AGN X-ray Spectroscopy with Neural Networks

Abstract

We explore the possibility of using machine learning to estimate physical parameters directly from 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 WFI spectra of AGN with warm absorbers, and train simple neural networks to estimate the ionisation 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.

8 pages, 4 figures, accepted for publication in MNRAS

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Cited by in corpus (6)

• Physics Beyond the Standard Model with Future X-ray Observatories: Projected Constraints on Very-Light Axion-Like Particles with $Athena$ and $AXIS$
• Relativistic Reflection Modeling in AGN and Related Variability from PCA: A Brief Review
• Comparing Reflection and Absorption Models for the Soft X-ray Variability in the NLS1 AGN UGC 11763
• Rapid Spectral Parameter Prediction for Black Hole X-Ray Binaries using Physicalised Autoencoders
• Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves
• X-ray spectral fitting with Monte Carlo Dropout Neural Networks