J/A+A/675/A65 AGN absorbing column densities (Silver+, 2023)
A machine learning algorithm to reliably predict active galactic nuclei
absorbing column densities.
Silver R., Torres-Alba N., Zhao X., Marchesi S., Pizzetti A., Cox I.,
Ajello M.
<Astron. Astrophys. 675, A65 (2023)>
=2023A&A...675A..65S 2023A&A...675A..65S (SIMBAD/NED BibCode)
ADC_Keywords: Galaxies, IR ; X-ray sources ; Abundances
Keywords: infrared: galaxies - galaxies: active - galaxies: nucleus -
X-rays: galaxies - X-rays: diffuse background - methods: data analysis
Abstract:
We present a new method to predict the line-of-sight column density
(NH) values of active galactic nuclei (AGN) based on mid-infrared
(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 NH and has the ability to accurately
predict NH 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(NH)<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 NH
determination. We applied the algorithm to 487 AGN from the BAT
150-month catalog with no previously measured NH values. This
algorithm will continue to contribute significantly to finding
Compton-thick (CT-) AGN (NH≥1024cm-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.
Description:
In this work, we present a new machine learning algorithm that
predicts the line-of-sight column density of AGN, thus enabling us to
discover new CT-AGN candidates. Using MIR data from WISE, soft X-ray
data from Swift-XRT and hard X-ray data from Swift-BAT, our machine
learning algorithm has proven its ability to accurately reproduce the
NH values of our 91-source test sample, correctly classifying 75% of
sources based on their obscuration.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table.dat 212 451 Data of 451 sources used to train and test
the algorithm
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Byte-by-byte Description of file: table.dat
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Bytes Format Units Label Explanations
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1- 24 A24 --- Name Name of the source
26- 31 F6.3 mag W1-W2 WISE W1-W2 colour
33- 37 F5.3 mag W1-W3 WISE W1-W3 colour
39- 43 F5.3 mag W1-W4 WISE W1-W4 colour
45- 49 F5.3 mag W2-W3 WISE W2-W3 colour
51- 55 F5.3 mag W2-W4 WISE W2-W4 colour
57- 61 F5.3 mag W3-W4 WISE W3-W4 colour
63- 69 F7.4 [-] log(FIR/FXR) Mid-IR-soft X-ray flux ratio
71- 79 F9.6 --- HR1 XRT hardness ratio using the bands:
(1-2keV)- (0.3-1keV)/(1-2keV)+(0.3-1keV)
81- 89 F9.6 --- HR2 XRT hardness ratio using the bands:
(2-10keV)-(1-2keV)/(2-10keV)+(1-2 keV)
91-102 E12.6 ct/s CR14-20keV BAT count rate in the band 14-20keV
104-115 E12.6 ct/s CR20-24keV BAT count rate in the band 20-24keV
117-128 E12.6 ct/s CR24-34keV BAT count rate in the band 24-34keV
130-141 E12.6 ct/s CR34-45keV BAT count rate in the band 34-45keV
143-154 E12.6 ct/s CR45-60keV BAT count rate in the band 45-60keV
156-167 E12.6 ct/s CR60-85keV BAT count rate in the band 60-85keV
169-180 E12.6 ct/s CR85-110keV BAT count rate in the band 85-110keV
182-193 E12.6 ct/s CR110-150keV BAT count rate in the band 110-150keV
195-206 E12.6 ct/s CR14-150keV BAT count rate in the band 14-150keV
208-212 F5.2 [cm/s2] logNH-RX NH column density determined through
X-ray spectral fitting
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Acknowledgements:
Ross Silver, rmsilve(at)g.clemson.edu
(End) Ross Silver [Clemson University, USA], Patricia Vannier [CDS] 17-May-2023