Strong Gravitational Lensing Parameter Estimation with Vision Transformer
Paper i proceeding, 2023

Quantifying the parameters and corresponding uncertainties of hundreds of strongly lensed quasar systems holds the key to resolving one of the most important scientific questions: the Hubble constant (H0 ) tension. The commonly used Markov chain Monte Carlo (MCMC) method has been too time-consuming to achieve this goal, yet recent work has shown that convolution neural networks (CNNs) can be an alternative with seven orders of magnitude improvement in speed. With 31,200 simulated strongly lensed quasar images, we explore the usage of Vision Transformer (ViT) for simulated strong gravitational lensing for the first time. We show that ViT could reach competitive results compared with CNNs, and is specifically good at some lensing parameters, including the most important mass-related parameters such as the center of lens θ1 and θ2, the ellipticities e1 and e2, and the radial power-law slope γ′. With this promising preliminary result, we believe the ViT (or attention-based) network architecture can be an important tool for strong lensing science for the next generation of surveys. The open source of our code and data is in


Kuan Wei Huang

Carnegie Mellon University (CMU)

Geoff Chih Fan Chen

University of California

Po Wen Chang

Ohio State University

Sheng Chieh Lin

University of Kentucky

Chia-Jung Hsu

Chalmers, Fysik, E-commons

Vishal Thengane

Mohamed Bin Zayed University of Artificial Intelligence

Joshua Yao Yu Lin

University of Illinois

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 13801 LNCS 143-153
9783031250552 (ISBN)

17th European Conference on Computer Vision, ECCV 2022
Tel Aviv, Israel,



Datavetenskap (datalogi)

Annan elektroteknik och elektronik



Mer information

Senast uppdaterat