Neural network-based anomaly detection...
By Y. Ichinohe and S. Yamada
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 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 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.
Keywords: methods: data analysis – techniques: spectroscopic – X-rays: general.