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Introduction

SLIME is an interactive model-fitting program for AIPS. It allows you to create a model brightness distribution from a number of individual components each of which can be described by a simple mathematical function with a small number of parameters. Once you have created a model brightness distribution, you can use SLIME to automatically adjust the model parameters to obtain the best fit to the data.

Unlike other AIPS tasks, SLIME makes full use of the X Window System environment. The graphical user interface allows you to adjust model parameters by dragging elements in a graphical representation of the model and to see how your changes affect the model visibilities as you make them. These features are intended to help you construct a model that is in fairly good agreement with the data. This is important because most model-fitting procedures will only give good results if they are given a starting model that is a fair approximation of the model which best fits the data.

You can consider SLIME as a set of tools that you can use to assist you in constructing a model that fits the data. Each tool is used for a specific task but communicates with the others so that all of the tools present a consistent view of the model and the data to which it is to be fitted.

The principle tool is the model editor. This tool provides a graphical representation of the model. You can change the parameters of the model by dragging the graphical representations of the model components with the mouse or by typing parameter values into forms. The model editor also allows you to create new components or delete existing ones as well as allowing you to save the model to an AIPS clean-component table or to load the contents of an existing clean-component table as a new model. It is also the central point of control for the program: you start new tools or shut the program down from the model editor.

Baseline tools display measured visibilities together with ideal visibilities calculated from the model for designated baselines. The model visibilities are updated whenever you make a change to the model so that you can monitor whether your changes improve the fit to the data or not. You may create as many baseline tools as you want.

The least-squares fitting tool will take your model and adjust it to minimize the tex2html_wrap_inline1943 statistic. The model editor display and the model visibilities in the baseline tools will be updated after every iteration of the fitting process so that you can see what is happening to the model and interrupt the procedure if it appears to be converging on an implausible set of model parameters.

Future releases of SLIME will include other tools for displaying closure phase and, perhaps, for using stochastic algorithms such as simulated annealing for fitting model parameters to the data. Stochastic algorithms do not generally require good starting models but are much slower than non-linear function-minimization techniques such as that used in the least-squares fitting tool. The increased time-cost of a stochastic fitting algorithm might be worth-while if you are finding it difficult to construct a good starting model.

The use of the existing tools will be illustrated in the next chapter which will walk you through a typical SLIME session.


next up previous contents
Next: A Guided Tour of Up: Users Guide Previous: Users Guide

Chris Flatters
Thu Mar 14 16:02:50 MST 1996