Deep Learning the Forecast of Galactic Cosmic-Ray Spectra
We introduce a novel deep learning framework based on long short-term memory networks to predict galactic cosmic-ray spectra on a one-day-ahead basis by leveraging historical solar activity data, overcoming limitations inherent in traditional transport models. By flexibly incorporating multiple sola...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal Letters |
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| Online Access: | https://doi.org/10.3847/2041-8213/ada427 |
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| author | Yi-Lun Du Xiaojian Song Xi Luo |
| author_facet | Yi-Lun Du Xiaojian Song Xi Luo |
| author_sort | Yi-Lun Du |
| collection | DOAJ |
| description | We introduce a novel deep learning framework based on long short-term memory networks to predict galactic cosmic-ray spectra on a one-day-ahead basis by leveraging historical solar activity data, overcoming limitations inherent in traditional transport models. By flexibly incorporating multiple solar parameters, such as the heliospheric magnetic field, solar wind speed, and sunspot numbers, the model achieves accurate short-term and long-term predictions of cosmic-ray flux. The addition of historical cosmic-ray flux data significantly enhances prediction accuracy, allowing the model to capture complex dependencies between past and future flux variations. Additionally, the model reliably predicts full cosmic-ray spectra for different particle species, enhancing its utility for comprehensive space weather forecasting. Our approach offers a scalable, data-driven alternative to traditional physics-based methods, ensuring robust daily and long-term forecasts. This work opens avenues for advanced models that can integrate broader observational data, with significant implications for space weather monitoring and mission planning. |
| format | Article |
| id | doaj-art-c3b6c57f1b2e46de9e65603b35bbcf89 |
| institution | Kabale University |
| issn | 2041-8205 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Letters |
| spelling | doaj-art-c3b6c57f1b2e46de9e65603b35bbcf892025-01-09T13:55:10ZengIOP PublishingThe Astrophysical Journal Letters2041-82052025-01-019782L3610.3847/2041-8213/ada427Deep Learning the Forecast of Galactic Cosmic-Ray SpectraYi-Lun Du0https://orcid.org/0000-0001-7531-5021Xiaojian Song1https://orcid.org/0000-0002-7723-5743Xi Luo2https://orcid.org/0000-0002-4508-6042Shandong Institute of Advanced Technology , Jinan 250100, People's Republic of China ; yilun.du@iat.cnShandong Institute of Advanced Technology , Jinan 250100, People's Republic of China ; yilun.du@iat.cnShandong Institute of Advanced Technology , Jinan 250100, People's Republic of China ; yilun.du@iat.cnWe introduce a novel deep learning framework based on long short-term memory networks to predict galactic cosmic-ray spectra on a one-day-ahead basis by leveraging historical solar activity data, overcoming limitations inherent in traditional transport models. By flexibly incorporating multiple solar parameters, such as the heliospheric magnetic field, solar wind speed, and sunspot numbers, the model achieves accurate short-term and long-term predictions of cosmic-ray flux. The addition of historical cosmic-ray flux data significantly enhances prediction accuracy, allowing the model to capture complex dependencies between past and future flux variations. Additionally, the model reliably predicts full cosmic-ray spectra for different particle species, enhancing its utility for comprehensive space weather forecasting. Our approach offers a scalable, data-driven alternative to traditional physics-based methods, ensuring robust daily and long-term forecasts. This work opens avenues for advanced models that can integrate broader observational data, with significant implications for space weather monitoring and mission planning.https://doi.org/10.3847/2041-8213/ada427Galactic cosmic raysSolar windHeliosphereCosmic rays |
| spellingShingle | Yi-Lun Du Xiaojian Song Xi Luo Deep Learning the Forecast of Galactic Cosmic-Ray Spectra The Astrophysical Journal Letters Galactic cosmic rays Solar wind Heliosphere Cosmic rays |
| title | Deep Learning the Forecast of Galactic Cosmic-Ray Spectra |
| title_full | Deep Learning the Forecast of Galactic Cosmic-Ray Spectra |
| title_fullStr | Deep Learning the Forecast of Galactic Cosmic-Ray Spectra |
| title_full_unstemmed | Deep Learning the Forecast of Galactic Cosmic-Ray Spectra |
| title_short | Deep Learning the Forecast of Galactic Cosmic-Ray Spectra |
| title_sort | deep learning the forecast of galactic cosmic ray spectra |
| topic | Galactic cosmic rays Solar wind Heliosphere Cosmic rays |
| url | https://doi.org/10.3847/2041-8213/ada427 |
| work_keys_str_mv | AT yilundu deeplearningtheforecastofgalacticcosmicrayspectra AT xiaojiansong deeplearningtheforecastofgalacticcosmicrayspectra AT xiluo deeplearningtheforecastofgalacticcosmicrayspectra |