10 citations · 33 across the 10 of their papers we have counts for
12 papers
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…
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…
Galaxy Spectra Networks (GaSNet). III. Generative pre-trained network for spectrum reconstruction, redshift estimate and anomaly detection
Fucheng Zhong, Nicola R. Napolitano, Caroline Heneka +11
Classification of spectra (1) and anomaly detection (2) are fundamental steps to guarantee the highest accuracy in redshift measurements (3) in modern all-sky spectroscopic surveys…