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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.