9 citations · 18 across the 6 of their papers we have counts for
6 papers
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…
Photometric Redshift Estimation with Convolutional Neural Networks and Galaxy Images: A Case Study of Resolving Biases in Data-Driven Methods
Q. Lin, D. Fouchez, J. Pasquet +4
Deep Learning models have been increasingly exploited in astrophysical studies, yet such data-driven algorithms are prone to producing biased outputs detrimental for subsequent ana…
Galaxy Image Translation with Semi-supervised Noise-reconstructed Generative Adversarial Networks
Qiufan Lin, Dominique Fouchez, Jérôme Pasquet
Image-to-image translation with Deep Learning neural networks, particularly with Generative Adversarial Networks (GANs), is one of the most powerful methods for simulating astronom…