Neural network-based preprocessing to estimate the parameters of the X-ray emission of a single-temperature thermal plasma

Abstract

We present data preprocessing based on an artificial neural network to estimate the parameters of the X-ray emission spectra of a single-temperature thermal plasma. The method finds appropriate parameters close to the global optimum. The neural network is designed to learn the parameters of the thermal plasma (temperature, abundance, normalisation, and redshift) of the input spectra. After training using 9000 simulated X-ray spectra, the network has grown to predict all the unknown parameters with uncertainties of about a few percent. The performance dependence on the network structure has been studied. We applied the neural network to an actual high-resolution spectrum obtained with {\it Hitomi}. The predicted plasma parameters agreed with the known best-fit parameters of the Perseus cluster within $\lesssim 10$\% uncertainties. The result shows a possibility that neural networks trained by simulated data can be useful to extract a feature built in the data, which would reduce human-intensive preprocessing costs before detailed spectral analysis, and help us make the best use of large quantities of spectral data coming in the next decades.

arXiv commentAccepted for publication in MNRAS

References in corpus (13)

• Adam: A Method for Stochastic Optimization
• Efficient Estimation of Word Representations in Vector Space
• The Leiden/Argentine/Bonn (LAB) Survey of Galactic HI: Final data release of the combined LDS and IAR surveys with improved stray-radiation corrections
• Collisional Plasma Models with APEC/APED: Emission Line Diagnostics of Hydrogen-like and Helium-like Ions
• The Quiescent Intracluster Medium in the Core of the Perseus Cluster
• Visualizing and Understanding Convolutional Networks
• The Hot and Energetic Universe: A White Paper presenting the science theme motivating the Athena+ mission
• Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks
• Detection of phase transition via convolutional neural network
• Atmospheric gas dynamics in the Perseus cluster observed with Hitomi
• Hitomi Observation of Radio Galaxy NGC 1275: The First X-ray Microcalorimeter Spectroscopy of Fe-Kα Line Emission from an Active Galactic Nucleus
• Analyzing γ-rays of the Galactic Center with Deep Learning
• Single-epoch supernova classification with deep convolutional neural networks

Cited by in corpus (6)

• Athena X-IFU synthetic observations of galaxy clusters to probe the chemical enrichment of the Universe
• A deep learning view of the census of galaxy clusters in IllustrisTNG
• Neural network-based anomaly detection for high-resolution X-ray spectroscopy
• X-ray Study of Spatial Structures in Tycho's Supernova Remnant Using Unsupervised Deep Learning
• AGN X-ray Spectroscopy with Neural Networks
• X-ray spectral fitting with Monte Carlo Dropout Neural Networks