用掩码自编码器预训练，让模型同时学会分辨暗物质类型和提升引力透镜图像清晰度。
Masked Autoencoder Pretraining on Strong-Lensing Images for Joint Dark-Matter Model Classification and Super-Resolution
- 在模拟透镜图像上用掩码自编码预训练视觉变换器，学习通用特征表示。
- 分类任务准确率达88.65%，超分辨率重建PSNR达33dB，性能优于从零开始训练。
- 同一编码器可支持多任务，适合从事天体物理与图像重建的研究者使用。
强引力透镜可揭示星系中暗物质子结构的影响，但噪声大、分辨率低的图像分析极具挑战。本文提出在DeepLense ML4SCI基准的模拟强透镜图像上进行掩码自编码器（MAE）预训练，以学习适用于两个下游任务的通用表征：(i) 分类暗物质模型（冷暗物质、轴子样或无子结构）；(ii) 通过超分辨率增强低分辨率透镜图像。采用掩码图像建模目标预训练视觉变换器编码器，再分别对每个任务微调。结果表明，结合合适掩码比例的MAE预训练，使共享编码器性能达到或超过从零开始训练的ViT。在90%掩码率下，分类器宏AUC达0.968，准确率88.65%，优于基线（AUC 0.957，准确率82.46%）。超分辨率（16×16到64×64）重建的图像PSNR约33 dB，SSIM达0.961，略优于基线。消融实验显示掩码率存在权衡：更高掩码率利于分类但略微降低重建质量。研究证明，在物理信息丰富的模拟数据上进行MAE预训练，能为多种强透镜分析任务提供灵活可复用的编码器。
原文摘要 · Abstract (English)
Strong gravitational lensing can reveal the influence of dark-matter substructure in galaxies, but analyzing these effects from noisy, low-resolution images poses a significant challenge. In this work, we propose a masked autoencoder (MAE) pretraining strategy on simulated strong-lensing images from the DeepLense ML4SCI benchmark to learn generalizable representations for two downstream tasks: (i) classifying the underlying dark matter model (cold dark matter, axion-like, or no substructure) and (ii) enhancing low-resolution lensed images via super-resolution. We pretrain a Vision Transformer encoder using a masked image modeling objective, then fine-tune the encoder separately for each task. Our results show that MAE pretraining, when combined with appropriate mask ratio tuning, yields a shared encoder that matches or exceeds a ViT trained from scratch. Specifically, at a 90% mask ratio, the fine-tuned classifier achieves macro AUC of 0.968 and accuracy of 88.65%, compared to the scratch baseline (AUC 0.957, accuracy 82.46%). For super-resolution (16x16 to 64x64), the MAE-pretrained model reconstructs images with PSNR ~33 dB and SSIM 0.961, modestly improving over scratch training. We ablate the MAE mask ratio, revealing a consistent trade-off: higher mask ratios improve classification but slightly degrade reconstruction fidelity. Our findings demonstrate that MAE pretraining on physics-rich simulations provides a flexible, reusable encoder for multiple strong-lensing analysis tasks.
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