Simulation and Deep Learning Inversion for Full-sky Energetic Neutral Atom Imaging at the Heliospheric Boundary

@misc{wang2026simulation,
  title = {{Simulation and Deep Learning Inversion for Full-sky Energetic Neutral Atom Imaging at the Heliospheric Boundary}},
  author = {Weining Wang and Rong Lin and Jiansen He and Hong Zou and Yongfu Wang and Jiali Chen and Yuguang Ye and Weihong Shi and Wei Zhang and Xi Lu and Die Duan and Tianhang Chen and Linghua Wang and Qiugang Zong},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.17414},
  eprint = {2610.17414},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.17414},
  note = {CC BY 4.0},
}

TY  - UNPB
TI  - Simulation and Deep Learning Inversion for Full-sky Energetic Neutral Atom Imaging at the Heliospheric Boundary
AU  - Weining Wang
AU  - Rong Lin
AU  - Jiansen He
AU  - Hong Zou
AU  - Yongfu Wang
AU  - Jiali Chen
AU  - Yuguang Ye
AU  - Weihong Shi
AU  - Wei Zhang
AU  - Xi Lu
AU  - Die Duan
AU  - Tianhang Chen
AU  - Linghua Wang
AU  - Qiugang Zong
PY  - 2026
DA  - 2026/10/12
PB  - arXiv
DO  - 10.48550/arxiv.2610.17414
UR  - https://arxiv.org/abs/2610.17414
AB  - The distribution of energetic neutral atom (ENA) differential flux density at the heliospheric boundary is the key to revealing the interaction mechanism between the solar wind and the interstellar medium. The Helios ENA Rapid Mapper (HERMA) is a pre-research project for a new ENA detection concept and technical principle, which is designed to provide technical support for refined detection with its advantages of wide energy range coverage and high spatial resolution. To verify the high spatiotemporal resolution rapid imaging capability of the HERMA, this study restored the particle response characteristics of the HERMA (spatial modulation grid + 256-pixel linear array silicon detector) based on Geant4 simulation, and we compared the performance of the traditional inversion algorithm with the convolutional neural network (CNN) inversion algorithm using INCA data as the benchmark. The results show that under different sampling times, the source distribution inverted by the CNN algorithm has a higher degree of agreement with the ideally expected distribution, and the CNN algorithm exhibits better stability and accuracy than the traditional algorithm under the conditions of low signal-to-noise ratio and short staring time. This study provides core technical support for the engineering verification and on-orbit application of the HERMA instrument, fills the technical gap in wide-energy-range and high-resolution ENA detection, and helps to deepen the research on the interaction mechanism between the solar wind and the interstellar medium at the heliospheric boundary.
ER  -