EGU22-6371
https://doi.org/10.5194/egusphere-egu22-6371
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

STIX solar flare image reconstruction and classification using machine learning

Hualin Xiao1, Säm Krucker1, Daniel Ryan1, Andrea Battaglia1, Erica Lastufka1, Etesi László1, Ewan Dickson2, and Wen Wang1
Hualin Xiao et al.
  • 1Institute for Data Science, University of Applied Sciences and Arts FHNW, 5210 Windisch, Switzerland (hualin.xiao@fhnw.ch)
  • 2Institute of Physics, University of Graz, 8010 Graz, Austria

The Spectrometer Telescope for Imaging X-rays (STIX) is an instrument onboard Solar Orbiter. It measures X-rays emitted during solar flares in the energy range from 4 to 150 keV and takes X-ray images by using an indirect imaging technique, based on the Moiré effect. STIX instrument
consists of 32 pairs of tungsten grids and 32 pixelated CdTe detector units. Flare Images can be reconstructed on the ground using algorithms such as back-projection, forward-fit, and maximum-entropy after full pixel data are downloaded. Here we report a new image reconstruction and
classification model based on machine learning. Results will be discussed and compared with those from the traditional algorithms.

How to cite: Xiao, H., Krucker, S., Ryan, D., Battaglia, A., Lastufka, E., László, E., Dickson, E., and Wang, W.: STIX solar flare image reconstruction and classification using machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6371, https://doi.org/10.5194/egusphere-egu22-6371, 2022.

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