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We present a new technique for obtaining model fittings to VLBI images ofastrophysical jets. The method minimizes a performance function proportional tothe sum of the squared difference between the model and observed images. Themodel image is constructed by summing elliptical Gaussian sources characterizedby six parameters: two-dimensional peak position, peak intensity, eccentricity,amplitude and orientation angle of the major axis. We present results for thefitting of two main benchmark jets: the first, constructed from threeindividual Gaussian sources, the second formed by five Gaussian sources. Bothjets were analyzed by our cross-entropy technique in finite and infinitesignal-to-noise regimes, the background noise chosen to mimic that found ininterferometric radio maps. We show that our technique is capable of recoveringthe parameters of the sources with a similar accuracy to that obtained from thetraditional AIPS task IMFIT when the image is relatively simple (e.g., fewcomponents). For more complex maps, our method displays superior performance inrecovering the parameters of the jet components. Our methodology is also ableto show quantitatively the number of individual components present in an image.An additional application of the cross-entropy technique to a real image of aBL Lac object is shown and discussed. Our results indicate that ourcross-entropy technique must be used in situations involving the analysis ofcomplex emission regions having more than three sources, even though it issubstantially slower than current model fitting tasks (at least 10,000 timesslower for a single processor, depending on the number of sources to beoptimized). As in the case of any model fitting performed in the image plane,caution is required in analyzing images constructed from a poorly sampled (u,v)plane.

Modeling very long baseline interferometric images with the cross-entropy global optimization technique