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[ascl:2211.006] baobab: Training data generator for hierarchically modeling strong lenses with Bayesian neural networks

baobab generates images of strongly-lensed systems, given some configurable prior distributions over the parameters of the lens and light profiles as well as configurable assumptions about the instrument and observation conditions. Wrapped around lenstronomy (ascl:1804.012), baobab supports prior distributions ranging from artificially simple to empirical. A major use case for baobab is the generation of training and test sets for hierarchical inference using Bayesian neural networks (BNNs); the code can generate the training and test sets using different priors.

Code site:
https://github.com/jiwoncpark/baobab
Used in:
https://ui.adsabs.harvard.edu/abs/2021ApJ...909..187W
Described in:
https://ui.adsabs.harvard.edu/abs/2021ApJ...910...39P
Bibcode:
2022ascl.soft11006P

Views: 970

ascl:2211.006
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