Identifying Compton-thick AGNs with Machine learning algorithm in Chandra Deep Field-South

TL;DR

This study employs a machine learning approach, specifically a Random Forest classifier, to identify Compton-thick AGNs in the Chandra Deep Field-South, significantly increasing known CT-AGNs and aligning observed fractions with theoretical models.

Contribution

The paper introduces a novel application of machine learning to identify previously undetected Compton-thick AGNs in X-ray survey data, improving detection accuracy and population estimates.

Findings

Achieved 90% accuracy in classifying CT-AGNs.

Identified 67 new CT-AGNs, increasing known population.

Found CT-AGNs host galaxies have higher star formation activity.

Abstract

Compton-thick active galactic nuclei (CT-AGNs), which are defined by column density $\mathrm{N_H} \geqslant 1.5 \times 10^{24} \ \mathrm{cm}^{-2}$, 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 $\sim$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.