# A Comparative Study of Convolutional Neural Networks for the Detection of Strong Gravitational Lensing

arXiv: https://arxiv.org/abs/2106.01754 · HTML: https://mlsanity.com/paper/2106.01754/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2021.

## Built on by (most cited first, 2 of 2)

- [Radio astronomical images object detection and segmentation: A benchmark on deep learning methods](https://mlsanity.com/paper/2303.04506/) (2023)
- [Finding Strong Gravitational Lenses Through Self-Attention](https://mlsanity.com/paper/2110.09202/) (2021)

## Abstract (arXiv)

As we enter the era of large-scale imaging surveys with the up-coming telescopes such as LSST and SKA, it is envisaged that the number of known strong gravitational lensing systems will increase dramatically. However, these events are still very rare and require the efficient processing of millions of images. In order to tackle this image processing problem, we present Machine Learning techniques and apply them to the Gravitational Lens Finding Challenge. The Convolutional Neural Networks (CNNs) presented have been re-implemented within a new modular, and extendable framework, LEXACTUM. We report an Area Under the Curve (AUC) of 0.9343 and 0.9870, and an execution time of 0.0061s and 0.0594s per image, for the Space and Ground datasets respectively, showing that the results obtained by CNNs are very competitive with conventional methods (such as visual inspection and arc finders) for detecting gravitational lenses.