A Deep Generative Model that Uses Physical Quantities to Generate and Retrieve Solar Magnetic Active Regions

Deep generative models have shown immense potential in generating unseen data that has the properties of real data. These models learn complex data-generating distributions starting from a smaller set of latent dimensions. However, generative models have encountered great skepticism in scientific do...

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Bibliografische Detailangaben

Hauptverfasser:	Subhamoy Chatterjee, Andrés Muñoz-Jaramillo, Anna Malanushenko
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IOP Publishing 2026-01-01
Schriftenreihe:	The Astrophysical Journal Supplement Series
Schlagworte:	Convolutional neural networks Support vector machine Solar active regions Solar magnetic fields
Online-Zugang:	https://doi.org/10.3847/1538-4365/ae47d9
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