  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.
