4 citations · 4 across the 8 of their papers we have counts for
10 papers
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…
Identifying Dust-lane Spheroidal Galaxies in DESI Legacy Imaging Surveys Using Semi-Supervised Methods
Zhijian Luo, Jianzhen Chen, Wenxiang Pei +4
Dust-lane spheroidal galaxies (DLSGs) are unique astrophysical systems that exhibit the morphology of early-type galaxies (ETGs) but are distinguished by prominent dust lanes. Rece…
The averaged broadband spectral energy distribution study of Fermi bright BL Lac objects
Hubing Xiao, Haitao Cao, Rui Xue +6
The physics-determined broadband spectral energy distributions (SEDs) of blazars have been widely used to study the property during their flaring/outburst states, while the non-fla…
Detecting Galactic Rings in the DESI Legacy Imaging Surveys with Semi-Supervised Deep Learning
Jianzhen Chen, Zhijian Luo, Cheng Cheng +3
The ring structures of disk galaxies are vital for understanding galaxy evolution and dynamics. However, due to the scarcity of ringed galaxies and challenges in their identificati…
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…