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| Main Authors: | , |
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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2605.23114 |
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| _version_ | 1866914590600724480 |
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| author | Li, Guangjian Kacprzak, Tomasz |
| author_facet | Li, Guangjian Kacprzak, Tomasz |
| contents | Weak gravitational lensing maps compactly encode the evolution of cosmic large-scale structure and are a key tool for cosmological analyses. Performing inference directly at the map level allows flexible choices of statistics and can increase constraining power. Conventional methods rely solely on N-body simulations and are computationally expensive. Generative machine-learning emulators can accelerate map-level theory prediction. However, existing GAN-based map-level surrogates still have limited statistical fidelity. They can produce over-smoothed maps, may fail to capture the full distribution of generated map sets and can be difficult to train. Continuous normalizing flows trained with flow matching have recently emerged as a powerful class of generative models. We present a residual label-conditional flow matching generative network that conditions explicitly on the matter density Omega_m and clustering amplitude sigma_8 for a fixed source redshift distribution n(z). The model learns a continuous probability flow in a residual space from label-specific noise distributions to convergence maps. We evaluate it using pixel and peak statistics, the power spectrum, bispectrum, power-spectrum correlation matrices, and other validation metrics. Compared with the previous GAN benchmark, the proposed method improves the typical fidelity of generated maps from below 10% and below 20% to below 1% and below 5% for basic and higher-order statistics, respectively. The agreement at the level of map distributions is also very good: maps generated from random noise match well the distribution of maps generated with N-body simulations from random initial conditions. This work brings us closer to a practical mass-map emulator that captures the cosmological signal while supporting multiple forms of data analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_23114 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Increasing the Precision of Surrogate Models for Weak Lensing Mass Maps with Flow Matching Li, Guangjian Kacprzak, Tomasz Cosmology and Nongalactic Astrophysics Weak gravitational lensing maps compactly encode the evolution of cosmic large-scale structure and are a key tool for cosmological analyses. Performing inference directly at the map level allows flexible choices of statistics and can increase constraining power. Conventional methods rely solely on N-body simulations and are computationally expensive. Generative machine-learning emulators can accelerate map-level theory prediction. However, existing GAN-based map-level surrogates still have limited statistical fidelity. They can produce over-smoothed maps, may fail to capture the full distribution of generated map sets and can be difficult to train. Continuous normalizing flows trained with flow matching have recently emerged as a powerful class of generative models. We present a residual label-conditional flow matching generative network that conditions explicitly on the matter density Omega_m and clustering amplitude sigma_8 for a fixed source redshift distribution n(z). The model learns a continuous probability flow in a residual space from label-specific noise distributions to convergence maps. We evaluate it using pixel and peak statistics, the power spectrum, bispectrum, power-spectrum correlation matrices, and other validation metrics. Compared with the previous GAN benchmark, the proposed method improves the typical fidelity of generated maps from below 10% and below 20% to below 1% and below 5% for basic and higher-order statistics, respectively. The agreement at the level of map distributions is also very good: maps generated from random noise match well the distribution of maps generated with N-body simulations from random initial conditions. This work brings us closer to a practical mass-map emulator that captures the cosmological signal while supporting multiple forms of data analysis. |
| title | Increasing the Precision of Surrogate Models for Weak Lensing Mass Maps with Flow Matching |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2605.23114 |