使用深度学习算法从模拟和观测中重构本动速度
Peculiar Velocity Reconstruction From Simulations and Observations Using Deep Learning Algorithms
AI总结：
本研究提出基于Unet的深度学习模型用于三维本动速度场重构，在含红移空间畸变等真实模拟条件下精度等指标优于理想解析方法，并成功应用于SDSS DR7观测数据。
摘要：
本文提出一种深度学习算法中的Unet模型（U型网络模型），用于重构三维本动速度场，该模型可简化重构流程并提升精度。我们在更贴近真实的条件下（包括红移空间畸变（RSD）效应和暗晕质量阈值），利用模拟数据测试了Unet模型的适应性。结果显示，Unet模型的表现优于理想条件下运行的解析方法，精度提升16%，残差优化13%，相关系数提升18%，平均相干度提升27%。该深度学习算法在捕捉非线性区域的速度特征方面表现出色，还能大幅提升边界区域的重构精度。随后，我们将在SDSS（斯隆数字巡天）观测条件下训练的Unet模型应用于SDSS DR7数据，开展观测类三维本动速度重构。
英文摘要：
In this paper, we introduce a Unet model of deep learning algorithms for reconstructions of the 3D peculiar velocity field, which simplifies the reconstruction process with enhanced precision. We test the adaptability of the Unet model with simulation data under more realistic conditions, including the redshift space distortion (RSD) effect and halo mass threshold. Our results show that the Unet model outperforms the analytical method that runs under ideal conditions, with a 16% improvement in precision, 13% in residuals, 18% in correlation coefficient and 27% in average coherence. The deep learning algorithm exhibits exceptional capacities to capture velocity features in non-linear regions and substantially improve reconstruction precision in boundary regions. We then apply the Unet model trained under SDSS observational conditions to the SDSS DR7 data for observational 3D peculiar velocity reconstructions.
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