从21厘米地图重建再电离时间场：在WDM宇宙学中研究预测器一致性
Reionisation time field reconstruction from 21-cm Maps: Investigating predictor coherence in WDM cosmology
AI总结：
本文研究了通过21厘米地图重建再电离时间场的方法，探讨了预测器与输入模型之间的一致性，验证了CNN在不同暗物质模型下的表现。
摘要：
再电离时间场treion(r)通过映射每个宇宙区域被电离的时间来捕捉整个再电离历史。先前研究表明，可以通过卷积神经网络(CNNs)从21厘米观测数据中推断出treion(r)。然而，这些CNN预测器是在特定再电离模型上训练的，当应用于可能与训练假设不同的观测数据时，可靠性受到质疑。本文旨在提出并测试一种方法，评估CNN预测器与输入模型之间的一致性，从而能够基于其重建行为验证或排除潜在的再电离模型。通过将CDM模型作为参考输入，我们通过在不同红移下比较不同预测模型的重建结果来评估treion(r)重建的一致性，因为treion(r)重建的统计特性在输入地图的每个红移下应相同。我们的研究特别探讨了训练于冷和暖暗物质(WDM)模型的CNNs，其中WDM粒子质量为2、3、5和7 keV。我们发现，训练于5和7 keV WDM模型的预测器表现出与CDM预测器相似的高一致性，而2 keV预测器以及在一定程度上3 keV预测器在多个指标上显示出显著偏差。这些发现似乎表明，CNN预测器仍能保留对潜在再电离模型差异的敏感性，并可用于评估模型与观测的兼容性。我们的结果强调了在将机器学习预测器应用于真实数据之前，必须验证其与输入模型的一致性。本文提出的方法为CNN在再电离研究中的更可靠应用提供了途径。
英文摘要：
The reionisation time field treion(r) captures the entire history of cosmic reionisation by mapping the moment where each region of the Universe became ionised. Previous work has shown that treion(r) can be inferred from 21-cm observations, using convolutional neural networks (CNNs). However, these CNN predictors are trained on specific reionisation models, raising critical concerns about their reliability when applied to observational data potentially differing from their training assumptions. This paper aims to propose and test a method to evaluate the coherence of our CNN predictors with respect to their input model, thereby enabling the validation or exclusion of underlying reionisation models based on their reconstruction behaviour. By setting the CDM model as reference input, we evaluate the coherence of treion(r) reconstructions by comparing them across different redshifts for several prediction models as the statistics of treion (r) reconstructions should be the same for every redshift of the input maps. Our study particularly investigates CNNs trained on cold and warm dark matter (WDM) models, with WDM particle masses of 2, 3, 5, and 7 keV. We find that the predictors trained on 5 and 7 keV WDM models exhibit high-level self-consistency similar to the CDM predictor, while the 2 keV predictor, and to a lesser extent the 3 keV predictor, display significant deviations across several metrics. These findings seem to demonstrate that CNN predictors retain sensitivity to differences in the underlying reionisation model and can be used to assess model compatibility with observations. Our results highlight the necessity of validating machine-learning predictors against their input models before applying them to real data. The method proposed here offers a pathway to more trustworthy applications of CNNs in the study of reionisation.
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