# Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique

## TL;DR

This paper introduces a computer vision-based method using Histogram of Oriented Gradients (HOG) for automatic identification of strong gravitational lensing systems in astronomical surveys, demonstrating promising results on simulated and real data.

## Contribution

The study presents a novel application of HOG features combined with supervised classification for strong lens detection, showing how training data size and parameterization affect performance.

## Key findings

- High AUC (0.975) on HST-like data with sufficient training images
- Performance varies with data type and parameterization, with some configurations achieving 0.6 AUC on real survey images
- Larger training sets improve model performance, especially when lens galaxy information is included.

## Abstract

Forthcoming surveys such as the Large Synoptic Survey Telescope (LSST) and Euclid necessitate automatic and efficient identification methods of strong lensing systems. We present a strong lensing identification approach that utilizes a feature extraction method from computer vision, the Histogram of Oriented Gradients (HOG), to capture edge patterns of arcs. We train a supervised classifier model on the HOG of mock strong galaxy-galaxy lens images similar to observations from the Hubble Space Telescope (HST) and LSST. We assess model performance with the area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve. Models trained on 10,000 lens and non-lens containing images images exhibit an AUC of 0.975 for an HST-like sample, 0.625 for one exposure of LSST, and 0.809 for 10-year mock LSST observations. Performance appears to continually improve with the training set size. Models trained on fewer images perform better in absence of the lens galaxy light. However, with larger training data sets, information from the lens galaxy actually improves model performance, indicating that HOG captures much of the morphological complexity of the arc finding problem. We test our classifier on data from the Sloan Lens ACS Survey and find that small scale image features reduces the efficiency of our trained model. However, these preliminary tests indicate that some parameterizations of HOG can compensate for differences between observed mock data. One example best-case parameterization results in an AUC of 0.6 in the F814 filter image with other parameterization results equivalent to random performance.