Abstract
The upcoming large scale surveys are expected to find approximately $10^5$ strong gravitational systems by analyzing data of many orders of magnitude than the current era. In this scenario, non-automated techniques will be highly challenging and time-consuming. We propose a new automated architecture based on the principle of self-attention to find strong gravitational lensing. The advantages of self-attention based encoder models over convolution neural networks are investigated and encoder models are analyzed to optimize performance. We constructed 21 self-attention based encoder models and four convolution neural networks trained to identify gravitational lenses from the Bologna Lens Challenge. Each model is trained separately using 18,000 simulated images, cross-validated using 2 000 images, and then applied to a test set with 100 000 images. We used four different metrics for evaluation: classification accuracy, the area under the receiver operating characteristic curve (AUROC), the $TPR_0$ score and the $TPR_{10}$ score. The performance of the self-attention based encoder models and CNN's participated in the challenge are compared. The encoder models performed better than the CNNs and surpassed the CNN models that participated in the bologna lens challenge by a high margin for the $TPR_0$ and $TPR_{10}$. In terms of the AUROC, the encoder models scored equivalent to the top CNN model by only using one-sixth parameters to that of the CNN. Self-Attention based models have a clear advantage compared to simpler CNNs. A low computational cost and complexity make it a highly competing architecture to currently used residual neural networks. Moreover, introducing the encoder layers can also tackle the over-fitting problem present in the CNN's by acting as effective filters.
Abstract (translated)
URL
https://arxiv.org/abs/2110.09202
