Solar Energetic Particle Event occurrence prediction using Solar Flare Soft X-ray measurements and Machine Learning

Sigiava Aminalragia-Giamini; Savvas Raptis; Anastasios Anastasiadis; Antonis Tsigkanos; Ingmar Sandberg; Athanasios Papaioannou; Constantinos Papadimitriou; Piers Jiggens; Angels Aran; Ioannis A. Daglis

doi:10.1051/swsc/2021043

All issues

Volume 11 (2021)

J. Space Weather Space Clim., 11 (2021) 59

References

Open Access

Research Article

Issue		J. Space Weather Space Clim. Volume 11, 2021


Article Number		59
Number of page(s)		15
DOI		https://doi.org/10.1051/swsc/2021043
Published online		24 December 2021

Abadi M, Barham P, Chen J, Chen Z, Davis A, et al. 2016. Tensorflow: A system for large-scale machine learning. In: Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation (OSDI’16), USENIX Association, USA, pp. 265–283. [Google Scholar]
Alberti T, Laurenza M, Cliver EW, Storini M, Consolini G, Lepreti F. 2017. Solar Activity from 2006 to 2014 and Short-term Forecasts of Solar Proton Events Using the ESPERTA Model. ApJ 838: 59. https://doi.org/10.3847/1538-4357/aa5cb8. [Google Scholar]
Aminalragia-Giamini S, Jiggens P, Anastasiadis A, Sandberg I, Aran A, et al. 2020. Prediction of Solar Proton Event Fluence spectra from their Peak flux spectra. J Space Weather Space Clim 10: 1. https://doi.org/10.1051/swsc/2019043. [Google Scholar]
Anastasiadis A, Papaioannou A, Sandberg I, Georgoulis M, Tziotziou K, Kouloumvakos A, Jiggens P. 2017. Predicting Flares and Solar Energetic Particle Events: The FORSPEF Tool. Sol Phys 292: 134. https://doi.org/10.1007/s11207-017-1163-7. [Google Scholar]
Anastasiadis A, Lario D, Papaioannou A, Kouloumvakos A, Vourlidas A. 2019. Solar energetic particles in the inner heliosphere: status and open questions. Phil Trans R Soc A 377: 20180100. https://doi.org/10.1098/rsta.2018.0100. [Google Scholar]
Armstrong TP. 1976. Handbook and reference manual for charged particle measurement experiment data from explorer 47 and 50. Johns Hopkins Univ. Press, Laurel, MD. [Google Scholar]
Aschwanden MJ, Crosby NB, Dimitropoulou M, Georgoulis MK, Hergarten S, et al. 2016. 25 years of self-organized criticality: solar and astrophysics. Space Sci Rev 198: 47–166. https://doi.org/10.1007/s11214-014-0054-6. [Google Scholar]
Azari AR, Lockhart JW, Liemohn MW, Jia X. 2020. Incorporating physical knowledge into machine learning for Planetary Space Physics. Front Astron Space Sci 7: 36. https://doi.org/10.3389/fspas.2020.00036. [Google Scholar]
Bain HM, Steenburgh RA, Onsager TG, Stitely EM. 2021. A summary of National Oceanic and Atmospheric Administration Space Weather Prediction Center proton event forecast performance and skill. Space Weather 19: e2020SW002670. https://doi.org/10.1029/2020SW002670. [Google Scholar]
Balasis G, Aminalragia-Giamini S, Papadimitriou C, Daglis IA, Anastasiadis A, Haagmans R. 2019. A machine learning approach for automated ULF wave recognition. J Space Weather Space Clim 9: A13. https://doi.org/10.1051/swsc/2019010. [Google Scholar]
Balch CC. 2008. Updated verification of the Space Weather Prediction Center’s solar energetic particle prediction model. Space Weather 6: S01001. https://doi.org/10.1029/2007SW000337. [Google Scholar]
