Transformers as Strong Lens Detectors- From Simulation to Surveys

Abstract

With the upcoming large-scale surveys like LSST, we expect to find approximately $10^5$ strong gravitational lenses among data of many orders of magnitude larger. In this scenario, the usage of non-automated techniques is too time-consuming and hence impractical for science. For this reason, machine learning techniques started becoming an alternative to previous methods. In our previous work, we proposed a new machine learning architecture based on the principle of self-attention, trained to find strong gravitational lenses on simulated data from the Bologna Lens Challenge. Self-attention-based models have clear advantages compared to simpler CNNs and highly competing performance in comparison to the current state-of-art CNN models. We apply the proposed model to the Kilo Degree Survey, identifying some new strong lens candidates. However, these have been identified among a plethora of false positives, which made the application of this model not so advantageous. Therefore, throughout this paper, we investigate the pitfalls of this approach, and possible solutions, such as transfer learning, are proposed.

9 pages, 9 figures

References in corpus (16)

• H0LiCOW V. New COSMOGRAIL time delays of HE0435-1223: $H_0$ to 3.8% precision from strong lensing in a flat $\Lambda$CDM model
• The fourth data release of the Kilo-Degree Survey: ugri imaging and nine-band optical-IR photometry over 1000 square degrees
• Stand-Alone Self-Attention in Vision Models
• Cosmological Constraints from Strong Gravitational Lensing in Galaxy Clusters
• Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
• LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using Convolutional Neural Networks
• Super-resolving distant galaxies with gravitational telescopes: Keck-LGSAO and Hubble imaging of the lens system SDSSJ0737+3216
• Strong lens systems search in the Dark Energy Survey using Convolutional Neural Networks
• Using Convolutional Neural Networks to identify Gravitational Lenses in Astronomical images
• On the Power Spectrum of Dark Matter Substructure in Strong Gravitational Lenses
• Deep Learning for Strong Lensing Search: Tests of the Convolutional Neural Networks and New Candidates from KiDS DR3
• North Ecliptic Pole merging galaxy catalogue
• Detecting gravitational lenses using machine learning: exploring interpretability and sensitivity to rare lensing configurations
• Finding Strong Gravitational Lenses Through Self-Attention
• Strong Lensing considerations for the LSST observing strategy
• Euclid preparation: I. The Euclid Wide Survey

Cited by in corpus (1)

• TEGLIE: Transformer encoders as strong gravitational lens finders in KiDS