Speaker
Description
Polarized emission from the vicinity of black hole systems carries essential information about local magnetic field configurations in the strong gravity regime. In this work, we employ unsupervised learning to a library of model GRMHD Stokes images of M87*, cluster the images based on the polarized image features, and explore how the clustering depends on the model parameters such as black hole spin, accretion type, and electron energies. To perform the clustering, we apply an autoencoder for dimension reduction of the image library, and group the resulting distributions in the latent space with k-mean clustering. The clustering results depend on whether the polarized properties are included in the channel of the input data, implying how the information of model parameters are embedded in different polarized properties.
| Participate the oral/poster presentation award competition | Yes |
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