Belov A, Garcia H, Kurt V, Mavromichalaki E. 2005. Proton events and X-ray Flares in the last three solar cycles. Cosmic Res 43: 165–178. https://doi.org/10.1007/s10604-005-0031-7. [Google Scholar]
Bloomfield DS, Higgins PA, McAteer RTJ, Gallagher PT. 2012. Toward reliable benchmarking of solar flare forecasting methods. ApJ 747: 2. https://doi.org/10.1088/2041-8205/747/2/L41. [Google Scholar]
Brownlee J. 2020. Imbalanced classification with python: better metrics, balance skewed classes, cost-sensitive learning, machine learning mastery. Available online at: https://books.google.be/books?id=jaXJDwAAQBAJ. [Google Scholar]
Bobra MG, Couvidat S. 2015. Solar Flare Prediction using SDO/HMI vector magnetic field data with Machine-Learning algorithm. ApJ 798: 2. https://doi.org/10.1088/0004-637X/798/2/135. [Google Scholar]
Camporeale E. 2019. The challenge of machine learning in Space Weather: Nowcasting and forecasting. Space Weather 17: 1166–1207. https://doi.org/10.1029/2018SW002061. [Google Scholar]
Cane HV, Richardson IG, von Rosenvinge TT. 2010. A study of solar energetic particle events of 1997–2006: Their composition and associations. J Geophys Res 115: A08101. https://doi.org/10.1029/2009JA014848. [Google Scholar]
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. 2002. SMOTE: Synthetic minority over-sampling technique. J Artif Intell Res 16: 321–357. doi: 10.1613/jair.953. [Google Scholar]
Chollet F. 2018. Keras: The python deep learning library. Astrophysics Source Code Library: ascl-1806. [Google Scholar]
Cliver EW, D’Huys E. 2018. Size distributions of solar proton events and their associated soft X-Ray flares: application of the maximum likelihood estimator. ApJ 864: 48. https://doi.org/10.3847/1538-4357/aad043. [Google Scholar]
Crosby N, Heynderickx D, Jiggens P, Aran A, Sanahuja B, et al. 2015. SEPEM: a tool for statistical modeling the solar energetic particle environment. Space Weather 13: 406–426. https://doi.org/10.1002/2013SW001008. [Google Scholar]
Dierckxsens M, Tziotziou K, Dalla S, Patsou I, Marsh MS, Crosby NB, Malandraki O, Tsiropoula G. 2015. Relationship between solar energetic particles and properties of flares and CMEs: Statistical analysis of solar cycle 23 events. Sol. Phys. 290: 841–874. https://doi.org/10.1007/s11207-014-0641-4. [Google Scholar]
Garcia HA. 1994. Temperature and emission measure from goes soft X-ray measurements. Sol Phys 154: 275–308. https://doi.org/10.1007/BF00681100. [Google Scholar]
GOES I-M DataBook. 1996. Prepared for NASA DRL 101–08, GSFC Specification S-480-21A, Contract NAS5-29500, Reference S-415-19, Revision 1. Greenbelt, MD. [Google Scholar]
Gold RE, Krimigis SM, Hawkins SE III, Haggerty DK, Lohr DA, Fiore E, Armstrong TP, Holland G, Lanzerotti LJ. 1998. Electron, proton, and alpha monitor on the advanced composition explorer spacecraft. Space Sci Rev 86: 541–562. https://doi.org/10.1007/978-94-011-4762-0_19. [Google Scholar]
Goodfellow I, Bengio Y, Courville A. 2016. Deep learning, Vol 1, No. 2, MIT Press, Cambridge. ISBN: 978-0262035613 [Google Scholar]
Gopalswamy N, Tsurutani B, Yan Y. 2015. Short-term variability of the Sun-Earth system: an overview of progress made during the CAWSES-II period. Prog Earth Planet Sci 2: 13. https://doi.org/10.1186/s40645-015-0043-8. [Google Scholar]
