6 citations · 11 across the 5 of their papers we have counts for
6 papers
NERV: Neural-network Enhanced Reconstruction of the UniVerse with Application to Baryon Acoustic Oscillations in the BOSS DR12 Galaxy Sample
Shi-Hui Zang, Hong-Ming Zhu, Tian-Xiang Mao +1
We present the first application of neural-network-based baryon acoustic oscillation (BAO) reconstruction to real galaxy survey data, restoring the acoustic signature damped by non…
A widely applicable Galaxy Group finder Using Machine Learning
Juntao Ma, Jie Wang, Tianxiang Mao +4
Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated hal…
Prediction of Individual Halo Concentrations Across Cosmic Time Using Neural Networks
Tianchi Zhang, Tianxiang Mao, Wenxiao Xu +1
The concentration of dark matter haloes is closely linked to their mass accretion history. We utilize the halo mass accretion histories from large cosmological N-body simulations a…
Half a Million Binary Stars from the low resolution spectra of LAMOST
Yingjie Jing, Tian-Xiang Mao, Jie Wang +2
Binary stars are prevalent yet challenging to detect. We present a novel approach using convolutional neural networks (CNNs) to identify binary stars from low-resolution spectra ob…
Estimation of line-of-sight velocities of individual galaxies using neural networks I. Modelling redshift-space distortions at large scales
Hongxiang Chen, Jie Wang, Tianxiang Mao +7
We present a scheme based on artificial neural networks (ANN) to estimate the line-of-sight velocities of individual galaxies from an observed redshift-space galaxy distribution. W…
Cosmic Tidal Reconstruction with Halo Fields
Hong-Ming Zhu, Tian-Xiang Mao, Ue-Li Pen
The gravitational coupling between large-scale perturbations and small-scale perturbations leads to anisotropic distortions of the small-scale matter distribution. The measured loc…