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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.
We present a catalog of physical properties for galaxies hosting active galactic nuclei (AGN) detected by the Wide-field Infrared Survey Explorer (WISE). By fitting broadband spectral energy distributions of sources in the WISE AGN Catalog (Assef et
In this paper we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder (CAE), and a clustering algorithm consisting of a Bayesian Gaussian mixture model (BGM). We apply this technique to v
We have measured the clustering of galaxies around active galactic nuclei (AGN) for which single-epoch virial masses of the super-massive black hole (SMBH) are available to investigate the relation between the large scale environment of AGNs and the
The Wide-field Infrared Survey Explorer (WISE) has scanned the entire sky with unprecedented sensitivity in four infrared bands, at 3.4, 4.6, 12, and 22 micron. The WISE Point Source Catalog contains more than 560 million objects, among them hundreds
The advancement of technology has resulted in a rapid increase in supernova (SN) discoveries. The Subaru/Hyper Suprime-Cam (HSC) transient survey, conducted from fall 2016 through spring 2017, yielded 1824 SN candidates. This gave rise to the need fo