Gopalswamy N, Yashiro S, Thakur N, Mäkelä P, Xie H, Akiyama S. 2016. The 2012 July 23 backsed eruption: An extreme energetic particle event? ApJ 833: 2. https://doi.org/10.3847/1538-4357/833/2/216. [Google Scholar]
Grayson JA, Krucker S, Lin RP. 2009. A Statistical study of spectral hardening in solar flares and related solar energetic particle events. ApJ 707: 1588. doi: 10.1088/0004-637X/707/2/1588. [Google Scholar]
Hu S, Kim M-HY, McClellan GE, Cucinotta FA. 2009. Modeling the acute health effects of astronauts from exposure to large solar particle events. Health Phys 96(4): 465–476. https://doi.org/10.1097/01.HP.0000339020.92837.61. [Google Scholar]
Ioffe S, Szegedy C. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, Vol. 37, pp. 448–456. [Google Scholar]
Jiggens PTA, Gabriel SB. 2009. Time distributions of solar energetic particle events: Are SEPEs really random? J Geophys Res 114: A10105. https://doi.org/10.1029/2009JA014291. [Google Scholar]
Jiggens P, Heynderickx D, Sandberg I, Truscott P, Raukunen O, Vainio R. 2018. Updated model of the solar energetic proton environment in space. J Space Weather Space Clim 8: A31. https://doi.org/10.1051/swsc/2018010. [Google Scholar]
Kahler SW, Cliver EW, Ling AG. 2007. Validating the proton prediction system (PPS). J Atmos Sol-Terr Phys 69: 1–2. https://doi.org/10.1016/j.jastp.2006.06.009. [Google Scholar]
Kahler SW, Ling AG. 2018. Forecasting Solar Energetic Particle (SEP) events with Flare X-ray peak ratios. J Space Weather Space Clim 8: A47. https://doi.org/10.1051/swsc/2018033. [Google Scholar]
Klein KL, Dalla S. 2017. Acceleration and propagation of solar energetic particles. Space Sci Rev 212: 1107–1136. https://doi.org/10.1007/s11214-017-0382-4. [Google Scholar]
Lario D, Decker RB, Armstrong TP. 2001. Major solar proton events observed by IMP-8 (from November 1973 to May 2001). ICRC Proc 3254: 1–4. [Google Scholar]
Lario D, Aran A, Gómez-Herrero R, Dresing N, Heber B, Ho GC, Decker RB, Roelof EC. 2013. Longitudinal and radial dependence of solar energetic particle peak intensities: STEREO, ACE, SOHO, GOES, and MESSENGER observations. ApJ 767: 41. https://doi.org/10.1088/0004-637X/767/1/41. [Google Scholar]
Laurenza M, Cliver EW, Hewitt J, Storini M, Ling AG, Balch CC, Kaiser ML. 2009. A technique for short-term warning of solar energetic particle events based on flare location, flare size, and evidence of particle escape. Space Weather 7: S04008. https://doi.org/10.1029/2007SW000379. [Google Scholar]
Laurenza M, Alberti T, Cliver EW. 2018. A Short-term (ESPERTA)-based Forecast Tool for Moderate-to-extreme Solar Proton Events. ApJ 857: 107. https://doi.org/10.3847/1538-4357/aab712. [Google Scholar]
Lemaître G, Nogueira F, Aridas CK. 2017. Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J Mach Learn Res 18: 559–563Available online at: http://jmlr.csail.mit.edu/papers/v18/16-365.html. [Google Scholar]
Maas AL, Hannun AY, Ng AY. 2013. Rectifier nonlinearities improve neural network acoustic models. Proc ICML 30, 1. [Google Scholar]
McComas DJ, Christian ER, Cohen CMS, Cummings AC, Davis AJ, et al. 2019. Probing the energetic particle environment near the Sun. Nature 576: 223–227. https://doi.org/10.1038/s41586-019-1811-1. [Google Scholar]
