Zobrazeno 1 - 10
of 2 696
pro vyhledávání: '"Li, Xiao Dong"'
Autor:
Zhao, Cheng, Huang, Song, He, Mengfan, Montero-Camacho, Paulo, Liu, Yu, Renard, Pablo, Tang, Yunyi, Verdier, Aurelien, Xu, Wenshuo, Yang, Xiaorui, Yu, Jiaxi, Zhang, Yao, Zhao, Siyi, Zhou, Xingchen, He, Shengyu, Kneib, Jean-Paul, Li, Jiayi, Li, Zhuoyang, Wang, Wen-Ting, Xianyu, Zhong-Zhi, Zhang, Yidian, Gsponer, Rafaela, Li, Xiao-Dong, Rocher, Antoine, Zou, Siwei, Tan, Ting, Huang, Zhiqi, Wang, Zhuoxiao, Li, Pei, Rombach, Maxime, Dong, Chenxing, Forero-Sanchez, Daniel, Shan, Huanyuan, Wang, Tao, Li, Yin, Zhai, Zhongxu, Wang, Yuting, Zhao, Gong-Bo, Shi, Yong, Mao, Shude, Huang, Lei, Guo, Liquan, Cai, Zheng
The MUltiplexed Survey Telescope (MUST) is a 6.5-meter telescope under development. Dedicated to highly-multiplexed, wide-field spectroscopic surveys, MUST observes over 20,000 targets simultaneously using 6.2-mm pitch positioning robots within a ~5
Externí odkaz:
http://arxiv.org/abs/2411.07970
Foreground removal presents a significant obstacle in both current and forthcoming intensity mapping surveys. While numerous techniques have been developed that show promise in simulated datasets, their efficacy often diminishes when applied to real-
Externí odkaz:
http://arxiv.org/abs/2408.06682
Autor:
Min, Zhiwei, Xiao, Xu, Ding, Jiacheng, Xiao, Liang, Jiang, Jie, Wu, Donglin, Lin, Qiufan, Wang, Yang, Liu, Shuai, Chen, Zhixin, Li, Xiangru, Zhang, Jinqu, Zhang, Le, Li, Xiao-Dong
We propose a lightweight deep convolutional neural network (lCNN) to estimate cosmological parameters from simulated three-dimensional dark matter (DM) halo distributions and associated statistics. The training dataset comprises 2000 realizations of
Externí odkaz:
http://arxiv.org/abs/2404.09483
This paper introduces ASTRA (Algorithm for Stochastic Topological RAnking), a novel method designed for the classification of galaxies into principal cosmic web structures -- voids, sheets, filaments, and knots -- especially tailored for large spectr
Externí odkaz:
http://arxiv.org/abs/2404.01124
Autor:
Yin, Fenfen, Ding, Jiacheng, Lai, Limin, Zhang, Wei, Xiao, Liang, Wang, Zihan, Forero-Romero, Jaime, Zhang, Le, Li, Xiao-Dong
Publikováno v:
Physical Review D,109,123537(2024)
The $\beta$-skeleton approach can be conveniently utilized to construct the cosmic web based on the spatial geometry distribution of galaxies, particularly in sparse samples. This method plays a key role in establishing the three-dimensional structur
Externí odkaz:
http://arxiv.org/abs/2403.14165
Publikováno v:
MNRAS, 531, 3991-4005 (2024)
The spectroscopic survey of China's Space Survey Telescope (CSST) is expected to obtain a huge number of slitless spectra, including more than one hundred million galaxy spectra and millions of active galactic nuclei (AGN) spectra. By making use of t
Externí odkaz:
http://arxiv.org/abs/2311.16903
We investigate the feasibility of using COmoving Lagrangian Acceleration (COLA) technique to efficiently generate galaxy mock catalogues that can accurately reproduce the statistical properties of observed galaxies. Our proposed scheme combines the s
Externí odkaz:
http://arxiv.org/abs/2311.00981
Environmental and instrumental conditions can cause anomalies in astronomical images, which can potentially bias all kinds of measurements if not excluded. Detection of the anomalous images is usually done by human eyes, which is slow and sometimes n
Externí odkaz:
http://arxiv.org/abs/2310.15481
Publikováno v:
Phys. Rev. D 108, 103027 (2023)
The accuracy of Bayesian inference can be negatively affected by the use of inaccurate forward models. In the case of gravitational-wave inference, accurate but computationally expensive waveform models are sometimes substituted with faster but appro
Externí odkaz:
http://arxiv.org/abs/2307.07233
Autor:
Wu, Ziyong, Xiao, Liang, Xiao, Xu, Wang, Jie, Kang, Xi, Wang, Yang, Wang, Xin, Zhang, Le, Li, Xiao-Dong
The peculiar velocities of dark matter halos are crucial to study many issues in cosmology and galaxy evolution. In this study, by using the state-of-the-art deep learning technique, a UNet-based neural network, we propose to reconstruct the peculiar
Externí odkaz:
http://arxiv.org/abs/2301.04586