Deep-Learning Identification and Global Characterization of Energetic O2+ Beams at Mars
Abstract
Martian ionospheric ions can be accelerated away from Mars by the solar wind, contributing substantially to atmospheric ion escape. Here, we develop a U-Net-based image segmentation model to identify a distinct population of escaping planetary ions, energetic O2+ beams, from a decade of Mars Atmosphere and Volatile EvolutioN (MAVEN) observations and perform an event-based statistical analysis of their global properties and dependence on external conditions. We find that O2+ beams are widely distributed throughout the Martian plasma environment, with a strong preference for the +E hemisphere, where the solar wind convective electric field points away from Mars. This pronounced spatial asymmetry indicates that the convective electric field plays a major role in beam formation. In the solar wind and magnetosheath, these beams are consistent with the plume. Their occurrence-weighted flux agrees with previous long-term plume estimates, whereas the flux during identified beam intervals is substantially higher. This highlights the advantage of our event-based approach in capturing the intrinsic properties of O2+ plumes. In the induced magnetotail, beam formation remains strongly associated with the convective electric field, while the Hall electric field can provide additional energization. The beam properties are primarily controlled by upstream solar wind conditions, with no apparent influence from solar extreme ultraviolet irradiance. These results might improve our understanding of ion escape at Mars. More broadly, we also demonstrate the potential of deep learning for identifying transient plasma populations from spacecraft observations.
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BibTeX
@misc{zhang2026deeplearning,
title = {{Deep-Learning Identification and Global Characterization of Energetic O2+ Beams at Mars}},
author = {Chi Zhang and Chuanfei Dong and Kathleen G. Hanley and Yaxue Dong and Hans Nilsson and Eduard Dubinin and Jasper Halekas and Janet Luhmann and Shannon Curry and Jiawei Gao and Yilan Qin and Xinmin Li and Hongyang Zhou and Liang Wang},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.30578},
eprint = {2610.30578},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.30578},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Deep-Learning Identification and Global Characterization of Energetic O2+ Beams at Mars
AU - Chi Zhang
AU - Chuanfei Dong
AU - Kathleen G. Hanley
AU - Yaxue Dong
AU - Hans Nilsson
AU - Eduard Dubinin
AU - Jasper Halekas
AU - Janet Luhmann
AU - Shannon Curry
AU - Jiawei Gao
AU - Yilan Qin
AU - Xinmin Li
AU - Hongyang Zhou
AU - Liang Wang
PY - 2026
DA - 2026/10/22
PB - arXiv
DO - 10.48550/arxiv.2610.30578
UR - https://arxiv.org/abs/2610.30578
AB - Martian ionospheric ions can be accelerated away from Mars by the solar wind, contributing substantially to atmospheric ion escape. Here, we develop a U-Net-based image segmentation model to identify a distinct population of escaping planetary ions, energetic O2+ beams, from a decade of Mars Atmosphere and Volatile EvolutioN (MAVEN) observations and perform an event-based statistical analysis of their global properties and dependence on external conditions. We find that O2+ beams are widely distributed throughout the Martian plasma environment, with a strong preference for the +E hemisphere, where the solar wind convective electric field points away from Mars. This pronounced spatial asymmetry indicates that the convective electric field plays a major role in beam formation. In the solar wind and magnetosheath, these beams are consistent with the plume. Their occurrence-weighted flux agrees with previous long-term plume estimates, whereas the flux during identified beam intervals is substantially higher. This highlights the advantage of our event-based approach in capturing the intrinsic properties of O2+ plumes. In the induced magnetotail, beam formation remains strongly associated with the convective electric field, while the Hall electric field can provide additional energization. The beam properties are primarily controlled by upstream solar wind conditions, with no apparent influence from solar extreme ultraviolet irradiance. These results might improve our understanding of ion escape at Mars. More broadly, we also demonstrate the potential of deep learning for identifying transient plasma populations from spacecraft observations.
ER -