A New Mid-Infrared and X-ray Machine Learning Algorithm to Discover Compton-thick AGN

Abstract

We present a new method to predict the line-of-sight column density (N H ) values of active galactic nuclei (AGN) based on midinfrared (MIR), soft, 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 towards accurate N H determination. This algorithm will 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.