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Found 3 papers, 3 papers with code
AGNet: Weighing Black Holes with Machine Learning
Supermassive black holes (SMBHs) are ubiquitously found at the centers of most galaxies.
AGNet: Weighing Black Holes with Deep Learning
We find a 1$\sigma$ scatter of 0. 37 dex between the predicted SMBH mass and the fiducial virial mass estimate based on SDSS single-epoch spectra, which is comparable to the systematic uncertainty in the virial mass estimate.
E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification
We propose the use of group convolutional neural network architectures (GCNNs) equivariant to the 2D Euclidean group, $E(2)$, for the task of galaxy morphology classification by utilizing symmetries of the data present in galaxy images as an inductive bias in the architecture.
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