标题： 模板拟合结合深度学习：使用物理引导的神经网络进行红移估计 显示英文标题

标题： Template-Fitting Meets Deep Learning: Redshift Estimation Using Physics-Guided Neural Networks

摘要： 精确的光度红移估计对于观测宇宙学至关重要，尤其是在光谱测量不切实际的大规模调查中。 传统方法包括模板拟合和机器学习，每种方法都有其独特的优点和局限性。 我们提出了一种混合方法，通过物理引导的神经网络将模板拟合与深度学习相结合。 通过将光谱能量分布模板嵌入网络架构中，我们的模型在训练过程中编码了物理先验知识。 该系统采用多模态设计，结合交叉注意力机制来融合光度和图像数据，并使用贝叶斯层进行不确定性估计。 我们在公开的PREML数据集上评估了我们的模型，该数据集包含来自Hyper Suprime-Cam PDR3发布的约400,000个星系，具有5波段光度、多波段成像和光谱红移。 我们的方法实现了RMS误差为0.0507，3-sigma灾难性异常率0.13%，偏差为0.0028。 该模型满足三个LSST光度红移要求中的两个，适用于红移低于3的情况。 这些结果突显了将物理驱动的模板与数据驱动模型相结合在即将到来的宇宙学调查中进行稳健红移估计的潜力。 显示英文摘要

摘要： Accurate photometric redshift estimation is critical for observational cosmology, especially in large-scale surveys where spectroscopic measurements are impractical. Traditional approaches include template fitting and machine learning, each with distinct strengths and limitations. We present a hybrid method that integrates template fitting with deep learning using physics-guided neural networks. By embedding spectral energy distribution templates into the network architecture, our model encodes physical priors into the training process. The system employs a multimodal design, incorporating cross-attention mechanisms to fuse photometric and image data, along with Bayesian layers for uncertainty estimation. We evaluate our model on the publicly available PREML dataset, which includes approximately 400,000 galaxies from the Hyper Suprime-Cam PDR3 release, with 5-band photometry, multi-band imaging, and spectroscopic redshifts. Our approach achieves an RMS error of 0.0507, a 3-sigma catastrophic outlier rate of 0.13%, and a bias of 0.0028. The model satisfies two of the three LSST photometric redshift requirements for redshifts below 3. These results highlight the potential of combining physically motivated templates with data-driven models for robust redshift estimation in upcoming cosmological surveys.