Astrophysics > Cosmology and Nongalactic Astrophysics
arXiv:2611.10228 (astro-ph)
[Submitted on 6 Nov 2026]
Authors:Biwei Dai, Po-Wen Chang, Erwan Allys, Wahid Bhimji, Boris Bolliet, Thomas Borrett, Paolo Calafiura, Ragansu Chakkappai, Yuan-Tang Chou, Noé Dia, Sascha Diefenbacher, Ibrahim Elsharkawy, Steven Farrell, Sacha Guerrini, Isabelle Guyon, Chris Harris, Shih-Chieh Hsu, Wassim Kablan, François Lanusse, Celia Lecat, Julia Linhart, Yuezhou Ma, Benjamin Nachman, Shubhojit Naskar, Andy Nilipour, Laurence Perreault-Levasseur, Sébastien Pierre, Benjamin Remy, David Rousseau, Uroš Seljak, Haonan Shangguan, Sammy Nasser Sharief, Yuanxu Sun, Andreas Tersenov, Ihsan Ullah, Huikun Weng, Licong Xu, Justine Zeghal, Yulei Zhang, Hang Zhou
Abstract:Weak gravitational lensing is a powerful cosmological probe that enables direct mapping of the matter distribution and precise constraints on the fundamental parameters governing structure formation in the universe. In recent years, machine learning has shown strong potential for extracting cosmological information from weak-lensing data beyond traditional summary statistics. However, progress has been limited by the lack of standardized benchmarks that capture the complexity of the dataset, simulator imperfections, and various systematic biases encountered in realistic analyses. To address this gap, we present the FAIR Universe Weak Lensing Machine Learning Uncertainty Challenge, one of the NeurIPS 2025 competitions, as a benchmark dataset and evaluation study. Our dataset reflects realistic weak-lensing observations, incorporating multiple systematic biases, and thereby providing a controlled testbed for robust cosmological inference under uncertainties. We summarize the main methodological advances developed by the winners of our challenge, including data augmentation with conditional optimal transport, an AI-agent-driven approach for parameter inference, ensemble learning, Vision Transformer-based architectures, and physics-informed feature integration. By unifying the benchmark dataset and the winning solutions, this work provides a community resource for developing and evaluating machine learning methods for weak lensing cosmology and, more broadly, for scientific inference under systematic uncertainties.
Submission history
From: Po-Wen Chang [view email]
[v1] Fri, 6 Nov 2026 21:16:37 UTC (1,395 KB)
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