Optimizing machine learning methods to discover strong gravitational lenses in the Deep Lens Survey

TL;DR

This study demonstrates that semi-supervised machine learning models, especially with data augmentation and GAN-generated images, significantly improve the efficiency of discovering strong gravitational lenses in large imaging surveys, reducing human effort.

Contribution

It introduces a semi-supervised learning approach with data augmentation and GANs that outperforms traditional supervised methods in lens detection accuracy.

Findings

Semi-supervised models achieve 5-10 times better precision than supervised models.

Application to the DLS survey yields multiple high-confidence lens candidates.

Spectroscopic follow-up confirms the lensing nature of two candidates.

Abstract

Machine learning models can greatly improve the search for strong gravitational lenses in imaging surveys by reducing the amount of human inspection required. In this work, we test the performance of supervised, semi-supervised, and unsupervised learning algorithms trained with the ResNetV2 neural network architecture on their ability to efficiently find strong gravitational lenses in the Deep Lens Survey (DLS). We use galaxy images from the survey, combined with simulated lensed sources, as labeled data in our training datasets. We find that models using semi-supervised learning along with data augmentations (transformations applied to an image during training, e.g., rotation) and Generative Adversarial Network (GAN) generated images yield the best performance. They offer 5--10 times better precision across all recall values compared to supervised algorithms. Applying the best performing models to the full 20 deg$^2$ DLS survey, we find 3 Grade-A lens candidates within the top 17 image predictions from the model. This increases to 9 Grade-A and 13 Grade-B candidates when $1$% ($\sim2500$ images) of the model predictions are visually inspected. This is $\gtrsim10\times$ the sky density of lens candidates compared to current shallower wide-area surveys (such as the Dark Energy Survey), indicating a trove of lenses awaiting discovery in upcoming deeper all-sky surveys. These results suggest that pipelines tasked with finding strong lens systems can be highly efficient, minimizing human effort. We additionally report spectroscopic confirmation of the lensing nature of two Grade-A candidates identified by our model, further validating our methods.