12 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…
Joint constraints on gravity and stellar orbital anisotropy in massive galaxies
Wei Du, Liping Fu, Gong-Bo Zhao +5
Strong gravitational lensing combined with stellar dynamics provides a complementary route for testing gravity on kiloparsec scales and probing the internal structure of massive ga…
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…
First Submillimeter Lights from Dome A: Tracing the Carbon Cycle in the Feedback of Massive Stars
Yan Gong, Jiaqiang Zhong, Yuan Ren +38
The cycling of carbon between its ionized, atomic, and molecular phases shapes the chemical compositions and physical conditions of the interstellar medium (ISM). However, ground-b…
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…