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We present confusion-limited SCUBA-2 450-$\mu$m observations in the COSMOS-CANDELS region as part of the JCMT Large Program, SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES). Our maps at 450 and 850 $\mu$m cover an area of 450 arcmin$^2$. We achieved instrumental noise levels of $\sigma_{\mathrm{450}}=$ 0.59 mJy beam$^{-1}$ and $\sigma_{\mathrm{850}}=$ 0.09 mJy beam$^{-1}$ in the deepest area of each map. The corresponding confusion noise levels are estimated to be 0.65 and 0.36 mJy beam$^{-1}$. Above the 4 (3.5) $\sigma$ threshold, we detected 360 (479) sources at 450 $\mu$m and 237 (314) sources at 850 $\mu$m. We derive the deepest blank-field number counts at 450 $\mu$m, covering the flux-density range of 2 to 43 mJy. These are in agreement with other SCUBA-2 blank-field and lensing-cluster observations, but are lower than various model counts. We compare the counts with those in other fields and find that the field-to-field variance observed at 450 $\mu$m at the $R=6^\prime$ scale is consistent with Poisson noise, so there is no evidence of strong 2-D clustering at this scale. Additionally, we derive the integrated surface brightness at 450 $\mu$m down to 2.1 mJy to be $57.3^{+1.0}_{-6.2}$~Jy deg$^{-2}$, contributing to (41$\pm$4)\% of the 450-$\mu$m extragalactic background light (EBL) measured by COBE and Planck. Our results suggest that the 450-$\mu$m EBL may be fully resolved at $0.08^{+0.09}_{-0.08}$~mJy, which extremely deep lensing-cluster observations and next-generation submillimeter instruments with large aperture sizes may be able to achieve., Comment: 29 pages, 14 figures, accepted for publication in ApJ

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., Comment: accepted for publication in ApJ