AI 中文总结
研究再电离时期21厘米信号光锥的参数推断，比较标准柱面PS、切片PS和ePS三种汇总统计量，通过训练神经网络模拟器并考虑宇宙方差和系统噪声，发现ePS性能最优，能产生更紧约束，确立其为解释未来数据的最优汇总统计量。
AI 中文摘要
光锥（LC）效应给21厘米信号引入视线（LoS）统计不均匀性，传统功率谱（PS）无法捕捉完整两点统计信息。演化功率谱（ePS）能解决此LoS演化问题。本文比较了三种不同汇总统计量的统计能力，包括标准柱面PS、切片PS和ePS。结果表明，ePS在多数k和z上能成功恢复共时模拟的基准3D PS，切片PS仅在大k时能恢复。通过训练神经网络模拟器进行参数推断，考虑宇宙方差和系统噪声，发现ePS性能最优，比其他两者分别能产生紧3倍和1.4倍的约束。研究结果确立了ePS作为解释未来数据的最优汇总统计量。
英文摘要
The light-cone (LC) effect introduces line-of-sight (LoS) statistical inhomogeneity into the 21-cm signal. Consequently, the traditional power spectrum (PS) fails to capture the full two-point statistical information. The evolving power spectrum (ePS), $P_e(k, z)$, offers an alternative that accounts for this LoS evolution. We compare the statistical power of three different summary statistics: the standard cylindrical PS $P(k_\perp,k_\parallel)$, slice-wise PS $P_s(k, z)$ (3D PS for small bandwidth LC slices), and ePS $P_e(k, z)$. We first demonstrate that $P_e(k,z)$ successfully recovers the benchmark 3D PS of coeval simulations across most $k$ and $z$, whereas the slice-wise PS recovers only at large $k$. To efficiently perform parameter inference, we train artificial neural network (ANN) emulators on $500$ LC 21-cm signals. Our forecasts incorporate cosmic variance, estimated using $50$ statistically independent realizations of the signal, alongside SKA-Low system noise for integration times of $1000$ and $104$ hrs. We find that ePS outperforms its peers, yielding $3$ and $1.4$ times tighter constraints than $P(k_\perp,k_\parallel)$ and $P_s(k,z)$, respectively. Our results establish the ePS as an optimal summary statistic for interpreting forthcoming data.
