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arXiv 2603.00531astro-ph.CO
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限制再电离期间中性氢分数:利用功率谱和双谱的交叉模拟推断

Constraining the neutral hydrogen fraction during reionization: Cross-simulation inference using power spectrum and bispectrum

National Institute of Technology Calicut(印度卡尔库特国家技术学院)

Anoop Krishna, Deepthi Moorkanat, Hiten, Rajesh Mondal

AI总结:

本文通过交叉模拟验证,利用功率谱和双谱推断中性氢分数,发现双谱在同代码情况下约束更紧,而交叉模拟情况下约束改善有限,但存在系统性偏差,表明模型不确定性是主要限制因素。

摘要:

红移的21厘米信号是探测早期宇宙的独特探针,特别是再电离时期(EoR)。尽管21厘米功率谱是参数推断的主要统计量,但其无法捕捉信号中的非高斯信息,因此引入更高阶统计量如双谱。我们通过严格交叉模拟验证,训练神经网络在21cmFAST模拟上,并将其应用于由ReionYuga代码生成的模拟观测。我们首先在理想化的21cmFAST-only设置中验证框架,然后应用于更严谨的ReionYuga--21cmFAST交叉模拟情况。我们的分析覆盖了六个红移,包括现实的SKA系统噪声和宇宙方差,计算自50个统计独立的实现。在同代码情况下,双谱显著收紧约束,而在交叉模拟情况下,改善是中等的,约束比仅使用功率谱的情况收紧约1.4倍。交叉模拟分析还识别出推断值与真实值之间的持续系统性偏差,通常超过统计不确定性,表明模型不确定性仍是主要限制因素。因此,我们的结果表明,同代码验证研究中获得的严格约束可能过于乐观,减轻跨模型系统性影响对于SKA时代参数推断的稳健性至关重要。

英文摘要:

The redshifted 21-cm signal is a unique probe of the early universe, particularly the Epoch of Reionization (EoR). While the 21-cm power spectrum has been the primary statistic for parameter inference, it fails to capture the non-Gaussian information in the signal, motivating the use of higher-order statistics such as the bispectrum. We perform a rigorous cross-simulation validation to infer the mean neutral hydrogen fraction ($\bar{x}_{\mathrm{H\,I}}$) by training a neural network on 21cmFAST simulations and applying it to mock observations generated by the ReionYuga code. We first benchmark the framework in an idealized 21cmFAST-only setting before applying it to the more rigorous ReionYuga--21cmFAST cross-simulation case. Our analysis spans six redshifts and includes realistic SKA system noise and cosmic variance, calculated from 50 statistically independent realizations. In the same-code case, the bispectrum yields substantially tighter constraints, whereas in the cross-simulation case the improvement is moderate, with constraints tightened by $\sim 1.4\times$ relative to the power spectrum-only case. The cross-simulation analysis also identifies a persistent systematic discrepancy between inferred and true values that often exceeds the statistical uncertainties, implying that modeling uncertainty remains the dominant limitation. Our results, therefore, indicate that the highly stringent constraints obtained in same-code validation studies may be overly optimistic, and mitigating cross-model systematics is crucial for robust parameter inference in the SKA era.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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