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.

翻译：即将开展的第四代巡天项目（如Euclid和Rubin）将提供大量高精度数据，为以空前精度约束宇宙学模型开辟新机遇。这一过程中的关键步骤是从含噪声的弱引力透镜剪切测量中重建暗物质分布。当前基于深度学习的质量图构建方法虽能实现高重建精度，但要么需要针对每个新观测天区重新训练模型（限制实用性），要么依赖缓慢的MCMC采样。因此，高效开发未来巡天数据需要一种精确、灵活且推断速度快的全新方法。此外，具有覆盖保证的不确定性量化对于可靠的宇宙学参数估计至关重要。我们提出PnPMass——一种用于弱引力透镜质量图构建的即插即用方法。该算法通过交替执行梯度下降步骤（含精心选择的数据保真项）和去噪步骤（使用在含高斯白噪声的模拟数据上训练的单一深度学习模型实现）来产生点估计。我们还提出了一种基于矩网络的快速无采样不确定性量化方案，通过保形预测校准误差棒以确保覆盖保证。最后，我们将PnPMass与模型驱动和数据驱动的质量图构建技术进行基准测试。PnPMass在接近最先进深度学习方法性能的同时，实现了快速推断（仅需数次迭代即可收敛）且仅需单次训练阶段，与观测噪声协方差无关。因此，该方法结合了灵活性、高效性和重建精度，并提供了比现有方法更紧凑的误差棒，非常适合即将开展的弱引力透镜巡天。