Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers

Jia, Peng; Sun, Ruiqi; Li, Nan; Song, Yu; Ning, Runyu; Wei, Hongyan; Luo, Rui

Abstract

Strong lensing in galaxy clusters probes properties of dense cores of dark matter halos in mass, studies the distant universe at flux levels and spatial resolutions otherwise unavailable, and constrains cosmological models independently. The next-generation large-scale sky imaging surveys are expected to discover thousands of cluster-scale strong lenses, which would lead to unprecedented opportunities for applying cluster-scale strong lenses to solve astrophysical and cosmological problems. However, the large data set challenges astronomers to identify and extract strong-lensing signals, particularly strongly lensed arcs, because of their complexity and variety. Hence, we propose a framework to detect cluster-scale strongly lensed arcs, which contains a transformer-based detection algorithm and an image simulation algorithm. We embed prior information of strongly lensed arcs at cluster scale into the training data through simulation and then train the detection algorithm with simulated images. We use the trained transformer to detect strongly lensed arcs from simulated and real data. Results show that our approach could achieve 99.63% accuracy rate, 90.32% recall rate, 85.37% precision rate, and 0.23% false-positive rate in detection of strongly lensed arcs from simulated images and could detect almost all strongly lensed arcs in real observation images. Besides, with an interpretation method, we have shown that our method could identify important information embedded in simulated data. Next, to test the reliability and usability of our approach, we will apply it to available observations (e.g., DESI Legacy Imaging Surveys6) and simulated data of upcoming large-scale sky surveys, such as Euclid7and the China Space Station Telescope.

Citation

Jia, P., Sun, R., Li, N., Song, Y., Ning, R., Wei, H., & Luo, R. (2023). Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers. Astronomical Journal, 165(1), Article 26. https://doi.org/10.3847/1538-3881/aca1c2

• Journal Article Type: Article
• Acceptance Date: Nov 8, 2022
• Online Publication Date: Dec 22, 2022
• Publication Date: Jan 1, 2023
• Deposit Date: Nov 1, 2023
• Publicly Available Date: Nov 1, 2023
• Journal: The Astronomical Journal
• Print ISSN: 0004-6256
• Electronic ISSN: 1538-3881
• Publisher: IOP Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 165
• Issue: 1
• Article Number: 26
• DOI: https://doi.org/10.3847/1538-3881/aca1c2
• Keywords: Space and Planetary Science; Astronomy and Astrophysics
• Public URL: https://durham-repository.worktribe.com/output/1873830