Fits a crescent model to visibility amplitude data using the deo tempering sampler with the stan exploration kernel. This is a validation and test for it. More...

#include "data_visibility_amplitude.h"
#include "model_image_crescent.h"
#include "model_ensemble_averaged_scattered_image.h"
#include "likelihood.h"
#include "sampler_stan_adapt_diag_e_nuts_MCMC.h"
#include "sampler_deo_tempering_MCMC.h"
#include "utils.h"

Include dependency graph for crescent_image_comparison_themistan.cpp:

Functions
int	main (int argc, char *argv[])

Detailed Description

Author: Jorge A. Preciado, Paul Tiede

Date: April, 2020

Compares a geometric crescent model to the visibility amplitude data taken in 2007 and 2009, permitting a day-specific intensity renormalization. The primary fit result is a measure of the size ( \( R \)), the relative thickness ( \( \psi \)), and the degree of symmetry ( \( \tau \)) of the emission region and can be compared to the fit results reported in Kamruddin and Dexter 2013.

The resulting parameter distribution is:

Triangle plot for the marginalized posterior probabilty distribution showing the likely parameter values and associated confidence contours.

Note that the intensity normalization is solved for analytically in the likelihood_marginalized_visibility_amplitude, and thus the intrinsic normalization is fixed near unity by design.

Author: Jorge A. Preciado, Paul Tiede

Date: April, 2020

Compares a geometric crescent model to the visibility amplitude data taken in 2007 and 2009, permitting a day-specific intensity renormalization. The primary fit result is a measure of the size ( \( R \)), the relative thickness ( \( \psi \)), and the degree of symmetry ( \( \tau \)) of the emission region and can be compared to the fit results reported in Kamruddin and Dexter 2013.

The resulting parameter distribution is:

Triangle plot for the marginalized posterior probabilty distribution showing the likely parameter values and associated confidence contours.

Note that the intensity normalization is solved for analytically in the likelihood_marginalized_visibility_amplitude, and thus the intrinsic normalization is fixed near unity by design.

The parameter values associated with the individual chains are:

Trace plot showing the fluctuations in the parameters for each MCMC chain as a function of MCMC step.

The associated likelihoods and \(\chi^2\) of the chains are:

Log-likelihoods of the individual chains as a function of MCMC step.

Chi-squared of the individual chains output periodically throughout the MCMC run.

See also: See reading_data.cpp example for details on reading in EHT data.

Functions

Detailed Description