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The Galaxy Morphology Posterior Estimation Network (GaMPEN) is a Bayesian machine learning framework that can estimate robust posteriors (i.e., values + uncertainties) for structural parameters of galaxies. GaMPEN also automatically crops input images to an optimal size before structural parameter estimation.
GaMPEN’s predicted posteriors are extremely well-calibrated (less than 5% deviation) and have been shown to be up to 60% more accurate compared to the uncertainties predicted by many light-profile fitting algorithms.
Once trained, it takes GaMPEN less than a millisecond to perform a single model evaluation on a CPU. Thus, GaMPEN’s posterior prediction capabilities are ready for large galaxy samples expected from upcoming large imaging surveys, such as Rubin-LSST, Euclid, and NGRST.