10 citations · 33 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
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…
Galaxy-Galaxy Strong Lensing simulation with the GPU acceleration across surveys and multi-bands
Fucheng Zhong, Ruibiao Luo, Nicola R. Napolitano +3
We present a GPU-accelerated, PyTorch tensor-based simulation framework designed to generate high-fidelity galaxy-galaxy strong lensing images. By integrating synthetic Spectral En…
Cosmology with galaxy clusters using machine learning. Application to eROSITA Data
Fucheng Zhong, Nicola R. Napolitano, Johan Comparat +8
Context: We present the first Cosmological Parameter inferences from eROSITA X-ray observations of galaxy clusters using a Machine Learning algorithm. Methods: We train a Random Fo…