Autoencoder-driven clustering of intersecting D-brane models via tadpole charge

We study the well-known type IIA intersecting D-brane models on the T/ℤ×ℤ orientifold via a machine-learning approach. We apply several autoencoder models with and without positional encoding to D6-brane configurations satisfying certain concrete models described in ref. [1] and attempt to extract some features which the configurations possess. We observe that the configurations cluster in two-dimensional latent layers of the autoencoder models and analyze which physical quantities are relevant to the clustering. As a result, it is found that tadpole charges of hidden D6-branes characterize the clustering. We expect that there is another important factor because a checkerboard pattern in two-dimensional latent layers is observed in the clustering.