Abstract
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.
Abstract (translated)
强引力透镜效应可以揭示星系中暗物质亚结构的影响，但从嘈杂、低分辨率的图像中分析这些影响是一个重大的挑战。在本工作中，我们提出了一种使用DeepLense ML4SCI基准中的模拟强引力透镜图像对掩码自动编码器（MAE）进行预训练的战略，以学习适用于两个下游任务的一般化表示：(i) 分类底层暗物质模型（冷暗物质、类似轴子或无亚结构），以及(ii) 通过超分辨率增强低分辨率的透镜图像。我们使用带掩码图像建模目标对视觉变换器编码器进行预训练，然后分别针对每个任务微调该编码器。 实验结果表明，当与适当的掩码比率调整相结合时，MAE 预训练可以生成一个共享编码器，其性能可匹敌或超越从头开始训练的ViT。特别是在90% 的掩码比率下，经过微调的分类器实现了宏AUC值为0.968和准确性为88.65%，相比之下，从零开始的基准模型（宏AUC 0.957，准确性 82.46%)。 对于超分辨率任务（从16x16到64x64），MAE预训练模型重建图像时获得了约33 dB 的PSNR值和SSIM为0.961的得分，这比从零开始训练略有提升。通过对MAE掩码比率进行消融分析，我们发现了一个一致的权衡：更高的掩码比率可以提高分类性能，但稍微降低了重建保真度。 我们的研究结果表明，通过在富含物理特性的模拟数据上对MAE进行预训练，可以获得一个灵活且可重用的编码器，用于多种强引力透镜分析任务。
URL
https://arxiv.org/abs/2512.06642
