A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping

2026-03-23

AI Summary

This work addresses the challenge of reconstructing dark matter distributions and quantifying associated uncertainties from noisy weak gravitational lensing shear data. The authors propose PnPMass, a method that alternates between data-fidelity gradient descent and plug-and-play optimization using a single pre-trained denoising network, enabling efficient and accurate mass mapping without retraining for different sky regions. By innovatively integrating moment networks with conformal prediction, the approach delivers sampling-free, tight, and coverage-guaranteed uncertainty estimates. PnPMass achieves reconstruction accuracy comparable to state-of-the-art deep learning models while significantly improving inference efficiency and generalizing across varying observational noise levels with only a single training run.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Stochastic Optimization
• Computer Vision: Learning & Optimization for CV

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Upcoming stage-IV surveys such as Euclid and Rubin will deliver vast amounts of high-precision data, opening new opportunities to constrain cosmological models with unprecedented accuracy. A key step in this process is the reconstruction of the dark matter distribution from noisy weak lensing shear measurements. Current deep learning-based mass mapping methods achieve high reconstruction accuracy, but either require retraining a model for each new observed sky region (limiting practicality) or rely on slow MCMC sampling. Efficient exploitation of future survey data therefore calls for a new method that is accurate, flexible, and fast at inference. In addition, uncertainty quantification with coverage guarantees is essential for reliable cosmological parameter estimation. We introduce PnPMass, a plug-and-play approach for weak lensing mass mapping. The algorithm produces point estimates by alternating between a gradient descent step with a carefully chosen data fidelity term, and a denoising step implemented with a single deep learning model trained on simulated data corrupted by Gaussian white noise. We also propose a fast, sampling-free uncertainty quantification scheme based on moment networks, with calibrated error bars obtained through conformal prediction to ensure coverage guarantees. Finally, we benchmark PnPMass against both model-driven and data-driven mass mapping techniques. PnPMass achieves performance close to that of state-of-the-art deep-learning methods while offering fast inference (converging in just a few iterations) and requiring only a single training phase, independently of the noise covariance of the observations. It therefore combines flexibility, efficiency, and reconstruction accuracy, while delivering tighter error bars than existing approaches, making it well suited for upcoming weak lensing surveys.

Problem

• weak lensing
• mass mapping
• uncertainty quantification
• cosmological parameter estimation
• dark matter reconstruction

Innovation

• plug-and-play
• uncertainty quantification
• weak lensing mass mapping
• conformal prediction
• deep denoising