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Abstract: Interpretable Machine Learning for ICME Identification: Leveraging Solar Wind Helium Abundance, Compressibility, and Cross Helicity (AGU25)

Interpretable Machine Learning for ICME Identification: Leveraging Solar Wind Helium Abundance, Compressibility, and Cross Helicity

Benjamin L. Alterman1, Andreas Weiss1, Fernando Carcaboso2, Sanchita Pal1, Dr. Yogesh ., PhD3, Brent M Randol4, Mihailo Martinovic5, Michael Louis Stevens6, Megha Mahendrabhai Pandya7, Bennett Maruca8, Leon Ofman1, Lynn B Wilson III4, Li Chen9, Adam Szabo4, Teresa Nieves-Chinchilla10 and Christian Möstl11, (1)NASA Goddard Space Flight Center, Greenbelt, United States, (2)NASA Goddard Space Flight Center, Heliophysics Science Division, Greenbelt, United States, (3)Indian Institute of Technology Gandhinagar, Gandhinagar, India, (4)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (5)University of Arizona, Lunar and Planetary Laboratory, Tucson, AZ, United States, (6)Smithsonian Astrophysical Observatory, Cambridge, MA, United States, (7)Catholic University of America, Washington, DC, United States, (8)Center for Astrophysics, Berkeley, CA, United States, (9)NASA GSFC, Greenbelt, United States, (10)NASA Goddard Space Flight Center, Heliophysics Science Division, MD, United States, (11)Austrian Academy of Sciences, Vienna, Austria