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AGN X-ray spectroscopy with neural networks

Parker, M L; Lieu, M; Matzeu, G A


M L Parker

G A Matzeu


We explore the possibility of using machine learning to estimate physical parameters directly from active galactic nucleus (AGN) X-ray spectra without needing computationally expensive spectral fitting. Specifically, we consider survey quality data, rather than long pointed observations, to ensure that this approach works in the regime where it is most likely to be applied. We simulate Athena Wide Field Imager spectra of AGN with warm absorbers, and train simple neural networks to estimate the ionization and column density of the absorbers. We find that this approach can give comparable accuracy to spectral fitting, without the risk of outliers caused by the fit sticking in a false minimum, and with an improvement of around three orders of magnitude in speed. We also demonstrate that using principal component analysis to reduce the dimensionality of the data prior to inputting it into the neural net can significantly increase the accuracy of the parameter estimation for negligible computational cost, while also allowing a simpler network architecture to be used.


Parker, M. L., Lieu, M., & Matzeu, G. A. (2022). AGN X-ray spectroscopy with neural networks. Monthly Notices of the Royal Astronomical Society, 514(3), 4061-4068.

Journal Article Type Article
Acceptance Date Jun 9, 2022
Online Publication Date Jun 15, 2022
Publication Date 2022-08
Deposit Date Sep 9, 2022
Publicly Available Date Sep 13, 2022
Journal Monthly Notices of the Royal Astronomical Society
Print ISSN 0035-8711
Electronic ISSN 1365-2966
Peer Reviewed Peer Reviewed
Volume 514
Issue 3
Pages 4061-4068
Keywords Space and Planetary Science; Astronomy and Astrophysics
Public URL
Publisher URL
Additional Information This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©2022 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.


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