Journal
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 475, Issue 4, Pages 4739-4744Publisher
OXFORD UNIV PRESS
DOI: 10.1093/mnras/sty161
Keywords
methods: data analysis; techniques: spectroscopic; galaxies: clusters: individual: Perseus; X-rays: galaxies: clusters
Categories
Funding
- JSPS [15H00785, 15H05438, 16H03954, 26220703]
- Astro-AI working group in RIKEN iTHEMS
- [16J02333]
- Grants-in-Aid for Scientific Research [15H00785, 15H05438, 16J02333, 16H03954, 26220703] Funding Source: KAKEN
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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, normalization 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 fewper cent. The performance dependence on the network structure has been studied. We applied the neural network to an actual high-resolution spectrum obtained with Hitomi. The predicted plasma parameters agree with the known best-fitting parameters of the Perseus cluster within uncertainties of less than or similar to 10 per cent. The result shows that neural networks trained by simulated data might possibly be used to extract a feature built in the data. This would reduce human-intensive preprocessing costs before detailed spectral analysis, and would help us make the best use of the large quantities of spectral data that will be available in the coming decades.
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