AI-Driven Reconstruction of Large-Scale Structure from Combined Photometric and Spectroscopic Surveys
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arXiv
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| Format: | Preprint |
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2025
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| author | Du, Wenying Luo, Xiaolin Jiang, Zhujun Xiao, Xu Lin, Qiufan Wang, Xin Wang, Yang Yin, Fenfen Zhang, Le Li, Xiao-Dong |
| author_facet | Du, Wenying Luo, Xiaolin Jiang, Zhujun Xiao, Xu Lin, Qiufan Wang, Xin Wang, Yang Yin, Fenfen Zhang, Le Li, Xiao-Dong |
| contents | Galaxy surveys are crucial for studying large-scale structure (LSS) and cosmology, yet they face limitations--imaging surveys provide extensive sky coverage but suffer from photo-$z$ uncertainties, while spectroscopic surveys yield precise redshifts but are sample-limited. To take advantage of both photo-$z$ and spec-$z$ data while eliminating photo-$z$ errors, we propose a deep learning framework based on a dual UNet architecture that integrates these two datasets at the field level to reconstruct the 3D photo-$z$ density field. We train the network on mock samples representative of stage-IV spectroscopic surveys, utilizing CosmicGrowth simulations with a $z=0.59$ snapshot containing $2048^3$ particles in a $(1200~h^{-1}\rm Mpc)^3$ volume. Several metrics, including correlation coefficient, MAE, MSE, PSNR, and SSIM, validate the model's accuracy. Moreover, the reconstructed power spectrum closely matches the ground truth at small scales ($k \gtrsim 0.06~h/\rm Mpc$) within the $1σ$ confidence level, while the UNet model significantly improves the estimation of photo-$z$ power spectrum multipoles. This study demonstrates the potential of deep learning to enhance LSS reconstruction by using both spectroscopic and photometric data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06309 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Driven Reconstruction of Large-Scale Structure from Combined Photometric and Spectroscopic Surveys Du, Wenying Luo, Xiaolin Jiang, Zhujun Xiao, Xu Lin, Qiufan Wang, Xin Wang, Yang Yin, Fenfen Zhang, Le Li, Xiao-Dong Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Galaxy surveys are crucial for studying large-scale structure (LSS) and cosmology, yet they face limitations--imaging surveys provide extensive sky coverage but suffer from photo-$z$ uncertainties, while spectroscopic surveys yield precise redshifts but are sample-limited. To take advantage of both photo-$z$ and spec-$z$ data while eliminating photo-$z$ errors, we propose a deep learning framework based on a dual UNet architecture that integrates these two datasets at the field level to reconstruct the 3D photo-$z$ density field. We train the network on mock samples representative of stage-IV spectroscopic surveys, utilizing CosmicGrowth simulations with a $z=0.59$ snapshot containing $2048^3$ particles in a $(1200~h^{-1}\rm Mpc)^3$ volume. Several metrics, including correlation coefficient, MAE, MSE, PSNR, and SSIM, validate the model's accuracy. Moreover, the reconstructed power spectrum closely matches the ground truth at small scales ($k \gtrsim 0.06~h/\rm Mpc$) within the $1σ$ confidence level, while the UNet model significantly improves the estimation of photo-$z$ power spectrum multipoles. This study demonstrates the potential of deep learning to enhance LSS reconstruction by using both spectroscopic and photometric data. |
| title | AI-Driven Reconstruction of Large-Scale Structure from Combined Photometric and Spectroscopic Surveys |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2504.06309 |