Masked Autoencoder Pretraining on Strong-Lensing Images for Joint Dark-Matter Model Classification and Super-Resolution

• URL: http://arxiv.org/abs/2512.06642v1
• Date: Sun, 07 Dec 2025 03:25:19 GMT
• Title: Masked Autoencoder Pretraining on Strong-Lensing Images for Joint Dark-Matter Model Classification and Super-Resolution
• Authors: Achmad Ardani Prasha, Clavino Ourizqi Rachmadi, Muhamad Fauzan Ibnu Syahlan, Naufal Rahfi Anugerah, Nanda Garin Raditya, Putri Amelia, Sabrina Laila Mutiara, Hilman Syachr Ramadhan,
• Abstract summary: Masked autoencoder (MAE) pretraining strategy on simulated strong-lensing images from DeepLense ML4SCI benchmark.<n>We pretrain a Vision Transformer encoder using a masked image modeling objective, then fine-tune the encoder separately for each task.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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