论文标题
从$ z \ geq 5.4 $从lyman- $ a $α$ forest forest flux自动相关函数的电离光子的平均自由途径的预测约束
Forecasting constraints on the mean free path of ionizing photons at $z \geq 5.4$ from the Lyman-$α$ forest flux auto-correlation function
论文作者
论文摘要
Lyman-$ $α$(LY $α$)森林的波动向高-O Z $ quasars的转移部分来自紫外线背景(UVB)的空间波动,其级别是由电离光子的平均自由路径($λ_{\λ_{\ text {\ text {mfp}} $设定的。 LY $α$ forest通量的自动相关函数表征了传输波动的强度和规模,因此,正如我们所显示的,对$λ_{\ text {mfp}} $敏感。 $ z \ sim 6 $的最新测量结果表明,在$ z> 5.0 $上的$λ_ {\ text {mfp}} $的快速演变将在自动相关函数的演变中留下签名。为了进行此预测,我们将模拟$α$森林数据模拟,其属性类似于XQR-30扩展数据集,为$ 5.4 \ leq Z \ leq 6.0 $。在每个$ z $中,我们研究了100个模拟数据集,并且模拟数据与自动相关函数的模型值匹配的理想情况。对于具有$λ_ {\ text {mfp}} = 9.0 $ cmpc at $ z = 6.0 $的理想数据,我们恢复$λ_{\ text {mfp}} = 12^{+6} _ { - 3} $ cmpc。此精度可与$λ_{\ text {Mfp}} $的直接测量相媲美,从列出的列曼频谱的堆叠量超出了Lyman限制。假设的高分辨率数据导致$ \ sim40 \%$减少了所有$ z $。对于这项工作,自动相关函数的模拟值的分布对于高$ z $来说是高度非高斯的,这应该警告与其他高级$ z $ ly $α$ forest的其他统计数据,而不是做出这一假设。我们使用严格的统计方法来通过推理测试,但是未来关于非高斯方法的工作将实现更高的精度测量。
Fluctuations in Lyman-$α$ (Ly$α$) forest transmission towards high-$z$ quasars are partially sourced from spatial fluctuations in the ultraviolet background (UVB), the level of which are set by the mean free path of ionizing photons ($λ_{\text{mfp}}$). The auto-correlation function of Ly$α$ forest flux characterizes the strength and scale of transmission fluctuations and, as we show, is thus sensitive to $λ_{\text{mfp}}$. Recent measurements at $z \sim 6$ suggest a rapid evolution of $λ_{\text{mfp}}$ at $z>5.0$ which would leave a signature in the evolution of the auto-correlation function. For this forecast, we model mock Ly$α$ forest data with properties similar to the XQR-30 extended data set at $5.4 \leq z \leq 6.0$. At each $z$ we investigate 100 mock data sets and an ideal case where mock data matches model values of the auto-correlation function. For ideal data with $λ_{\text{mfp}}=9.0$ cMpc at $z=6.0$, we recover $λ_{\text{mfp}}=12^{+6}_{-3}$ cMpc. This precision is comparable to direct measurements of $λ_{\text{mfp}}$ from the stacking of quasar spectra beyond the Lyman limit. Hypothetical high-resolution data leads to a $\sim40\%$ reduction in the error bars over all $z$. The distribution of mock values of the auto-correlation function in this work is highly non-Gaussian for high-$z$, which should caution work with other statistics of the high-$z$ Ly$α$ forest against making this assumption. We use a rigorous statistical method to pass an inference test, however future work on non-Gaussian methods will enable higher precision measurements.