Modeling#
Analysis#
The Analysis
objects define the log_likelihood_function
of how a galaxy model is fitted to a dataset.
It acts as an interface between the data, model and the non-linear search.
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Fits a CTI model to a charge injection imaging dataset via a non-linear search. |
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Fits a CTI model to a 1D CTI dataset via a non-linear search. |
Settings#
Input into an Analysis
class to customize the behaviour of a CTI model-fit performed via a non-linear search.
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Controls the modeling settings of CTI clocking in 1D. |
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Controls the modeling settings of CTI clocking in 2D. |
Non-linear Searches#
A non-linear search is an algorithm which fits a model to data.
PyAutoCTI currently supports three types of non-linear search algorithms: nested samplers, Markov Chain Monte Carlo (MCMC) and optimizers.
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A Nautilus non-linear search. |
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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.
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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 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. |