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Benchmarking Dimensionality Reduction Methods for High-dimensional ALMA Image Cubes

  • Authors: Haley N. Scolati, Ryan A. Loomis, Anthony J. Remijan, Kin Long Kelvin Lee

Haley N. Scolati et al 2026 The Astronomical Journal 171 .

  • Provider: AAS Journals

Caption: Figure 10.

Reconstructions, residuals, and residual errors with respect to ground-truth images of select channels from the G34.30 and LkCa 15 datasets using a simple convolutional autoencoder. Reconstructions were screened with a varying number of components from n = 50 to 500 for both datasets. The residual images were generated by taking the difference of the ground-truth image and corresponding reconstruction.

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