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Comparative analysis of missing data imputation methods for CSST survey: Impact on photometric redshift estimation performance
Ling Wang, Zhu Chen, Zhijian Luo +42
Improving the accuracy of photometric redshifts (photo-) is essential for reliable statistical studies of cosmology and galaxy evolution. However, missing photometric bands are…
LSTM-MDNz: Estimating Quasar Photometric Redshifts with an LSTM-Augmented Mixture Density Network
Jianzhen Chen, Zhijian Luo, Liping Fu +4
Quasar photometric redshifts are essential for studying cosmology and large-scale structures. However, their complex spectral energy distributions cause significant redshift-color…
BALNet: Deep Learning-Based Detection and Measurement of Broad Absorption Lines in Quasar Spectra
Yangyang Li, Zhijian Luo, Shaohua Zhang +5
Broad absorption line (BAL) quasars serve as critical probes for understanding active galactic nucleus (AGN) outflows, black hole accretion, and cosmic evolution. To address the li…
Galaxy Morphology Classification via Deep Semi-Supervised Learning with Limited Labeled Data
Zhijian Luo, Jianzhen Chen, Zhu Chen +4
Galaxy morphology classification plays a crucial role in understanding the structure and evolution of the universe. With galaxy observation data growing exponentially, machine lear…
Photometric Redshift Estimation for CSST Survey with LSTM Neural Networks
Zhijian Luo, Yicheng Li, Junhao Lu +11
Accurate estimation of photometric redshifts (photo-s) is crucial for cosmological surveys. Various methods have been developed for this purpose, such as template fitting method…