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Your search keyword '"Zheng, Xiaogang"' showing total 208 results
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208 results on '"Zheng, Xiaogang"'

1. Constraining on the non-standard cosmological models combining the observations of high-redshift quasars and BAO

Author

Liu, Ziqiang, Liu, Tonghua, Zhong, Xinyi, Xu, Yifei, and Zheng, Xiaogang

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

In this work, we studied four types of cosmological models with different mechanisms driving the accelerated expansion of the universe, include Braneworld models, Chaplygin Gas models, Emergent Dark Energy models, and cosmological torsion models. Considering that the dynamics of these models at low redshifts are very similar and difficult to distinguish, we used the latest and largest UV and X-ray measurements of quasars (QSOs) observations covering the range of redshift $0.009

Published
2024

2. Distinguishing $\Lambda$CDM from evolving dark energy with the future gravitational-wave space-borne detector DECIGO

Author

Liu, Yuting, Cao, Shuo, Zheng, Xiaogang, Biesiada, Marek, Jiang, Jianyong, and Liu, Tonghua

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

The $Omh^2(z_i,z_j)$ two point diagnostics was proposed as a litmus test of $\Lambda$CDM model and measurements of cosmic expansion rate $H(z)$ have been extensively used to perform this test. The results obtained so far suggested a tension between observations and predictions of the $\Lambda$CDM model. However, the dataset of $H(z)$ direct measurements from cosmic chronometers and BAO was quite limited. This motivated us to study the performance of this test on a larger sample obtained in an alternative way. In this Letter, we propose that gravitational wave (GW) standard sirens could provide large samples of $H(z)$ measurements in the redshift range of $0

Published
2024

28. Progress in heavy ion cancer therapy at IMP and future development

Author

Li, Qiang, primary, Liu, Xinguo, additional, Dai, Zhongying, additional, He, Pengbo, additional, Ma, Yuanyuan, additional, Shen, Guosheng, additional, Jin, Xiaodong, additional, Ye, Fei, additional, Zheng, Xiaogang, additional, Zhao, Ting, additional, Zhang, Hui, additional, Li, Zheng, additional, Zou, Bingwen, additional, Pu, Yuehu, additional, and Chen, Weiqiang, additional

Published
2024
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47. Gadolinium Oxide Nanoparticles Reinforce the Fractionated Radiotherapy-Induced Immune Response in Tri-Negative Breast Cancer via cGAS-STING Pathway

Author

Yu, Boyi, primary, Lu, Xuanyi, additional, Feng, Xianglong, additional, Zhao, Ting, additional, Li, Jiaxin, additional, Lu, Yudie, additional, Ye, Fei, additional, Liu, Xiongxiong, additional, Zheng, Xiaogang, additional, Shen, Zheyu, additional, Jin, Xiaodong, additional, Chen, Weiqiang, additional, and Li, Qiang, additional

Published
2023
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50. Automatic Classification of All-Sky Nighttime Cloud Images Based on Machine Learning.

Author

Zhong, Xin, Du, Fujia, Hu, Yi, Hou, Xu, Zhu, Zonghong, Zheng, Xiaogang, Huang, Kang, Ren, Zhimin, and Hou, Yonghui

Subjects
AUTOMATIC classification, IMAGE recognition (Computer vision), MACHINE learning, DENSITY of stars, INSPECTION & review, DAYLIGHT, GEOGRAPHIC names, IMAGE registration
Abstract

Cloud-induced atmospheric extinction and occlusion significantly affect the effectiveness and quality of telescope observations. Real-time cloud-cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. Visual inspection is currently the primary approach for analyzing cloud distribution at ground-based astronomical sites. However, the main disadvantages of manual observation methods are human subjectivity, heavy workloads, and poor real-time performance. Therefore, a real-time automatic cloud image classification method is desperately needed. This paper presents a novel cloud identification method named the PSO+XGBoost model, which combines eXtreme Gradient Boosting (XGBoost) with particle-swarm optimization (PSO). The entire cloud image is divided into 37 sub-regions to identify the distribution of the clouds more precisely. Nineteen features, including the sky background, star density, lighting conditions, and subregion grayscale values, are extracted. The experimental results have shown that the overall classification accuracy is 96.91%, and our model can outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements. [ABSTRACT FROM AUTHOR]

Published
2024
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