1 citations · 4 across the 10 of their papers we have counts for
10 papers
Reducing False Positives in Strong-Lens Searches with Generalized-Mean Consensus of Machine-Learning Ensembles in the Kilo-Degree Survey
Ziqi Li, Rui Li, Xu Huang +11
Context. In wide-field surveys, the main challenge is not just classifier sensitivity, but the overwhelming number of false positives. Searching for strong lenses among millions to…
LenNet: Direct Detection and Localization of Strong Gravitational Lenses in Wide-Field Sky Survey Images
Pufan Liu, Hui Li, Ziqi Li +9
Strong gravitational lenses are invaluable tools for addressing fundamental questions in astrophysics, from the nature of dark matter to the expansion of the universe. While curren…
Identification of gravitational lenses obscured by foreground light in the KiDS dataset using U-Nets and ResNets
S. Liu, Rui Li, J. Jia +13
*Context.* Many lensing images are often obscured by foreground light from the central galaxies, making them challenging to detect. *Aims.* To address the limitations of previous l…
MIU2Net: weak-lensing mass inversion using deep learning with nested U-structures
Han W. G., An Zhao, Xinyue Chen +5
One of the primary goals of next-generation gravitational lensing surveys is to measure the large-scale distribution of dark matter, which requires accurate mass inversion to conve…
Using Deep Learning Methods to Detect for Ultra-diffuse Galaxies in KiDS
Hao Su, Rui Li, Nicola R. Napolitano +13
Ultra-diffuse Galaxies (UDGs) are a subset of Low Surface Brightness Galaxies (LSBGs), showing mean effective surface brightness fainter than and a d…
CSST Strong Lensing Preparation: Fast Modeling of Galaxy-Galaxy Strong Lenses in the Big Data Era
Xiaoyue Cao, Ran Li, Nan Li +4
Galaxy-galaxy strong lensing provides a powerful probe of galaxy formation, evolution, and the properties of dark matter and dark energy. However, conventional lens-modeling approa…
Automation of finding strong gravitational lenses in the Kilo Degree Survey with U-DenseLens (DenseLens + Segmentation)
Bharath Chowdhary Nagam, Léon V E Koopmans, Edwin A Valentijn +7
In the context of upcoming large-scale surveys like Euclid, the necessity for the automation of strong lens detection is essential. While existing machine learning pipelines heavil…
Galaxy-Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting 2-Dimensional Information from Multi-Band Images in Ground and Space Observations
Fucheng Zhong, Ruibiao Luo, Nicola R. Napolitano +6
We present a novel deep learning method to separately extract the two-dimensional flux information of the foreground galaxy (deflector) and background system (source) of Galaxy-Gal…
Using Convolutional Neural Networks to Search for Strongly Lensed Quasars in KiDS DR5
Zizhao He, Rui Li, Yiping Shu +13
Gravitationally strongly lensed quasars (SL-QSO) offer invaluable insights into cosmological and astrophysical phenomena. With the data from ongoing and next-generation surveys, th…
CSST Strong Lensing Preparation: Forecasting the galaxy-galaxy strong lensing population for the China Space Station Telescope
Xiaoyue Cao, Ran Li, Nan Li +8
Galaxy-galaxy strong gravitational lensing (GGSL) is a powerful probe for the formation and evolution of galaxies and cosmology, while the sample size of GGSLs leads to considerabl…