Nita G, Georgoulis M, Kitiashvili I, Sadykov V, Camporeale E, et al. 2020. Machine learning in heliophysics and space weather forecasting: a white paper of findings and recommendations. arXiv:2006.12224v1 [astro-ph.SR]. [Google Scholar]
Núñez M. 2011. Predicting solar energetic proton events (E > 10 MeV). Space Weather 9: S07003. https://doi.org/10.1029/2010SW000640. [Google Scholar]
Núñez M, Daniel P-P. 2020. Predicting >10 MeV SEP events from solar flare and radio burst data. Universe 6(10): 161. https://doi.org/10.3390/universe6100161. [Google Scholar]
Pacheco D. 2019. Analysis and modelling of the solar energetic particle radiation environment in the inner heliosphere in preparation for solar orbiter, Ph.D. Thesis, Dep. Física Quàtica i Astrofísica, Universitat de Barcelona, Barcelona, Spain http://hdl.handle.net/10803/667033. [Google Scholar]
Papaioannou A, Sandberg I, Anastasiadis A, Kouloumvakos A, Georgoulis MK, Tziotziou K, Tsiropoula G, Jiggens P, Hilgers A. 2016. Solar flares, coronal mass ejections and solar energetic particle event characteristics. J Space Weather Space Clim 6: A42. https://doi.org/10.1051/swsc/2016035. [Google Scholar]
Papaioannou A, Anastasiadis A, Kouloumvakos A, Paassilta M, Vainio R, et al. 2018. Nowcasting solar energetic particle events using principal component analysis. Sol Phys 293: 100. https://doi.org/10.1007/s11207-018-1320-7. [Google Scholar]
Posner A. 2007. Up to 1-hour forecasting of radiation hazards from solar energetic ion events with relativistic electrons. Space Weather 5: S05001. https://doi.org/10.1029/2006SW000268. [Google Scholar]
Raptis S, Aminalragia-Giamini S, Karlsson T, Lindberg M. 2020. Classification of magnetosheath jets using neural networks and High Resolution OMNI (HRO) Data. Front Astron Space Sci 7: 24. https://doi.org/10.3389/fspas.2020.00024. [Google Scholar]
Reames DV. 2020. Four distinct pathways to the element abundances in solar energetic particles. Space Sci Rev 216: 20. https://doi.org/10.1007/s11214-020-0643-5. [Google Scholar]
Richardson I, Rosenvinge T, Cane H, Christian E, Cohen C, Labrador A, Leske R, Mewaldt R, Wiedenbeck M, Stone E. 2014. > 25 MeV proton events observed by the high energy telescopes on the STEREO A and B spacecraft and/or at Earth during the first ~seven years of the STEREO mission. Sol Phys 289: 3059–3107. https://doi.org/10.1007/s11207-014-0524-8. [Google Scholar]
Steyn R, Strauss DT, Effenberger F, Pacheco D. 2020. The soft X-ray Neupert effect as a proxy for solar energetic particle injection - A proof-of-concept physics-based forecasting model. J Space Weather Space Clim 10: 64. https://doi.org/10.1051/swsc/2020067. [Google Scholar]
Winter LM, Ledbetter K. 2015. Type II and Type III Radio Bursts and their correlation with Solar Energetic Proton Events. ApJ 809: 105. https://doi.org/10.1088/0004-637X/809/1/105. [Google Scholar]
Xapsos MA, Stauffer C, Barth JL, Burke EA. 2006. Solar particle events and self-organized criticality: Are deterministic predictions of events possible? IEEE Trans Nuc Sci 53: 4. https://doi.org/10.1109/TNS.2006.880576. [Google Scholar]
Youssef M. 2012. On the relation between the CMEs and the solar flares. NRIAG J Astron Geophys 1(2): 172–178. https://doi.org/10.1016/j.nrjag.2012.12.014. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.