# Neural network-based anomaly detection for high-resolution X-ray spectroscopy

## TL;DR

This paper introduces a neural network-based anomaly detection method using variational autoencoders for high-resolution X-ray spectroscopy, enabling efficient analysis of large datasets from future observatories.

## Contribution

The study develops a novel anomaly detection approach tailored for high-resolution X-ray spectral data using variational autoencoders trained only on normal samples.

## Key findings

- Successfully demonstrated with simulated datasets of plasma spectra.
- Effective in identifying anomalies in high-resolution X-ray spectral data.
- Accounts for Poisson statistics in neural network implementation.

## Abstract

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.