  We propose an anomaly detection technique for high-resolution X-ray
spectroscopy. The method is based on the neural network architecture
variational autoencoder, and requires only {\it normal} samples for training.
We implement the network using Python taking account of the effect of Poisson
statistics carefully, and deonstrate the concept with simulated high-resolution
X-ray spectral datasets of one-temperature, two-temperature and non-equilibrium
plasma. Our proposed technique would assist scientists in finding important
information that would otherwise be missed due to the unmanageable amount of
data taken with future X-ray observatories.
