Strong Gravitational Lensing Parameter Estimation with Vision Transformer

Document Type

Conference Proceeding

Publication Title

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

Abstract

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 https://github.com/kuanweih/strong_lensing_vit_resnet.

First Page

143

Last Page

153

DOI

10.1007/978-3-031-25056-9_10

Publication Date

2-15-2023

Keywords

Convolution neural network, Gravitational lensing, Hubble constant, Markov chain Monte Carlo method, Open-source, Orders of magnitude, Parameters estimation, Power-law, Uncertainty

Comments

IR conditions: non-described

Link to Full Text

Share

COinS