4 citations · 9 across the 5 of their papers we have counts for
5 papers
Extracting Cosmological Information from Lightcone Data: A Comparison of CNNs and Summary-Statistic-Based Approaches
Min Zhiwei, Xiao Xu, Jiang Zhujun +9
Lightcone observations are the natural data format of galaxy surveys, but their evolving geometry breaks the translational symmetry assumed by standard convolutional neural network…
Interpreting deep learning-based stellar mass estimation via causal analysis and mutual information decomposition
Wei Zhang, Qiufan Lin, Yuan-Sen Ting +4
End-to-end deep learning models fed with multi-band galaxy images are powerful data-driven tools used to estimate galaxy physical properties in the absence of spectroscopy. However…
Investigation on deep learning-based galaxy image translation models
Hengxin Ruan, Qiufan Lin, Shupei Chen +2
Galaxy image translation is an important application in galaxy physics and cosmology. With deep learning-based generative models, image translation has been performed for image gen…
CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimation
Qiufan Lin, Hengxin Ruan, Dominique Fouchez +6
Obtaining well-calibrated photometric redshift probability densities for galaxies without a spectroscopic measurement remains a challenge. Deep learning discriminative models, typi…
The multi-component fitting to the star formation histories in the TNG simulation
Yang Wang, Chengxing Dong, Hengxin Ruan +3
The star formation history (SFH) is a key issue in the evolution of galaxies. In this work, we developed a model based on a Gaussian and gamma function mixture to fit SFHs with var…