SIMBAD references

We propose an anomaly detection technique for high-resolution X-ray spectroscopy. The method is based on the neural network architecture variational auto-encoder, and requires only normal samples for training. We implement the network using PYTHON taking account of the effect of Poisson statistics carefully, and demonstrate the concept with simulated high-resolution X-ray spectral data sets 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.