Identifying AGN host galaxies by Machine Learning with HSC+WISE

TL;DR

This study employs machine learning techniques, especially XGBoost, to classify various types of active galactic nuclei (AGNs) using HSC and WISE data, achieving high accuracy especially for bright AGN host galaxies.

Contribution

It demonstrates the effectiveness of combining optical and infrared data with machine learning for AGN host galaxy classification, and compares multiple Python packages for this task.

Findings

High classification accuracy for bright XAGN and IRAGN host galaxies.

Optical-infrared data combination improves AGN host identification.

Method applicable to wide-area surveys and future all-sky data.

Abstract

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 well-established 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.