5 papers
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…
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…