Extracting Cosmological Information from Lightcone Data: A Comparison of CNNs and Summary-Statistic-Based Approaches
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912985230868480 |
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| author | Zhiwei, Min Xu, Xiao Zhujun, Jiang Liang, Xiao Fenfen, Yin Jiacheng, Ding Haitao, Miao Shupei, Chen Qiufan, Lin yang, Wang Le, Zhang XiaoDong, Li |
| author_facet | Zhiwei, Min Xu, Xiao Zhujun, Jiang Liang, Xiao Fenfen, Yin Jiacheng, Ding Haitao, Miao Shupei, Chen Qiufan, Lin yang, Wang Le, Zhang XiaoDong, Li |
| contents | Lightcone observations are the natural data format of galaxy surveys, but their evolving geometry breaks the translational symmetry assumed by standard convolutional neural networks (CNNs). In particular, applying CNNs to 3D gridded lightcone data implicitly treats the line-of-sight direction as translationally invariant, despite encoding cosmic time evolution. We propose a simple alternative (CNN+2D) that divides the lightcone into redshift slices, projects each onto a HEALPix sphere, and analyzes them with a 2D CNN. Using \texttt{AbacusSummit} halo lightcone mocks ($0.3<z<0.8$, $40^\circ\times40^\circ$), we compare this approach with fully connected networks (FC) applied to different summary statistics, including spherical harmonic coefficients ($a_{\ell m}$), wavelet scattering transform (WST) coefficients, and the angular two-point correlation function (2PCF), along with standard 2PCF likelihood and Fisher forecasts. We find that multiple statistics beyond CNNs can achieve competitive performance: FC networks combined with $a_{\ell m}$ and especially WST significantly outperform 2PCF-based methods, with FC+WST yielding the best overall parameter constraints across cosmologies. Meanwhile, for a fiducial cosmology with multiple realizations, the CNN+2D approach achieves the smallest statistical uncertainties. These results demonstrate that both learned features and carefully constructed summary statistics can effectively extract cosmological information from lightcone data, providing flexible and robust analysis strategies for upcoming surveys such as DESI, Euclid, and CSST. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_26533 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Extracting Cosmological Information from Lightcone Data: A Comparison of CNNs and Summary-Statistic-Based Approaches Zhiwei, Min Xu, Xiao Zhujun, Jiang Liang, Xiao Fenfen, Yin Jiacheng, Ding Haitao, Miao Shupei, Chen Qiufan, Lin yang, Wang Le, Zhang XiaoDong, Li Cosmology and Nongalactic Astrophysics Lightcone observations are the natural data format of galaxy surveys, but their evolving geometry breaks the translational symmetry assumed by standard convolutional neural networks (CNNs). In particular, applying CNNs to 3D gridded lightcone data implicitly treats the line-of-sight direction as translationally invariant, despite encoding cosmic time evolution. We propose a simple alternative (CNN+2D) that divides the lightcone into redshift slices, projects each onto a HEALPix sphere, and analyzes them with a 2D CNN. Using \texttt{AbacusSummit} halo lightcone mocks ($0.3<z<0.8$, $40^\circ\times40^\circ$), we compare this approach with fully connected networks (FC) applied to different summary statistics, including spherical harmonic coefficients ($a_{\ell m}$), wavelet scattering transform (WST) coefficients, and the angular two-point correlation function (2PCF), along with standard 2PCF likelihood and Fisher forecasts. We find that multiple statistics beyond CNNs can achieve competitive performance: FC networks combined with $a_{\ell m}$ and especially WST significantly outperform 2PCF-based methods, with FC+WST yielding the best overall parameter constraints across cosmologies. Meanwhile, for a fiducial cosmology with multiple realizations, the CNN+2D approach achieves the smallest statistical uncertainties. These results demonstrate that both learned features and carefully constructed summary statistics can effectively extract cosmological information from lightcone data, providing flexible and robust analysis strategies for upcoming surveys such as DESI, Euclid, and CSST. |
| title | Extracting Cosmological Information from Lightcone Data: A Comparison of CNNs and Summary-Statistic-Based Approaches |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2603.26533 |