Lens Modeling#
Analysis#
The Analysis objects define the log_likelihood_function of how a lens model is fitted to a dataset.
It acts as an interface between the data, model and the non-linear search.
Analysis classes are used by PyAutoFit to fit a model to a dataset via a non-linear search. |
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Analysis classes are used by PyAutoFit to fit a model to a dataset via a non-linear search. |
Non-linear Searches#
A non-linear search is an algorithm which fits a model to data.
PyAutoLens currently supports three types of non-linear search algorithms: nested samplers, Markov Chain Monte Carlo (MCMC) and optimizers.
A Dynesty NonLinearSearch using a static number of live points. |
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A Dynesty non-linear search, using a dynamically changing number of live points. |
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An Emcee non-linear search. |
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A PySwarms Particle Swarm Optimizer global non-linear search. |
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A PySwarms Particle Swarm Optimizer global non-linear search. |
Priors#
The priors of parameters of every component of a mdoel, which is fitted to data, are customized using Prior objects.
A prior with a uniform distribution, defined between a lower limit and upper limit. |
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A prior with a uniform distribution, defined between a lower limit and upper limit. |
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A prior with a log base 10 uniform distribution, defined between a lower limit and upper limit. |
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A prior with a log base 10 uniform distribution, defined between a lower limit and upper limit. |
Hyper#
The hyper setup of a pipeline, which controls how hyper-features in PyAutoLens template pipelines run, for example controlling whether hyper galaxies are used to scale the noise and the non-linear searches used in these searchs. |