Abstract Upcoming imaging missions—NASA's LEXI and ESA/CAS's SMILE—will target solar wind charge exchange X‐ray (SWCX) emission from Earth's magnetosheath. This emission is generated by highly charged ions colliding with neutrals in Earth's exosphere. Accurate SWCX models require data on exospheric neutral densities, as well as solar wind flux and composition. The Advanced Composition Explorer (ACE) Solar Wind Ionic Composition Spectrometer (SWICS) provided the needed solar wind composition data from 1998 until an instrument anomaly in 2011 limited its outputs. To address this, we developed empirical functions using ion ratios () still available from ACE, partially compensating for missing composition data. The results underscore the need for a new mission to measure solar wind composition and support future SWCX analysis efforts.
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Can XMM‐Newton Be Used to Track Compositional Changes in the Solar Wind?
Abstract Geocoronal Solar Wind Charge Exchange (SWCX) is the process by which heavy ions from the solar wind undergo charge exchange with neutral hydrogen atoms from the Earth's exosphere, releasing photons at discrete energies characteristic of the solar wind ions. This paper investigates the solar wind types driving geocoronal SWCX. We find that during periods of time‐variable SWCX, higher fractions of every ion species are recorded by ACE compared to the averages. Notably, a subset of the slow solar wind characterized by a systematic lower temperature and higher proton flux is surprisingly effective for producing SWCX. Given the degradation of the solar wind composition spectrometer on ACE in 2011, we explore the capabilities of XMM‐Newton as an alternative sensor to monitor heavy ion composition in the solar wind. Unlike the distributions of other ion line fluxes analyzed, only OVIII, extracted via spectral analysis of XMM‐Newton observations, display patterns similar to the corresponding parent ion abundances from ACE . Finally, we employ a Random Forest Classifier model to predict solar wind types based on literature results. When combining proton data with XMM‐Newton features, the model performance improves significantly, achieving a macro‐averaged F1 score of 0.80 (with a standard deviation of 0.06).
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- Award ID(s):
- 2229138
- PAR ID:
- 10604033
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Space Physics
- Volume:
- 129
- Issue:
- 12
- ISSN:
- 2169-9380
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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