67 citations · 501 across the 27 of their papers we have counts for
26 papers
A Catalog of 1,408 Carbon-Enhanced Metal-Poor Stars from LAMOST DR11
Xianqi Liu, Xiangru Li, Ziyu Fang
Metal-poor (MP) stars are important targets for investigating the chemical evolution of the early universe. Among them, Carbon-Enhanced Metal-Poor (CEMP) stars have attracted exten…
A Weak Signal Learning Dataset and Its Baseline Method
Xianqi Liu, Xiangru Li, Lefeng He +1
Weak signal learning (WSL) is a common challenge in many fields like fault diagnosis, medical imaging, and autonomous driving, where critical information is often masked by noise a…
A Catalog of 12,766 Carbon-enhanced Metal-poor Stars from LAMOST Data Release 8
Ziyu Fang, Xiangru Li, Haining Li
Metal-poor stars are a rare and ancient type of stars; Carbon-Enhanced Metal-Poor (CEMP) stars are a subset of these celestial bodies that show an enrichment of carbon relative to…
A Versatile Framework for Analyzing Galaxy Image Data by Implanting Human-in-the-loop on a Large Vision Model
Mingxiang Fu, Yu Song, Jiameng Lv +14
The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the Universe. However, effectively analyzing this vast amount…
Deep learning for cosmological parameter inference from a dark matter halo density field
Zhiwei Min, Xu Xiao, Jiacheng Ding +11
We propose a lightweight deep convolutional neural network (lCNN) to estimate cosmological parameters from simulated three-dimensional dark matter (DM) halo distributions and assoc…
Dealing with the data imbalance problem on pulsar candidates sifting based on feature selection
Haitao Lin, Xiangru Li
Pulsar detection has become an active research topic in radio astronomy recently. One of the essential procedures for pulsar detection is pulsar candidate sifting (PCS), a procedur…
Parameter Estimation of LAMOST Medium-resolution Stellar Spectra
Xiangru Li, Xiaoyu Zhang, Shengchun Xiong +2
This paper investigates the problem of estimating three stellar atmospheric physical parameters and thirteen elemental abundances for medium-resolution spectra from Large Sky Area…
Estimating Stellar Parameters from LAMOST Low-resolution Spectra
Xiangru Li, Boyu Lin
The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has acquired tens of millions of low-resolution spectra of stars. This paper investigated the parameter estim…
Galaxy Image Classification using Hierarchical Data Learning with Weighted Sampling and Label Smoothing
Xiaohua Ma, Xiangru Li, Ali Luo +2
With the development of a series of Galaxy sky surveys in recent years, the observations increased rapidly, which makes the research of machine learning methods for galaxy image re…
Estimating Atmospheric Parameters from LAMOST Low-Resolution Spectra with Low SNR
Xiangru Li, Si Zeng, Zhu Wang +3
Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) acquired tens of millions of low-resolution stellar spectra. The large amount of the spectra result in the urgenc…
A Model RRNet for Spectral Information Exploitation and LAMOST Medium-resolution Spectrum Parameter Estimation
Shengchun Xiong, Xiangru Li, Caixiu Liao
This work proposes a Residual Recurrent Neural Network (RRNet) for synthetically extracting spectral information, and estimating stellar atmospheric parameters together with 15 che…
Estimation of stellar atmospheric parameters from LAMOST DR8 low-resolution spectra with 20SNR30
Xiangru Li, Zhu Wang, Si Zeng +4
The accuracy of the estimated stellar atmospheric parameter decreases evidently with the decreasing of spectral signal-to-noise ratio (SNR) and there are a huge amount of this kind…
Radio Frequency Interference Mitigation based on the ArPLS and SumThreshold Method
Qingguo Zeng, Xue Chen, Xiangru Li +4
As radio telescopes become sensitive, radio frequency interference (RFI) is more and more serious for interesting signals of radio astronomy. There exist demands for developing an…
Pulsar Candidate Sifting Using Multi-input Convolution Neural Networks
Haitao Lin, Xiangru Li, Qingguo Zeng
Pulsar candidate sifting is an essential process for discovering new pulsars. It aims to search for the most promising pulsar candidates from an all-sky survey, such as High Time R…
Concat Convolutional Neural Network for Pulsar Candidate Selection
Qingguo Zeng, Xiangru Li, Haitao Lin
Pulsar searching is essential for the scientific research in the field of physics and astrophysics. As the development of the radio telescope, the exploding volume and it growth sp…
Pulsars Detection by Machine Learning with Very Few Features
Haitao Lin, Xiangru Li, Ziying Luo
It is an active topic to investigate the schemes based on machine learning (ML) methods for detecting pulsars as the data volume growing exponentially in modern surveys. To improve…
SPCANet: Stellar Parameters and Chemical Abundances Network for LAMOST-II Medium Resolution Survey
Rui Wang, A-Li Luo, Jian-Jun Chen +6
The fundamental stellar atmospheric parameters T_eff and log g and 13 chemical abundances are derived for medium-resolution spectroscopy from LAMOST Medium-Resolution Survey (MRS)…
Binary Neutron Stars Gravitational Wave Detection Based on Wavelet Packet Analysis And Convolutional Neural Networks
Baijiong Lin, Xiangru Li, Woliang Yu
This work investigates the detection of binary neutron stars gravitational wave based on convolutional neural network (CNN). To promote the detection performance and efficiency, we…
Stellar Spectra Classification and Feature evaluation Based on Random Forest
Xiangru Li, Yangtao Lin, Kaibin Qiu
With the availability of multi-object spectrometers and the designing \& running of some large scale sky surveys, we are obtaining massive spectra. Therefore, it becomes more and m…
Spectral feature extraction for DB white dwarfs through machine learning applied to new discoveries in the SDSS DR12 and DR14
Xiao Kong, A-Li Luo, Xiang-Ru Li +3
Using a machine learning (ML) method, we mine DB white dwarfs (DBWDs) from the Sloan Digital Sky Survey (SDSS) Data Release (DR) 12 and DR14. The ML method consists of two parts: f…
Some Optimizations on Detecting Gravitational Wave Using Convolutional Neural Network
Xiangru Li, Woliang Yu, Xilong Fan +1
This work investigates the problem of detecting gravitational wave (GW) events based on simulated damped sinusoid signals contaminated with white Gaussian noise. It is treated as a…
Parameterizing Stellar Spectra Using Deep Neural Networks
Xiangru Li, Ruyang Pan
This work investigates the spectrum parameterization problem using deep neural networks (DNNs). The proposed scheme consists of the following procedures: first, the configuration o…
Estimating stellar atmospheric parameters based on LASSO and support-vector regression
Yu Lu, Xiangru Li
A scheme for estimating atmospheric parameters T, log, and [Fe/H] is proposed on the basis of Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and Haar…
An autoencoder of stellar spectra and its application in automatically estimating atmospheric parameters
Tan Yang, Xiangru Li
This article investigates the problem of estimating stellar atmospheric parameters from spectra. Feature extraction is a key procedure in estimating stellar parameters automaticall…