  Black hole X-ray binaries (BHBs) offer insights into extreme gravitational
environments and the testing of general relativity. The X-ray spectrum
collected by NICER offers valuable information on the properties and behaviour
of BHBs through spectral fitting. However, traditional spectral fitting methods
are slow and scale poorly with model complexity. This paper presents a new
semi-supervised autoencoder neural network for parameter prediction and
spectral reconstruction of BHBs, showing an improvement of up to a factor of
2,700 in speed while maintaining comparable accuracy. The approach maps the
spectral features from the numerous outbursts catalogued by NICER and
generalises them to new systems for efficient and accurate spectral fitting.
The effectiveness of this approach is demonstrated in the spectral fitting of
BHBs and holds promise for use in other areas of astronomy and physics for
categorising large datasets.
