AI 中文总结
该研究提出从多波段背景光图估计未解析星系概率红移的框架，用条件归一化流训练，在特定成像质量下精度高，对不完整覆盖有弹性，还解决了模拟与观测不匹配问题，展示层析分析可行性，为相关研究开辟新机会。
AI 中文摘要
准确了解未解析星系群体的红移分布对于提取宇宙光学背景中编码的宇宙学信息至关重要。我们提出了第一个直接从未解析背景光的多波段图估计这些分布的框架，使用在真实图像模拟上训练的条件归一化流。对于与欧几里得和鲁宾LSST调查协同预期相当的成像质量，当训练和目标样本在统计上匹配良好时，我们的模型在通量加权红移分布的均值和标准差上都达到了亚百分比精度。此外，结合定制的数据插补策略，该网络对不完整的光度覆盖具有高度弹性。为应对模拟与实际观测之间的统计不匹配挑战，我们建议将观测数量纳入目标变量，提供一种内置诊断以从预测本身评估模型可靠性和不确定性。我们还展示了基于图的层析分析的可行性，表明该模型保留了足够的像素级细节，以在所有箱中以接近亚百分比精度恢复层析定义子样本的红移分布。这些结果确立了条件归一化流作为未解析源红移估计的可行工具，为使用多波段光学背景波动进行成分分离、信号解释和宇宙学推断开辟了新机会。
英文摘要
Accurate knowledge of the redshift distributions of unresolved galaxy populations is essential for extracting the cosmological information encoded in the cosmic optical background. We present the first framework for estimating these distributions directly from multi-band maps of unresolved background light, using conditional normalising flows trained on realistic image simulations. For imaging qualities comparable to those expected from the synergy between the Euclid and Rubin LSST surveys, our model achieves sub-per cent accuracy in both the mean and standard deviation of the flux-weighted redshift distributions when the training and target samples are statistically well matched. Furthermore, the network proves highly resilient to incomplete photometric coverage when combined with tailored data imputation strategies. To address the challenge of statistical mismatches between simulations and real observations, we propose incorporating observational quantities into the target variables, providing a built-in diagnostic to assess model reliability and uncertainty from the predictions themselves. We also demonstrate the feasibility of map-based tomographic analyses, showing that the model retains sufficient pixel-level detail to recover redshift distributions for tomographically defined subsamples with near sub-per cent accuracy across all bins. These results establish conditional normalising flows as a viable tool for redshift estimation of unresolved sources, opening new opportunities for component separation, signal interpretation, and cosmological inference using multi-band optical background fluctuations.
Comments14 pages, 9 figures, 2 tables
