4 citations · 6 across the 6 of their papers we have counts for
7 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…
Morphology classification for galaxies in the Kilo Degree Survey using a label-efficient self-supervised learning framework
Xu Huang, Rui Li, Liang Gao +14
Galaxy morphology classification is fundamental to understanding galaxy formation and evolution. The advent of large-scale sky surveys has produced an unprecedented volume of galax…
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…
Morpho-Photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star-Quasar-Galaxy Catalog
Hai-Cheng Feng, Rui Li, Nicola R. Napolitano +14
We present a novel multimodal neural network (MNN) for classifying astronomical sources in multiband ground-based observations, from optical to near infrared, to separate sources i…