11 papers
Enhancing Photometric Redshift Estimation for LSST with a Hybrid LSTM-Mixture Density Network
Zhijian Luo, Yangyang Li, Xinyu Luo +4
Accurate photometric redshift (photo-) estimation and robust uncertainty quantification are essential for the LSST to achieve its precision cosmology goals. Traditional machine…
Beyond Colors: Probing Redshifts from Galaxy Morphology in Single-band Images with ViT-MDNz
Zhijian Luo, Yangyang Li, Jianzhen Chen +5
To address the challenge of estimating redshifts when only single-band images are available, this study introduces a deep learning model named ViT-MDNz. Leveraging robust statistic…
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…
Detection of Quasi-periodic Oscillations in the -Ray Light Curve of 4FGL J0309.9-6058
Jingyu Wu, Zhihao Ouyang, Hubing Xiao +7
In this work, we report, for the first time, a quasi-periodic oscillation (QPO) in the -ray band of 4FGL J0309.9-6058, also known as PKS 0308-611. We employed three analytical…
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…