Identifying Compton-thick AGNs with Machine learning algorithm in Chandra Deep Field-South
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918036449001472 |
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| author | Zhang, Rui Guo, Xiaotong Gu, Qiusheng Fang, Guanwen Xu, Jun Feng, Hai-Cheng Chen, Yongyun Li, Rui Ding, Nan Wang, Hongtao |
| author_facet | Zhang, Rui Guo, Xiaotong Gu, Qiusheng Fang, Guanwen Xu, Jun Feng, Hai-Cheng Chen, Yongyun Li, Rui Ding, Nan Wang, Hongtao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21105 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identifying Compton-thick AGNs with Machine learning algorithm in Chandra Deep Field-South Zhang, Rui Guo, Xiaotong Gu, Qiusheng Fang, Guanwen Xu, Jun Feng, Hai-Cheng Chen, Yongyun Li, Rui Ding, Nan Wang, Hongtao Astrophysics of Galaxies 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. |
| title | Identifying Compton-thick AGNs with Machine learning algorithm in Chandra Deep Field-South |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2505.21105 |