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名稱Title
2021天文年會
國立嘉義大學蘭潭校區
5月21日至23日

論文摘要

Identifying AGN host galaxies by Machine Learning

[ Oral ]

Yu-Yen Chang (NCHU/ASIAA), Bau-Ching Hsieh (ASIAA), Wei-Hao Wang (ASIAA), Yen-Ting Lin (ASIAA), Chen-Fatt Lim (NTU/ASIAA), Yoshiki Toba (Kyoto U/ASIAA/Ehime U), Yuxing Zhong (Waseda U)

We use machine learning techniques to investigate their performance in classifying active galactic nuclei (AGNs), including X-ray selected AGNs (XAGNs), infrared selected AGNs (IRAGNs), and radio selected AGNs (RAGNs). Using known physical parameters in the Cosmic Evolution Survey (COSMOS) field, we are able to establish training samples in the region of Hyper Suprime-Cam (HSC) survey. We compare several Python packages (e.g., scikit-learn, Keras, and XGBoost), and use XGBoost to identify AGNs and show the performance (e.g., accuracy, precision, recall, F1 score, and AUROC). Our results indicate that the performance is high for bright XAGN and IRAGN host galaxies. The combination of the HSC (optical) information with the Wide-field Infrared Survey Explorer (WISE) band-1 and WISE band-2 (near-infrared) information perform well to identify AGN hosts. For both type-1 (broad-line) XAGNs and type-1 (unobscured) IRAGNs, the performance is very good by using optical to infrared information. These results can apply to the five-band data from the wide regions of the HSC survey, and future all-sky surveys.