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

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.
Our publicly available fine-tuned models provide a scalable transfer learning solution for gravitational lens finding in LSST.