度场遵循对数正态分布这一假设。基于此，我们扩展了早期工作，以改进Lyα森林的对数正态模型，从而在2.2≤z≤2.7范围内恢复表征IGM状态的参数，特别是氢光电离率（Γ₁₂）；方法是在略低于Sherwood光滑粒子流体动力学（SPH）模拟数据的红移处模拟模型光谱。热参数（即平均密度IGM温度T₀和温度—密度关系斜率γ）的恢复也得到了缓解。这些参数通过马尔可夫链蒙特卡洛（MCMC）技术，利用透射通量的均值和功率谱进行估计。我们发现，IGM密度的通常对数正态分布往往会高估SPH模拟中所见Lyα吸收体的数量。在低于SPH数据的红移处模拟的对数正态模型可以在一定程度上解决这一局限。我们表明，利用这种“拖尾”对数正态分布模型，Γ₁₂值的恢复精度达到≲1σ。我们认为该模型可用于约束宇宙学参数。
英文摘要：
Lyman-$α$(Ly$α$) forest in the spectra of distant quasars encodes the information of the underlying cosmic density field at smallest scales. The modelling of the upcoming large and high-fidelity forest data using cosmological hydrodynamical simulations is computationally challenging and therefore, requires accurate semi-analytical techniques. One such approach is based on the assumption that baryonic density fields in the intergalactic medium (IGM) follow lognormal distribution. Keeping this in mind, we extend our earlier work to improve the lognormal model of the Ly$α$ forest in recovering the parameters characterizing IGM state, particularly the hydrogen photoionization rate ($Γ_{12}$), between $2.2 \leq z \leq 2.7$, by simulating the model spectra at a slightly lower redshift than the Sherwood smooth particle hydrodynamical simulations (SPH) data. The recovery of thermal parameters, namely, the mean-density IGM temperature ($T_0$) and the slope of the temperature-density relation ($γ$) is also alleviated. These parameters are estimated through a Markov Chain Monte Carlo (MCMC) technique, using the mean and power spectrum of the transmitted flux. We find that the usual lognormal distribution of IGM densities tend to over-predict the number of Ly$α$ absorbers seen in SPH simulation. A lognormal model simulated at a lower redshift than SPH data can address this limitation to a certain extent. We show that with such a "trailing" model of lognormal distribution, values of $Γ_{12}$ are recovered at $\lesssim 1-σ$. We argue that this model can be useful for constraining cosmological parameters.
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