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

## TL;DR

This paper introduces GraViT, a pipeline leveraging pretrained Vision Transformers and MLP-Mixer models for automated detection of gravitational lenses, demonstrating the effectiveness of transfer learning in astronomical image classification.

## Contribution

It presents a novel transfer learning approach using state-of-the-art vision models for gravitational lens detection, with comprehensive evaluation and benchmarking.

## Key findings

- Transfer learning improves classification accuracy.
- Vision Transformers outperform convolutional models.
- Ensemble methods enhance detection robustness.

## 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.