Identifying Compton-thick Active Galactic Nuclei with a Machine Learning Algorithm in Chandra Deep Field-South - Rui Zhang; Xiaotong Guo; Qiusheng Gu; Guanwen Fang; Jun Xu; Hai-Cheng Feng; Yongyun Chen; Rui Li; Nan Ding; Hongtao Wang (The Astrophysical Journal, 2025)
Compton-thick active galactic nuclei (CT-AGNs), which are defined by column density N _H ≥ 1.5 × 10 ^24 cm ^−2 , emit feeble X-ray radiation, even undetectable by X-ray instruments. Despite this, the X-ray emissions from CT-AGNs are believed to be a substantial contributor to the cosmic X-ray background (CXB). According to synthesis models of AGNs, CT-AGNs are expected to make up a significant fraction of the AGN population, likely around 30% or more. However, only ∼11% of AGNs have been identified as CT-AGNs in the Chandra Deep Field-South (CDFS). To identify hitherto unknown CT-AGNs in the field, we used a random forest algorithm for identifying them. First, we build a secure classified subset of 210 AGNs to train and evaluate our algorithm. Our algorithm achieved an accuracy rate of 90% on the test set after training. Then, we applied our algorithm to an additional subset of 254 AGNs, successfully identifying 67 CT-AGNs within this group. This result significantly increased the fraction of CT-AGNs in the CDFS, which is closer to the theoretical predictions of the CXB. Finally, we compared the properties of host galaxies between CT-AGNs and non-CT-AGNs and found that the host galaxies of CT-AGNs exhibit higher levels of star formation activity.