标题： GraViT：使用视觉变压器和MLP-Mixer进行强引力透镜发现的迁移学习 显示英文标题

标题： GraViT: Transfer Learning with Vision Transformers and MLP-Mixer for Strong Gravitational Lens Discovery

摘要： 引力透镜效应为研究暗物质的特性提供了一个强大的探针，并对于推断宇宙学参数至关重要。 空间和时间的遗产调查（LSST）预计在未来十年内发现O(10^5)个引力透镜，这需要自动分类器。 在本工作中，我们介绍了GraViT，这是一个用于引力透镜检测的PyTorch管道，它利用了最先进的视觉变换器（ViT）模型和MLP-Mixer的广泛预训练。 我们通过检查数据质量（源和样本量）、模型架构（选择和微调）、训练策略（增强、归一化和优化）和集成预测来评估迁移学习对分类性能的影响。 本研究重现了之前神经网络系统比较中的实验，并提供了关于在该通用测试样本上强引力透镜可检测性的见解。 我们使用来自HOLISMOKES VI和SuGOHI X的数据集对十个架构进行微调，并将其与卷积基线进行基准测试，讨论复杂性和推理时间分析。 显示英文摘要

摘要： 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.