15 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…
Tracing the kinematic perturbations of the Milky Way spiral arms with APOGEE DR17 and Gaia DR3
Xi-Can Tang, Zhi Li, Iulia T. Simion +4
Aims. We constrain the dynamical perturbations of the spiral arms in the Milky Way disk, based on the non-axisymmetric streaming motions of RGB stars revealed by APOGEE and \textit…
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…
Chasing the neutrino blazar candidates II: SED modeling with hadronic model
Hubing Xiao, Zhihao Ouyang, Lili Yang +6
Blazars are promising candidates for high energy neutrino sources, yet the physical origin of their neutrino emission remains uncertain. In this work, we extend our previous study…
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…