Details

Autor(en) / Beteiligte

• Silver, R.
• Torres-Albà, N.
• Zhao, X.
• Marchesi, S.
• Pizzetti, A.
• Cox, I.
• Ajello, M.

Titel

A machine learning algorithm for reliably predicting active galactic nucleus absorbing column densities

Ist Teil von

• Astronomy and astrophysics (Berlin), 2023-07, Vol.675, p.A65

Erscheinungsjahr

2023

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• We present a new method for predicting the line-of-sight column density ( N H ) values of active galactic nuclei (AGN) based on mid-infrared (MIR), soft X-ray, and hard X-ray data. We developed a multiple linear regression machine learning algorithm trained with WISE colors, Swift -BAT count rates, soft X-ray hardness ratios, and an MIR–soft X-ray flux ratio. Our algorithm was trained off 451 AGN from the Swift -BAT sample with known N H and has the ability to accurately predict N H values for AGN of all levels of obscuration, as evidenced by its Spearman correlation coefficient value of 0.86 and its 75% classification accuracy. This is significant as few other methods can be reliably applied to AGN with Log( N H  < 22.5). It was determined that the two soft X-ray hardness ratios and the MIR–soft X-ray flux ratio were the largest contributors toward accurate N H determinations. We applied the algorithm to 487 AGN from the BAT 150 Month catalog with no previously measured N H values. This algorithm will continue to contribute significantly to finding Compton-thick (CT) AGN ( N H  ≥ 10 24 cm −2 ), thus enabling us to determine the true intrinsic fraction of CT-AGN in the local Universe and their contribution to the cosmic X-ray background.