Contribution of Radio Halos to the Foreground for SKA EoR Experiments

4 code implementations • 14 May 2019 • Weitian Li, Haiguang Xu, Zhixian Ma, Dan Hu, Zhenghao Zhu, Chenxi Shan, Jingying Wang, Junhua Gu, Dongchao Zheng, Xiaoli Lian, Qian Zheng, Yu Wang, Jie Zhu, Xiang-Ping Wu

The overwhelming foreground contamination is one of the primary impediments to probing the EoR through measuring the redshifted 21 cm signal.

Cosmology and Nongalactic Astrophysics

Separating the EoR Signal with a Convolutional Denoising Autoencoder: A Deep-learning-based Method

1 code implementation • 25 Feb 2019 • Weitian Li, Haiguang Xu, Zhixian Ma, Ruimin Zhu, Dan Hu, Zhenghao Zhu, Junhua Gu, Chenxi Shan, Jie Zhu, Xiang-Ping Wu

When applying the foreground removal methods to uncover the faint cosmological signal from the epoch of reionization (EoR), the foreground spectra are assumed to be smooth.

Denoising

Radio Galaxy Morphology Generation Using DNN Autoencoder and Gaussian Mixture Models

1 code implementation • 1 Jun 2018 • Zhixian Ma, Jie Zhu, Weitian Li, Haiguang Xu

In this work, we propose a morphology generation framework for two typical radio galaxies namely Fanaroff-Riley type-I (FRI) and type-II (FRII) with deep neural network based autoencoder (DNNAE) and Gaussian mixture models (GMMs).

X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM

no code implementations • 7 Mar 2017 • Zhixian Ma, Weitian Li, Lei Wang, Haiguang Xu, Jie Zhu

An approach to recognize the point sources (PS) in the X-ray astronomical images using our newly designed granular binary-tree support vector machine (GBT-SVM) classifier is proposed.