Predicting cutoff L-shells of solar protons using the GPPSn particle dataset
Solar energetic protons (SEPs) arriving at the Earth trigger severe radiation storms in the near-Earth space, directly impacting space missions operating at various altitudes. Therefore, monitoring SEP events and predicting the penetration depths of solar protons are critical for aerospace sectors....
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| Format: | Article |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Astronomy and Space Sciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fspas.2025.1630911/full |
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| author | Yue Chen Steven K. Morley Matthew R. Carver Andrew S. Hoover Cordell J. Delzer Katherine E. Gattiker Elizabeth C. Auden |
| author_facet | Yue Chen Steven K. Morley Matthew R. Carver Andrew S. Hoover Cordell J. Delzer Katherine E. Gattiker Elizabeth C. Auden |
| author_sort | Yue Chen |
| collection | DOAJ |
| description | Solar energetic protons (SEPs) arriving at the Earth trigger severe radiation storms in the near-Earth space, directly impacting space missions operating at various altitudes. Therefore, monitoring SEP events and predicting the penetration depths of solar protons are critical for aerospace sectors. Building on previous efforts, here we demonstrate the feasibility of using proton measurements from the Global Prompt Proton Sensor network (GPPSn), enabled by Los Alamos National Laboratory developed combined X-ray dosimeters aboard GPS satellites, to characterize and predict the penetration of solar protons into the geomagnetic field. The inclined medium-Earth-orbits (MEOs) of the global GPS constellation offer a unique advantage of allowing simultaneous measurements of penetrating solar protons inside both open- and closed-field line regions. Therefore, the L-profiles of ∼10s–100 MeV solar protons and their associated cutoff L-shells can be determined from the GPPSn dataset, using predefined threshold proton flux values rather than traditional flux ratios. After examining a list of SEP event intervals across solar cycles 23, 24 and 25—including the 2024 Mother’s Day superstorm, we showcase how the latest GPPSn proton dataset (release v1.10), reprocessed and calibrated, can not only be used to monitor solar proton distributions inside the dynamic geomagnetic field for individual events, but also to derive a new empirical model linking cutoff L-shells with several key space weather parameters. This newly developed SEPCL-MEO model demonstrates high predictive performance; for example, predictions for >30 MeV solar protons yield a correlation coefficient of 0.85 and performance efficiency of 0.67 when validated against GPPSn observations. Results from this pilot study underscores the scientific and operational value of the GPPSn dataset, and this dataset—when paired with machine-learning techniques—can play a critical role in observing and predicting the effects of future incoming SEP events, including extreme ones. |
| format | Article |
| id | doaj-art-329c8794546e4637af9a7d766de18a5a |
| institution | Kabale University |
| issn | 2296-987X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Astronomy and Space Sciences |
| spelling | doaj-art-329c8794546e4637af9a7d766de18a5a2025-08-20T03:46:46ZengFrontiers Media S.A.Frontiers in Astronomy and Space Sciences2296-987X2025-08-011210.3389/fspas.2025.16309111630911Predicting cutoff L-shells of solar protons using the GPPSn particle datasetYue Chen0Steven K. Morley1Matthew R. Carver2Andrew S. Hoover3Cordell J. Delzer4Katherine E. Gattiker5Elizabeth C. Auden6Los Alamos National Laboratory, Los Alamos, NM, United StatesLos Alamos National Laboratory, Los Alamos, NM, United StatesCarver Scientific, Santa Fe, NM, United StatesLos Alamos National Laboratory, Los Alamos, NM, United StatesLos Alamos National Laboratory, Los Alamos, NM, United StatesLos Alamos National Laboratory, Los Alamos, NM, United StatesLos Alamos National Laboratory, Los Alamos, NM, United StatesSolar energetic protons (SEPs) arriving at the Earth trigger severe radiation storms in the near-Earth space, directly impacting space missions operating at various altitudes. Therefore, monitoring SEP events and predicting the penetration depths of solar protons are critical for aerospace sectors. Building on previous efforts, here we demonstrate the feasibility of using proton measurements from the Global Prompt Proton Sensor network (GPPSn), enabled by Los Alamos National Laboratory developed combined X-ray dosimeters aboard GPS satellites, to characterize and predict the penetration of solar protons into the geomagnetic field. The inclined medium-Earth-orbits (MEOs) of the global GPS constellation offer a unique advantage of allowing simultaneous measurements of penetrating solar protons inside both open- and closed-field line regions. Therefore, the L-profiles of ∼10s–100 MeV solar protons and their associated cutoff L-shells can be determined from the GPPSn dataset, using predefined threshold proton flux values rather than traditional flux ratios. After examining a list of SEP event intervals across solar cycles 23, 24 and 25—including the 2024 Mother’s Day superstorm, we showcase how the latest GPPSn proton dataset (release v1.10), reprocessed and calibrated, can not only be used to monitor solar proton distributions inside the dynamic geomagnetic field for individual events, but also to derive a new empirical model linking cutoff L-shells with several key space weather parameters. This newly developed SEPCL-MEO model demonstrates high predictive performance; for example, predictions for >30 MeV solar protons yield a correlation coefficient of 0.85 and performance efficiency of 0.67 when validated against GPPSn observations. Results from this pilot study underscores the scientific and operational value of the GPPSn dataset, and this dataset—when paired with machine-learning techniques—can play a critical role in observing and predicting the effects of future incoming SEP events, including extreme ones.https://www.frontiersin.org/articles/10.3389/fspas.2025.1630911/fullsolar energetic protonspredicting cutoff L-shellsGPPSn datasetGPS CXD particle instrumentSEPCL-MEO model |
| spellingShingle | Yue Chen Steven K. Morley Matthew R. Carver Andrew S. Hoover Cordell J. Delzer Katherine E. Gattiker Elizabeth C. Auden Predicting cutoff L-shells of solar protons using the GPPSn particle dataset Frontiers in Astronomy and Space Sciences solar energetic protons predicting cutoff L-shells GPPSn dataset GPS CXD particle instrument SEPCL-MEO model |
| title | Predicting cutoff L-shells of solar protons using the GPPSn particle dataset |
| title_full | Predicting cutoff L-shells of solar protons using the GPPSn particle dataset |
| title_fullStr | Predicting cutoff L-shells of solar protons using the GPPSn particle dataset |
| title_full_unstemmed | Predicting cutoff L-shells of solar protons using the GPPSn particle dataset |
| title_short | Predicting cutoff L-shells of solar protons using the GPPSn particle dataset |
| title_sort | predicting cutoff l shells of solar protons using the gppsn particle dataset |
| topic | solar energetic protons predicting cutoff L-shells GPPSn dataset GPS CXD particle instrument SEPCL-MEO model |
| url | https://www.frontiersin.org/articles/10.3389/fspas.2025.1630911/full |
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