Abstract
Gravitational lensing offers a powerful probe into the properties of dark matter and is crucial to infer cosmological parameters. The Legacy Survey of Space and Time (LSST) is predicted to find O(10^5) gravitational lenses over the next decade, demanding automated classifiers. In this work, we introduce GraViT, a PyTorch pipeline for gravitational lens detection that leverages extensive pretraining of state-of-the-art Vision Transformer (ViT) models and MLP-Mixer. We assess the impact of transfer learning on classification performance by examining data quality (source and sample size), model architecture (selection and fine-tuning), training strategies (augmentation, normalization, and optimization), and ensemble predictions. This study reproduces the experiments in a previous systematic comparison of neural networks and provides insights into the detectability of strong gravitational lenses on that common test sample. We fine-tune ten architectures using datasets from HOLISMOKES VI and SuGOHI X, and benchmark them against convolutional baselines, discussing complexity and inference-time analysis.
Abstract (translated)
引力透镜效应为探索暗物质的性质提供了强有力的工具，并且对于推断宇宙学参数至关重要。根据预测，为期十年的时间和空间遗产调查（LSST）将发现大约10^5个引力透镜现象，这需要自动分类器的支持。在这项工作中，我们介绍了一个名为GraViT的PyTorch管道，用于探测引力透镜效应，该系统利用了最先进的视觉变换器(ViT)模型和MLP-Mixer的广泛预训练。 我们在数据质量（源质量和样本大小）、模型架构（选择和微调）、训练策略（增强、归一化和优化）以及集成预测方面评估了迁移学习对分类性能的影响。这项研究重现了先前关于神经网络系统比较实验的结果，并提供了在公共测试样本上强引力透镜检测可行性的见解。 我们使用HOLISMOKES VI和SuGOHI X的数据集，对十种架构进行了微调，并与卷积基线进行了基准测试，讨论了复杂性和推理时间的分析。
URL
https://arxiv.org/abs/2509.00226
